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Primer: The deconstruction of neuronal spike trains
Department of Physics, University of California, San Diego, CA
Department of Neurobiology, University of Chicago, IL
Department of Applied Mathematics, University of Washington, Seattle, WA
Department of Physiology and Biophysics, University of Washington, Seattle, WA
Section of Neurobiology, University of California, San Diego, CA
As information flows through the brain, neuronal firing progresses from encoding the world as
sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable
goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor
streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this
task. We focus on classes of models in which the external variable is compared with one or more
feature vectors to extract a low-dimensional representation, the history of spiking is potentially
incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We
illustrate these techniques in application to datasets of different degrees of complexity. In particular,
we address the fitting of models in the presence of strong correlations in the sensory stream, as
occurs in natural external stimuli and with sensation generated by self-motion.
Nonparametric models
Spike Triggered Average (STA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Calculating the feature for retinal ganglion cells. . . . . . . . . . . . . . . . . . . . 11
Interpreting the feature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Spike-Triggered Covariance (STC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Calculating the features for data from retina . . . . . . . . . . . . . . . . . . . . . 16
Interpreting the features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Relation to Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . . 18
Natural stimuli and correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Spectral whitening for correlated stimuli . . . . . . . . . . . . . . . . . . . . . . . 19
Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Features from thalamic spiking during whisking in rat. . . . . . . . . . . . . . . . 20
Maximally informative dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Models with constrained nonlinearities
The linear/nonlinear modeling approach . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maximum Noise Entropy (MNE) method . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Interpreting the model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Features from thalamic spiking during whisking in rat. . . . . . . . . . . . . . . . 26
Separability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Separability of a feature vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Separability of the nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Generalized Linear Models (GLM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Overfitting and regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Choice of basis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Features from retina and thalamic cells. . . . . . . . . . . . . . . . . . . . . . . . . 32
Model evaluation
Log-likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Spectral coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Validation of models with white noise stimuli . . . . . . . . . . . . . . . . . . . . . . . . . 34
Validation of models with correlated noise from self-motion . . . . . . . . . . . . . . . . 36
Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Network GLMs
Application to cortical data during a monkey reach task . . . . . . . . . . . . . . . . . . . 40
Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Further network GLM methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Model assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Caveats on whitening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Adaptation and dependence on stimulus statistics . . . . . . . . . . . . . . . . . . . . . . 46
Population dimensionality reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Non-spiking data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Advances in experimental design, measurement techniques, and computational analysis allow us unprecedented access to the dynamics of neural activity in brain areas that transform sensory input into
behavior. One can address, for example, the representation of external stimuli by neurons in sensory pathways, the integration of information across modalities and across time, the transformations
that occur during decision-making, and the representation of dynamic motor commands. While new
methods are emerging with the potential to elucidate complex internal representations and transformations (Cunningham and Yu, 2014), here we will focus on established techniques within the rubric
of neuroinformatics that summarize the relationship between sensory input or motor output and the
spiking of neurons. These techniques have provided insight into neural function in a relatively large
number of experimental paradigms. We discuss these methods in detail, illustrate their application
to experimental data, and contrast and discuss the interpretation, reliability, and utility of the results
obtained with different methods.
The methods that we will consider aim to establish input/output relationships that capture how spiking activity, generally at the single neuron level, is related to external variables: either sensory signals
or motor output. These models focus on describing the statistical nature of this relationship without
any direct attempt to establish mechanisms. The neural computation is parsed into several components (Fig. 1A). The first includes linear feature vectors that extract a low-dimensional description
of the stimulus that drives firing. In some models, terms that capture dependence on the history of
firing as well as the history of firing by other neurons in the network are incorporated. Finally, a
nonlinear function describe the probability of firing as a function of these variables. The form of these
components can reveal properties of the system that test theoretical concepts, such as information
maximization. For example, changes in the feature under different stimulus conditions can reveal the
system’s ability to adapt to or cancel out correlations in the input (Hosoya et al., 2005; Sharpee et al.,
2006), while changes in the nonlinearity reveal how the system can adapt its dynamic range as the
intensity or variability of the stimulus changes (Fairhall et al., 2001; Wark et al., 2007; Dáaz-Quesada
and Maravall, 2008).
While representing neuronal spiking through a predictive statistical model is only a limited aspect
of neural computation, it is a fundamental first step in establishing function and guiding predictions
as to the structure of neural circuitry. The key to any predictive model of a complex input/output
relationship is dimensionality reduction, i.e., a simplification of the number of relevant variables that
are needed to describe the stimulus. Here our primary goal is to present current methods for fitting
descriptive models for single neurons and to directly compare and contrast them using different kinds
of data. With the growing importance of multi-neuronal recording, it will also be necessary to seek
lower-dimensional representations of network activity. While we will largely focus on methods to
reduce the representation of external variables in order to predict firing, we will further point toward
methods that yield a reduced description of oth external and neural variables in models of network
We have chosen three datasets for analysis here as illustrative examples. The first set consists of
multi-electrode array recordings from salamander retinal ganglion cells that have been presented
with a long spatiotemporal white noise stimulus. This preparation has been a paradigmatic one
Figure 1: Schematic for the generation of spike trains from stimuli from different classes of models. (A) A LN
model consists of a number of processing steps that transform the input stimulus into a predicted rate spike. Here
we illustrate the types of processing stages included in the computational methods we consider. Most generally,
the stimulus is projected onto one or more features and may then be passed through a nonlinear function. The
result of this may be summed with a term that depends on the spike history and passed through a further
nonlinear function. Finally there is a stochastic spike generation mechanism that yields a spike train. Note
that none of the methods we considered have all model components; one should choose among the methods
depending on the nature of the stimulus, the type of response and on the need for parsimony. For example, only
the generalized linear model includes the influence of the spike history. (B) Here we illustrate how an example
visual stimulus is reformatted as a time-dependent vector. Each stimulus frame has two spatial coordinates and
a total of nX pixels. First, the frame presented in each time point is unwrapped to give a NX dimensional vector.
Then, if the model we will construct depends on the stimulus at NT time points, the final stimulus is a vector in
which the spatial component is copied across consecutive time points to form N = NX × NT components.
in which many iterations of predictive modeling have been first successfully applied (Chichilnisky,
2001; Touryan et al., 2002; Rust et al., 2005; Pillow et al., 2008). The second and third set involve
more challenging cases: the relationship between single unit recordings of thalamic neurons of alert,
freely whisking rats and the recorded vibrissa self-generated motion (Moore et al., 2015a); and the
relationship between unit recordings from motor cortex of monkeys and the recorded position and
grip strength of the hand as monkeys use a joystick to manipulate a robotic arm (Engelhard et al.,
2013). The data from behaving animals is more representative of typical and future experiments and
allows us to discuss several important issues, including smaller data size and the highly correlated
and non-repeated external variables that are generated by natural stimulus statistics and self-motion.
With each statistical model considered, it is important to ensure that one is capturing the trend of
interest, and not simply structure that is specific to the training set. Thus one must always test the
performance of a model with a portion of the data that was not used to build the model; typically
80% of the data is used to build the model and 20% for validation. By permuting the data among
the fractions used to fit and to validate, one can build up a jack-knife estimate of the variance for the
reliability of the fit.
We provide all of the code and spiking and stimulus data required to reproduce our results. Simple modification of this code will enable readers to extend the analysis methods we present to new
The linear/nonlinear modeling approach
We will focus on models broadly known as linear/nonlinear (LN) models. These have been successful
in providing a phenomenological description for many neuronal input/output transformations and
are constructed by correlating spikes with the external variable. Some models are nonparametric in
the sense that both feature vectors and the nonlinear input/output response of the neuron are derived
from the data. Other models are parametric, in that the mathematical form of the nonlinearity is fixed.
While the external variable, as emphasized in the Introduction, could be either a sensory drive or a
motor output, we will use the term stimulus for convenience from now on. Note, however, that while
for sensory drive one considers only the stimulus history, in motor coding applications one would
also consider motor outputs that extend into the future.
We express the neuron’s response r(t) at time t as a function of the recent stimulus s(t0 ) (with t0 < t)
and, also, potentially its own previous spiking activity:
r(t) = f (r (t0 < t) , s (t0 < t)) .
The stimulus vector s(t0 < t) might, for example, represent the intensity of a full-field flicker or the
pixels of a movie, the spectro-temporal power of a sound, the position of an animal’s whiskers, and
so on. The choice of this initial stimulus representation is an important step on its own and could in
principle involve a nonlinear transformation, e.g., the phase in a whisk cycle, a case we will discuss
later. The function f (·) generally represents a nonlinear dependence of the response on the stimulus,
i.e., the response r(t) is equivalent to the conditional probability of spiking and given by
r(t) = p(spike(t)|r (t0 < t) , s (t0 < t)).
In this form, the response r(t) is generally taken to be the expected firing rate of a random process,
which is assumed here to be Poisson. We will denote the spike counts observed on a single trial as
If the neuron’s response does not depend on its own history but only on the stimulus, the function
f (·) can be expanded as a Volterra series (Marmarelis and Marmarelis, 1978; Chichilnisky, 2001), i.e.,
= f (s (t0 < t))
= f1 (t < t) ∗ s(t < t) + f2 (t , t < t) ∗ s(t )s(t ) + . . . ,
where the functions f1 (·), f2 (·), etc. are kernels, analogous to the coefficients of a Taylor series, that
are convolved with increasing powers of the stimulus. The Volterra series approach has been applied
to a few examples in neuroscience, such as complex cells in primary vision (Szulborski and Palmer,
1990), limb position in walking in insects (Vidal-Gadea and Belanger, 2009), and single-neuron firing
(Powers and Binder, 1996). To successfully implement this method, the amount of data needed to fit
the kernels increases exponentially with the order of the expansion. Furthermore, capturing realistic
nonlinearities including, e.g., saturation, typically requires expansions to more than first or second
The LN model is a powerful alternative approach that allows one to approximate the input/output
relation (Eq. 1) using a plausible amount of experimental data. This differs from the Volterra series in
that no attempt is made to approximate the nonlinearity in successive orders. Rather, the nonlinearity
is an explicit component of the model and is arbitrary in some formulations and constrained to have
a specific functional form in others. The stimulus s(t0 < t) is in general high dimensional. It may
consist of a sequence of successive instantaneous snapshots, e.g., frames of a movie, each with NX
spatial pixels or an auditory waveform with NX frequency bands. With each "frame" discretized in
time at sampling rate ∆t (Fig. 1), there is some timescale T = ∆tNT beyond which the influence of
the stimulus on future spiking can be assumed to go to zero, defining the number of relevant frames
as NT . Then the total number of components defining the stimulus, or dimensionality of the stimulus
space, denoted N , is given by
N = NX × NT .
The key strategy of the LN approach is two-fold. First, to find a simplified description of this
high- dimensional stimulus that captures its relevance to neuronal firing in terms of one to a few
N-dimensional feature vectors that span the stimulus space. Second, to fit the response as a nonlinear function of those few components. As a matter of implementation, the relation f (·) in Eq. (1) is
divided into two parts. First, the full stimulus is processed by a set of linear filters defined by feature vectors. These filters take the N -dimensional stimulus and extract from it certain components,
i.e., linear combinations or dimensions of the stimulus, analogous to the Volterra approach, and possibly also spike history. Second, there is a nonlinear stage, which we will denote as g(·), that acts
upon those components to predict the associated firing rate. The LN family of models makes two
important assumptions about the system’s input/output transformation. One is that the number of
stimulus components or dimensions that are relevant to the neuron’s response, K, is much less than
the maximum stimulus dimensionality N . All methods then necessarily include a dimensionality
reduction step, whose goal is to find these relevant K vectors, which we will call features and denote
by φi , each of size N with i = 1, . . . , K, in terms of which the input/output transformation can be
r(t) = g (z1 , z2 , . . . , zK , r(t0 < t))
zi = φi · s(t0 < t)
is the projection of the stimulus on the ith feature. The features φi span a low-dimensional subspace
within the full stimulus space, and the response of the system is approximated to depend only on
variations of the stimulus within that subspace. The second assumption is that the nonlinear transformation, g(·) above, is taken to be stationary in time, i.e., g(·) has time dependence only through
the stimulus and the history of the neuron’s spiking response that, like the stimulus, may be highdimensional.
The non-linearity g(·) can be determined nonparametrically using the probabilistic interpretation of
Eq. (1) given in Eq. (2). Considering for now only dependence on the stimulus, we can use Bayes’
rule, i.e.,
p (spike|s(t)) =
p (s(t)|spike) p(spike)
p (s(t))
to determine an input/output relation in terms of the reduced variables defined above (Eq. 6):
g(z1 , z2 , . . . , zK )
= p(spike|z1 , z2 , . . . , zK )
p(z1 , z2 , . . . , zK |spike) p(spike)
p(z1 , z2 , . . . , zK )
The probability distributions on the right hand side can be found from the data, i.e.,
• p (z1 , z2 , . . . , zK ), the prior, is the probability distribution of all stimuli in the experiment, projected on K stimulus features;
• p (z1 , z2 , . . . , zK |spike), the spike-conditional distribution, is the probability distribution of the stimuli, projected onto the K features, conditioned on the occurrence of one or more spikes;
• p(spike), the mean firing rate over the entire stimulus presentation.
The prior distribution, p(z1 , z2 , . . . , zK ), and the spike-conditional distribution, p(z1 , z2 , . . . , zK |spike),
are estimated by binning the K-dimensional stimulus space along each one of its directions. If we
discretize each of the K stimulus components into NB bins, the total number of bins is (NB )K , which
grows exponentially with K. An accurate estimate of g requires some minimal number of samples in
each bin, so the appropriate number of bins into which to divide the stimulus space will be determined
by the duration of the experiment and the typical spike rate. With multiple dimensions, and when
incorporating the history of activity, this direct method becomes prohibitive.
Nonparametric models
We will begin with methods that apply to situations in which systems are well driven by stimuli that
approximate white noise. White means that the value of the stimulus at one point or time is unrelated
to its value at any other point or time, that is, there are no correlations in the input. This means that all
frequencies are represented in a spectral analysis of the stimulus up to the Nyquist frequency, which
is simply 1/2∆t; the choice of ∆t may be guided by the 5 to 100 ms time-scale of neuronal responses.
An example of such a stimulus is a visual checkerboard stimulus with a total of NX pixels whose
luminance values are each chosen randomly from a Bernoulli distribution, i.e., a binary distribution
with two choices, relative to an average intensity (Fig. 1B). The input that drives the cell may be
viewed as a matrix of pixels in space and time, denoted I(x, t).
NX spatial positions
To define a stimulus sample at time t, we select nT frames of the input to form a matrix, i.e.,
 x
  I(1, t − NT )
I(1, t − 1) 
 
 
 
 
 
NT time points
where (· · · ) labels the component. In general, we wish to consider each stimulus sample as a vector
in a high-dimensional space; thus one reorganizes each stimulus sample from this matrix format to
an N = NX × NT (Eq. 4) vector that indexes the NT frames that go back in time by NT ∆t (Fig. 1B):
I(1, t − NT )
 I(NX , t − NT )
s(t) = 
 I(1, t − 1)
I(NX , t − 1)
Spike Triggered Average (STA)
The goal of the dimensionality reduction step is to identify a small number of stimulus features that
most strongly modulate the neuron’s probability to fire. Dimensionality reduction can be understood
geometrically by considering each presented stimulus s(t) as a point in the N -dimensional space.
Each location in this space is associated with a spiking probability, or firing rate r(s), that is given by
the nonlinearity evaluated at that location. A given experiment will sample a cloud of points in this
N -dimensional space, with a geometry which is set by the stimulus design (all dots in Fig. 2A). The
spike-triggering stimuli are a smaller cloud, or subset, of these stimuli (red dots in Fig. 2A). Dimensionality reduction seeks to find the stimulus subspace which captures the interesting geometrical
structure of this spike-triggering ensemble.
The simplest assumption is that a cell’s response is modulated by a single linear combination of the
stimulus parameters, i.e., K is one. The single most effective dimension is in general the centroid of
the points in this high-dimensional stimulus space that are associated with a spike. This is the spiketriggered average, denoted φsta , the feature obtained by averaging together the stimuli that precede
spikes (De Boer and Kuyper, 1968; Podvigin et al., 1974; Eckhorn and Pöpel, 1981; Chichilnisky, 2001),
φsta =
1 X
n(t) (s(t) − s̄) ,
nT t
Figure 2: Schematic of stimulus samples plotted in two arbitrary directions in stimulus space as gray and
red dots; only red dots lead to a spike. (A) The STA is a vector that points to the mean of the spike-triggered
stimuli (red dots). (B) The covariance of the spike-triggered stimuli captures the coordinates of variation of the
cloud. The covariance of the stimulus, i.e., the prior covariance Cp , forms one set of vectors and the covariance
of the spike-triggered stimuli, Cs , forms a second set. The two dominant vectors comprising their difference, i.e.,
∆C = Cs - Cp , yield the dominant STC two modes.
where s̄ is the average stimulus, i.e.,
s̄ =
1 X
nT t
n(t) is the number of spikes at time t, and nT is the total number of spikes. As for the case of the
stimuli (Fig. 1B), the STA is organized as a vector of length N that indexes the NT frames back in time
from t = ∆t to t = NT ∆t (Eq. 4), i.e.,
φsta [1]
φsta [NX ]
φsta [2NX ]
φsta [NT × NX ]
For a Gaussian stimulus, the prior distribution of stimulus values projected onto the STA, p (φsta · s(t)),
is also Gaussian. Often in experiments, however, the stimulus is binary, so that the stimulus in each
pixel or time takes one of two values. If the stimulus has a large number of components, the central
limit theorem ensures that these projections, as the weighted sum over many random values, will
also have a Gaussian distribution whose form can either be computed analytically from the statistics
used to construct the stimuli or accurately fit from data.
The nonlinearity can be estimated directly using Bayes’ rule, as in Eqs. (7, 8), i.e.,
= p (spike|φsta · s(t))
p (φsta · s(t)|spike) p(spike)
p (φsta · s(t))
The conditional histogram defining p(φsta · s(t)|spike) is generally not Gaussian and is often undersampled in the tails of the distributions. Thus when computing this ratio of histograms, it can be
helpful to fit the nonlinearity using a parametric model. If no functional form is assumed, one can
simply apply a smoothing spline to the conditional distribution. Further, it may be necessary to
reduce the a priori stimulus dimension by concatenating pixels or downsampling in time if the spiking
data is too sparse leading to under-sampled probability distributions.
Calculating the feature for retinal ganglion cells.
We consider the case of a binary checkerboard stimulus used to drive spiking in retinal ganglion
cells (Fig. 3), the third-order sensory neurons that output visual information from the retina. In this
experiment, the pixel values were chosen from a binary distribution (Fig. 3A). We applied the above
formalism to the stimulus set reorganized as three consecutive frames for a stimulus dimension of
N = 142 × 3 = 588. We varied the number of bins used to discretize the stimulus to get reasonably
smooth features. The STA feature was computed according to Eqs. (12, 11) for each of 53 retinal
ganglion units; an example is shown in Fig. 4. One should aim to choose the dimensionality of the
stimulus, i.e., the product of NT and NX , such that the stimulus-induced variation in spiking is well
captured and returns to zero at the temporal and spatial boundaries of the STA. Further, the features
of the STA should be well resolved but also well averaged given the data size.
Here we tried both NT = 3 time points with a larger patch size of 14×14 pixels (Fig. 4A,B), and NT = 6
frames with a smaller patch of 10 × 10 pixels (Fig. 4C,D). The key feature is a central spot of excitation
that rises and falls over three frames (Fig. 4A,C) and is thus best captured in the configuration with
smaller spatial extent and 6 temporal points (Fig. 4C). Thus the STA provides a readily computed
one-dimensional description of the cell; in this case the feature is a transient spot of light. We return
to this point when we extend the description through a covariance analysis.
For this dataset, the large number of frames and spikes permits the prior stimulus distribution p (φsta · s(t))
and the conditional stimulus distribution p(φsta · s(t)|spike) to be well sampled. The prior is consistent with a Gaussian, as can be expected for a projection on any direction for a white noise stimulus
(Fig. 4B,D). The application of Bayes’ rule (Eq. 14) yields a monotonic nonlinearity. The nonlinearity, which is proportional to the ratio of these two distributions, was smoothed by estimating it on a
downsampled set of bins (Fig. 4B,D).
Interpreting the feature.
The STA procedure (Eqs. 11 and 12) has a strong theoretical basis. It has been shown (Chichilnisky,
2001; Paninski, 2003) that φsta is an unbiased estimator of the feature if the spike-triggering stimuli
have a non-zero mean when projected onto any vector, i.e., the cloud of spike-triggering stimuli is
offset from the origin, and if the distribution of spike-triggering stimuli has finite variance. In the
limit of infinite data, the STA feature is guaranteed to correctly recover the dependence of the neuron’s response on this single feature. Geometrically, the vector φsta points from the origin exactly to
the center of the cloud for a sufficiently large dataset. This is independent of the nonlinearity of the
cell’s response. However, this theorem does not guarantee that if the cell’s response depends only
on the projection of the stimulus onto one vector, that vector must be φsta . For example, the spiketriggering cloud of stimuli points might be symmetric, such that the average lies at the origin, but
Figure 3: Spike responses from salamander retinal ganglion cell 3 for a visual checkerboard stimulus, used
to illustrate the methods with a "white noise" stimulus. (A) Each pixel in the checkerboard was refreshed each
∆t = 33.33 ms with a random value and the spikes recorded within the same interval. (B) We constructed the
covariance matrix of the stimulus (Eq. 16) and plotted its spectrum (black).The eigenvalues are all close to the
variance of a single pixel, σ 2 = 1, for the checkerboard stimulus. We compared this spectrum to the theoretical prediction given by the Marchenko-Pastur distribution with geometrical parameter γ = nT /N (number of
samples divided by number of dimensions). (C) The hallmark of white noise is that there is no structure in
the stimulus, and indeed the largest eigenvectors of the stimulus covariance matrix (Eq. 16) contain no spatial
or temporal structure. Methods: The dataset consists of 50 time series of spike arrival times simultaneously
recorded from 53 retinal ganglion cells of retinae that had been isolated from larval tiger salamander (Ambystoma
tigrinum) and laid upon a square array of planar electrodes (Segev et al., 2004). The pitch of the array was 30
µm and the spiking output of each cell, which includes spikes in both the soma and the axon, was observed
on several electrodes. Using a template distributed across multiple electrodes enables one to accurately identify
spikes as arising from a single retinal ganglion cell. Visual stimuli were a 142 = 196 square pixel array that was
displayed on a cathode ray tube monitor at a frame rate of 30 Hz (Segev et al., 2006). Each pixel was randomly
selected to be bright or dark relative to a mean value on each successive frame, i.e., the amplitude of each pixel
was distributed bimodally, and was spectrally white up to the Nyquist frequency of 15 Hz. The image from
the monitor was conjugate with the plane of the retina and the magnification was such that visual space was
divided into 50 µm squares on the retina, which allowed many squares to fit inside the receptive field center of
each ganglion cell with a cut-off of 200 cm−1 in spatial frequency. Each time series was 60 to 120 minutes long
and contained between 1,000 and 10,000 spikes, but samples fewer than 212 of the 2288 potential patterns. For
some of the analyses, we extracted the 102 = 100 pixel central region for 2100 potential patterns.
Figure 4: The spike triggered average, φsta , for the responses of retinal ganglion cell 3. We considered two
stimulus representations. (A,B) A short sequence where we retain three stimulus frames in the past (NT = 3)
and the frame was NX = 14 × 14 = 196 pixels. (C,D) A long sequence where NT = 6 but the frame was
cropped such that NX = 10 × 10 = 100. In the case of the short sequence we chose the optimal lag for which
the cell’s response is maximally modulated by the stimulus, where for the long sequence we chose the first six
frames into the past. We computed the STA for both representations, panels A and C, respectively. We then
computed the prior distribution (black), the distribution of projections of all stimuli on the STA feature, and the
spike-conditional distribution (red), the distribution of projections of stimuli associated with a spike on the STA
feature. Clearly the spike-conditional distribution is shifted compared to the prior. Finally, we use Bayes’ rule
(Eq. 14) to obtain the input/output nonlinearity (blue), which is proportional to the probability of a spike given
the value of the stimulus projected on the STA feature, i.e., p (spike|φsta · s(t))
the shape is nonetheless very different from the cloud consisting of all stimuli, i.e., the prior distribution p (φsta · s(t)). The spike-triggered covariance, discussed next, is designed to make use of this
additional information.
Spike-Triggered Covariance (STC)
While generally the spike-triggered average is the best solution to reduce the stimulus to a single
dimension, the probability of a spike may be modulated along more than one direction in a stimulus
space, as has been shown for many types of neurons across different sensory systems (Brenner et al.,
2000; Fairhall et al., 2006; Slee et al., 2005; Fox et al., 2010; Maravall et al., 2007). Further, there may be
a symmetry in the response, such as sensitivity to both ON or OFF visual inputs for a retinal ganglion
cell, or invariance to phase in the whisk cycle for a vibrissa cortical cell, that causes the φsta to be
close to zero. Thus, our next step is to generalize the notion of feature to a K-dimensional model of
the form:
p(spike|s(t)) = p (zi , z2 , . . . , zK ) ,
where, as a reminder, zi = φi ·s(t) is the projection of the stimulus at time t on the ith identified feature
vector φi . To find these K relevant dimensions, we will make use of the second order statistics of the
spike-triggering stimuli.
Let us first consider the the second-order statistics of the stimulus itself, which are captured by its
covariance matrix, also referred to as the prior covariance:
Cp =
1 X
(s(t) − s̄) (s(t) − s̄)
n−1 t
where > means transpose and we assume averaging over m stimulus samples indexed by t. The
covariance matrix can be diagonalized into its eigenvalues, denoted λi , and corresponding eigenvectors, denoted vi , as in principal component analysis (PCA), i.e.,
Cp =
λi vi vi> ,
where the eigenvectors of Cp are space-time patterns in the present case. The eigenvectors define a
new basis set to represent directions in stimulus space that are ordered according to the variance of
the stimulus in that direction, which is given by the corresponding eigenvalue.
For a Gaussian white noise stimulus, all eigenvalues of the covariance of the prior are equal and Cp
is a diagonal matrix with Cp = σ 2 I where I is the identity matrix. The constant σ 2 is the variance
of the distribution of pixel amplitudes. In practice, the use of a finite amount of data to compute
the prior covariance (Eq. 17) effects the spectrum slightly but in a predictable way; the spectrum of
eigenvalues of the stimulus covariance matrix is close to flat (black dots in Fig. 3B) in agreement with
the analytical spectrum calculated for the same stimulus dimension and same number of samples
(red dots in Fig. 3B). Although we could have computed the prior without finite size limitations,
it is instructive to see the effect. The dominant eigenvectors, shown in a space-time format, appear
featureless as they should (Fig. 3C).
Our goal is to find the directions in stimulus space in which the variances of the spike-triggering stimuli differ relative to the prior distribution of stimuli. These can be found through the covariance
difference matrix (Van Steveninck and Bialek, 1988; Agüera y Arcas and Fairhall, 2003; Bialek and
van Steveninck, 2005; Aljadeff et al., 2013), denoted ∆C, where:
∆C = Cs − Cp ,
and the spike-triggered covariance matrix Cs , is computed relative to the spike triggered average
(Eq. 11) and given by
Cs =
n(t) (s(t) − φsta ) (s(t) − φsta ) .
nT − 1 t
The prior covariance matrix Cp is given by Eq. (16) and we recall that nT is the total number of spikes.
The matrix ∆C (Eq. 18) may be expanded in terms of its eigenvalues, λi , and eigenvectors, φstc,i , i.e.,
∆C =
λi φstc,i φ>
stc,i .
As ∆C is a symmetric matrix, the eigenvalues are real numbers and the corresponding eigenvectors
form a orthogonal normalized basis of the N -dimensional stimulus space, such that φstc,i · φstc,j = 0
for i 6= j and φstc,i · φstc,i = 1. Positive eigenvalues correspond to directions in the stimulus space
along which the variance of the spike-triggered distribution is larger than the prior distribution, and
negative eigenvalues correspond to smaller variance. This analysis is illustrated in two dimension
in Figure 2B. The dominant STC vectors, STC modes 1 and 2 respectively, are found by subtracting
the eigenvectors of the prior covariance matrix (gray area Fig. 2B) from those of the spike-triggered
covariance matrix (blue area in Fig. 2B).
Some eigenvalues will emerge from the background simply because of noise from finite sampling.
To determine which K of the N eigenvectors of ∆C are significant for the cell’s input/output transformations, the eigenvalues λi are compared to a null distribution of eigenvalues obtained randomly
from the same stimulus. We compute, for a large number of repetitions, a spike-triggered covariance matrix using randomly chosen spike times, tr , to select the same number of stimulus samples at
random, i.e.,
Cr =
n(tr ) (s(tr ) − φsta ) (s(tr ) − φsta ) .
nT − 1 t
The corresponding matrix of covariance differences ∆Cr = Cr − Cp and its eigenvalues are computed for each random choice. The eigenvalues of all matrices ∆Cr form a so-called null distribution.
Eigenvalues of ∆C (Eq. 18) computed from the real spike train that lie outside the desired confidence
interval of the null distribution are said to be significant. Note that one might wish to preserve any
structure resulting from temporal correlations in the spike train, e.g., a tendency to spike in bursts. If
such structure exists, one can compute the matrix Cr (Eq. 21) using spike trains shifted by a random
time lag with periodic boundary conditions such that the end of the spike train is wrapped around to
the beginning.
The STC features, φstc,i , are the corresponding significant eigenvectors of the covariance difference
matrix. If there is a nonzero STA, φsta will tend to be the most informative direction in stimulus
space. Thus a higher dimensional model of the stimuli that lead to spiking includes the STA and the
significant STC features. Examination of these features will give insight into the underlying feature
selectivity of the neurons. However, for the purpose of predicting spikes, we must work in a basis
where all features are orthogonal. As the STC feature vectors are not generally orthogonal to the STA,
one should project out the STA from each eigenvector used, recalling that the STC features remain
orthogonal to one another. The new features are denoted as φ⊥
stc,i where:
stc,i = φstc,i −
φstc,i · φsta
φsta , i = 1, . . . K − 1.
||φsta ||2
It is convenient to normalize these feature vectors such that the norm of each of them is equal to 1:
stc,i · φstc,i = 1.
For the case of white noise, where the variance of the stimulus is equal along every direction, the
eigenvalues of the prior covariance matrix, Cp , are essentially all equal and the STC features can be
computed directly from Cs . However, if the variance along some directions of the stimulus is larger
than others, as for the case for correlated noise, the significance threshold for each eigenvalue of Cs is
different. In this case, subtracting the prior covariance allows one to test whether the variance of the
spike triggered distribution is different from that of the prior along each direction.
The STA and the set of orthogonalized STC vectors are then used to compute a multidimensional
nonlinear function by computing the joint histogram of the K values of the spike-triggering stimuli
projected onto the feature vectors and applying Bayes’ rule (Eq. 7). The function p(spike|s(t)) acts as
a multidimensional look-up table to determine the spike rate of the cell in terms of the overlap for the
stimulus with each of the feature vectors.
Calculating the features for data from retina
The STC features were computed according to Eq. (18) for a set of retinal ganglion units; results for
the same representative unit used for the STA features (Figs. 4 and 5A) are shown in Fig. 5. There are
four STC features (Fig. 5A) that are statistically significant (Fig. 5B). The first STC feature appears as
a spatial bump with a 0.93 overlap with the STA feature. Thus the dominant STC stimulus dimension
is oriented in almost the same direction as the STA. The second STC feature is spatially bimodal and
the third and fourth STC features have higher frequency spatial oscillations; all of these second-order
features are nearly orthogonal to the STA and indicate space-time patterns beyond a “bump” that will
drive the neuron to fire.
We complete the model by calculating the nonlinearity (Eq. 7). We first project out the component
along the STA feature from the STC features (Eq. 22) to find the orthogonal components. The first STC
feature has such a high overlap with the STA feature that the projection essentially leaves only noise.
The second STC feature is essentially unchanged by the projection. As there are too few spikes to
consider fitting more than a two dimensional nonlinearity, the nonlinearity is computed as a function
of two variables, i.e., p(spike|φsta · s, φ⊥
stc,2 · s) (Fig. 5C). As a check on this calculation, we recover
the previous result for the nonlinearity with respect to the STA alone by projecting along the STC
axis (Fig. 5C). The corresponding nonlinearity for the STC mode is bowl-shaped, increasing at large
negative as well as positive values of the overlap of the stimulus with φ⊥
stc,2 . Such a nonlinearity can
arise if the neuron is sensitive to a feature independent of its sign, e.g., responds equally to ’ON’ or
’OFF’ inputs, as in some retinal ganglion cells (Fairhall et al., 2006; Gollisch and Meister, 2008).
Figure 5: The spike triggered covariance features for the response of retinal ganglion cell 13. (A) The two
significant STC feature vectors, in addition to the STA feature for comparison, using the stimulus representation
with NT = 6 and NX = 100. The feature vector φstc,1 has 0.930 overlap with φsta , while φstc,2 through φstc,4 ,
have only a 0.195, 0.109, and a 0.052 overlap, respectively. (B) The significance of each candidate STC feature,
i.e., eigenvectors of ∆C (Eq. 18) were determined by comparing the corresponding eigenvalue (red and black)
to the null distribution (gray shaded area). (D) The nonlinearity in the space spanned by the STA and the second
orthogonalized STC feature, after the STA feature was projected out (Eq. 22), φ⊥
stc,2 , completes the construction
of the spiking model. The nonlinearity is a scaled version of the bivariate probability p(spike|s(t)), which is
found by invoking Bayes’ rule (Eq. 7). The marginals of this distribution give nonlinearities with respect to the
STA (top) and second STC features (right) alone.
Interpreting the features
For a sufficiently large dataset, the significant STC features are guaranteed to span the entire subspace
where the variance of the spike-triggered stimulus ensemble is not equal to the variance of the prior
stimulus distribution (Paninski, 2003). In contrast to the corresponding result for the STA feature,
for the STC feature this guarantee only holds when the stimulus distribution is Gaussian or under
certain restrictions on the form of the nonlinearity (Paninski, 2003). Even when it is difficult to obtain
an accurate model for the nonlinearity, the relevant STC features help to develop an understanding
of the processing the system performs on its inputs. For example, in the retina, STC analysis can
reveal potentially separate ON and OFF inputs to an ON/OFF cell (Fairhall et al., 2006), as noted
above, and can capture spatial or temporal phase invariance, such as that exhibited by complex cells,
by spanning the stimulus space with two complementary filters that can add in quadrature (Touryan
et al., 2002; Fairhall et al., 2006; Rust et al., 2005; Schwartz et al., 2006; Maravall et al., 2007). Because
the spectral decomposition of the symmetric matrix ∆C always returns orthogonal components, the
STC features cannot in general be interpreted as stimulus subunits that independently modulate the
cell’s response (McFarland et al., 2013). Instead, the features span a basis that includes relevant stimulus components, which may be found by rotation or other methods (Hong et al., 2008; Kaardal et al.,
2013; Ramirez et al., 2014), strengthening the potential link between the functional model and underlying properties of the neural circuit. In the case of single neuron dynamics, the sampled STC
feature vectors can in some cases be shown to correspond to a rotation of eigenvectors derived from
subthreshold neuronal dynamics (Hong et al., 2008).
Relation to Principal Component Analysis (PCA)
While in PCA one usually selects eigenvectors that point in the directions of maximum variance,
stimulus dimensions that are relevant to triggering a spike have a variance which may be either
decreased or increased relative to the background. Consider for example a filter-and-fire type neuron
(Agüera y Arcas and Fairhall, 2003; Paninski, 2006), where the neuron extracts a single component
of the stimulus and fires when the projection of the stimulus onto that feature is larger than some
threshold. The variance of the spike-triggered stimuli in the direction of that filter will therefore be
reduced relative to the background. On the other hand, for a neuron such as an ON/OFF neuron
in the retina (Fairhall et al., 2006), the neuron is driven to fire by an upward or by a downward
fluctuation in light level. While the STA feature may then be close to zero, the eigenvalue for the
eigenmode describing that feature will be positive, as spike-triggering stimuli will have both large
positive and large negative values.
Natural stimuli and correlations
Our development so far has focused on methods that work well for white noise inputs, yet neurons
in intermediate and late stages of sensory processing, for example, areas V2 or V4 in the visual pathway, are often not responsive to such stimuli. Rather, robust responses from these cells often require
drive by highly structured stimuli, such as correlated moving stimuli that are typical of the statistics
of the natural sensory environment (Simoncelli and Olshausen, 2001). In this case, the methods discussed above may be inappropriate or at the very least can be expected to yield suboptimal models.
Therefore, much attention has been given to developing methods that are appropriate to analyzing
neuronal responses to natural stimuli or stimuli with statistics that match those of the natural sensory
environment (David and Gallant, 2005; Sharpee, 2013).
Another facet of coding natural scene statistics is that animals self-modulate the structure of incoming stimuli through active sensing. While one could, for example, sample the natural scene statistics
of a forest environment by computing the spatial and temporal correlations recorded by a stationary
video camera (Ruderman and Bialek, 1994; van Hateren and van der Schaaf, 1998), an animal navigating through the forest experiences very different statistics because of its body motion (Lee and
Kalmus, 1980) and saccadic eye movements (Rao et al., 2002; Nandy and Tjan, 2012). It is desirable
to characterize the response properties of groups of neurons to the type of inputs driving them in a
scenario that is as close to real as possible, but as we will see below, analysis of responses to such
stimuli presents considerable challenges.
Spectral whitening for correlated stimuli
Our calculation of features so far has been limited to the case of white noise stimuli with a variance
that is equal, or nearly equal, in all stimulus dimensions. This led to a covariance matrix for these
stimuli, Cp , whose eigenvalue spectrum was nearly flat (Fig. 3B). Yet stimuli in the natural sensory
environment have statistics that differ markedly, with spatiotemporal correlations and non-Gaussian
structure (Ruderman and Bialek, 1994; Simoncelli and Olshausen, 2001). While the complex higherorder moments of natural inputs may be relevant for neural responses and will not be captured by
first- and second-order moments (see for example Pasupathy and Connor (2002)), we can still address the issue of correlation. A correlated stimulus has a prior covariance matrix Cp that contains
significant off-diagonal components and whose eigenvalue spectrum is far from flat.
The STA feature and the eigenvectors of ∆C, i.e., the STC features, will be filtered by the correlations
within the stimulus (Bialek and van Steveninck, 2005). There are two ways to correct for this. First,
the features may be calculated as above, and the effect of correlations removed by dividing by the
prior covariance matrix (Eq. 16). This process is referred to as decorrelation or whitening, and we
denote the whitened features as φ̂sta and φ̂stc,i , where:
p φsta
p φstc,i ,
i = 1, . . . , K − 1,
where φsta and φstc,i are the estimates defined by Eqs. (11, 20), respectively. The matrix C−1
p has the
same eigenvectors as Cp (Eq. 17) but the eigenvalues are inverted, i.e.,
i vi vi .
Recall that Cp and thus C−1
p are close to the identity matrix for white noise.
Equivalently, one can also first decorrelate or pre-whiten the stimulus itself by dividing by the prior
covariance matrix (Theunissen et al., 2001; Schwartz et al., 2006):
ŝ(t) = Cp 2 s(t),
and then proceed with the STA and STC analysis as defined by Eqs. 11 and 20, but with s(t) replacing
− 12
ŝ(t). Similar to the inverse of the covariance of the prior (Eq. 25), the matrix Cp
− 12
is defined by
λi 2 vi vi> .
The decorrelation procedure is also applied when producing the null eigenvalue distribution used to
determine the significance of the STC features (Eq. 21).
The whitening procedure is usually numerically unstable as it tends to amplify noise. This is because
decorrelation attempts to equalize the variance in all directions. Yet the eigenvector decomposition
of the stimulus prior covariance matrix, Cp , includes directions in the stimulus space that have very
low variance, i.e., small values of λi that are also likely to be poorly sampled. Unchecked, this leads
to dividing the feature vectors or stimulus by small but noisy eigenvalues that amplify the noise in
these components. This is especially a problem when there is a big difference between the large and
small eigenvalues of Cp . To surmount this problem, we replace C−1
p with the pseudoinverse of Cp ,
which allows one to discard the small eigenvalue modes. The pseudoinverse of order L and pseudo
square-root inverse of order L, with the eigenvalues λi arranged in decreasing order and L < N , are
respectively defined as:
i vi vi
λi 2 vi vi> .
Multiplying by the pseudoinverse is equivalent to projecting out components of the stimulus along
directions vi that correspond to small λi before multiplying by the inverse.
The order of the pseudoinverse, L, is a regularization parameter that allows one to decide how small
the variance along a certain direction of the stimulus space has to be in order to decide that one cannot
accurately estimate the component of the feature in that direction. If we are able to construct a full
spiking model of features and nonlinearity, we may choose the value of L as the one that yields a
model that gives the best predictions for a test dataset; this is the course we followed.
Features from thalamic spiking during whisking in rat.
As an animal probes its environment, it presumably encodes and separates information about the motion of its sensors from the sensor’s responses to external stimuli (Nelson and MacIver, 2006; Kleinfeld
et al., 2006; Schroeder et al., 2010; Prescott et al., 2011). Rat whisking provides an excellent example
of such active sensing in which spiking is tied to the motion of the vibrissae, i.e., long hairs that the
rat sweeps through space as it interrogates the region about its head (Fig. 6A). Whisking consists of
an underlying 6 to 10 Hz rhythm whose envelope and set-point are slowly modulated over time. It
is often convenient to characterize vibrissa position in terms of phase in the whisk cycle as opposed
to absolute angle (Curtis and Kleinfeld, 2009) (Fig. 6A), as many neurons have a preferred phase for
spiking (Fig. 6B). In our dataset, we include records of spiking from seven neurons along the primary
sensory pathway in thalamus along with vibrissa position as the rats whisked in air (Moore et al.,
2015a) (Fig. 6C). To ensure that the mean firing rate is stationary over the time-course of each behavioral epoch, we decomposed the whisking stimulus using a Hilbert transform (Hill et al., 2011a) and
removed shifts in the set-point of the motion (compare red and blue traces of the full reconstruction
in Fig. 6C), and then reconstructed the stimulus as changes in angle with respect to the set-point (Fig.
To analyze these data, we choose a 300 ms window with a 2 ms sampling period so that the stimulus
s(t) is a NT = 150 dimension vector in time. Here, because we consider only a single whisker, NX = 1
and N = NT . The prior covariance (Eq. 17) has eigenvalues that fall off dramatically by a few
orders of magnitude (Fig. 6E), contrasting with the nearly flat spectrum of white noise (Fig. 3B). The
dominant eigenvectors appear as sines and cosines at the whisking frequency (modes 1 and 2 in Fig.
6E), with higher order modes corresponding to variations in amplitude (modes 3 to 6) and higher
harmonics (mode 7 and 8). The power in modes higher than about 60 is negligible. This spectral
decomposition illustrates the high degree of correlation of the stimulus and the considerable variation
in the sampling of each stimulus dimension, seen from the amplitude fall-off in high frequencies.
Lastly, we observed that while the inter-whisk interval shows a peak at the whisking frequency, the
inter-spike interval for a representative neuron appears largely exponential despite the presence of a
strong rhythmic component in the stimulus (Fig. 6C).
We first consider the case of the feature vectors without whitening. We computed the STA feature
(Eq. 11) (Fig. 7A) and the three significant STC features (Eq. 18) (Fig. 7A,B) for neurons in vibrissa
thalamus. The STA feature appears as a decaying sine wave (Fig. 7A) and the dominant STC feature
appears as a phase-shifted version of the φsta (gray, Fig. 7A). The overlap of φstc,1 with φsta is small,
-0.06. Thus the dominant unwhitened STC feature could be safely orthogonalized relative to the
unwhitened STA feature (Eq. 22) and used to construct a nonlinear input/output surface for this cell
(not shown).
We repeated the above analysis with a whitened stimulus. The stimulus was decorrelated using an
order L pseudoinverse (Eq. 25), where L was varied between 2 and 40. For each value of L we computed a predictive model, as described below, and chose L = 3 as providing the best predictability.
We show the decorrelated (Eq. 23) and regularized (Eq. 28) STA feature (Fig. 7A) and the one significant STC feature (Fig. 7A,B). Here, the STA and the STC feature are very similar to those for the
unwhitened case even though the analysis was restricted to a three dimensional subspace spanned
by the leading eigenvalues of ∆C (Egs. 18) after whitening . We then constructed the nonlinear input/output surface for the cell using these feature vectors (Fig. 7C). The nonlinearity with respect to
the STA feature alone appears as a saturating curve with a shut-down for extremely high inputs.
Maximally informative dimensions
So far the model features and nonlinearity have been nonparametric, determined only by data. Another method in the same spirit is that of maximally informative dimensions (Sharpee et al., 2003, 2004;
Rowekamp and Sharpee, 2011), an alternative means to find spike-triggering features and an arbitrary
nonlinearity. Rather than using a geometrical approach, this method instead implements a search to
locate a feature that maximizes the information that the spikes contain about this feature, i.e., the
Figure 6: Spike responses from thalamic cell 57 in response to whisking in air. (A) The coordinate systems
used to describe the whisk cycle. The left is absolute angle, θwhisk and the right is phase, Φ(t), which are related
by θwhisk (t) = θprotract - θamp (1 − cos(Φ(t))). (B) The spike rate as a function of phase in the whisk cycle; the
peak defines the preferred phase Φo . (C) A typical whisk, the stimulus, and spikes in the vibrissa area of ventral
posterior medial thalamus. We show raw whisking data and, as a check, the data after the components θprotract ,
θamp , and Φ(t) were found by the Hilbert transform and the whisk reconstructed. (D) Reconstructed whisk,
leaving out slowly varying mid-point θprotract - θamp . The self-motion stimulus is taken as the vibrissa position
up to 300ms in the past with ∆t = 2 ms time bins, so that NX = 1, NT = 150, and thus N = 150. (E) The
spectrum of the covariance matrix of the self-motion (Eq. 16). Note the highly structured dominant modes.
(F) The inter-whisk and inter-spike intervals. Methods: The whisking dataset is used to illustrate our methods
with a stimulus that contains strong temporal correlations. It consists of seven sets of spike arrival times, each
recorded from a single unit in the vibrissa region of ventral posterior medial thalamus of awake, head-restrained
rats (Moore et al., 2015a). The animals were motivated to whisk by the smell of their home-cage. Spiking data
were obtained with quartz pipets using juxtacellular recording (Moore et al., 2015b); this method ensures that the
spiking events originate from a single cell. The anterior-to-posterior angle of the vibrissae as a function of time
was recorded simultaneously using high-speed videography. Each time-series contained 4 to 14 trials, each 10 s
in length, with between 1,300 and 3,500 spikes per time series. The correlation time of the whisking, which serves
as the stimulus for encoding by neurons in thalamus, is nominally 0.2 s (Hill et al., 2011a). We found that the
cells’ response was strongly modulated by the whisker dynamics only when the amplitude θamp was relatively
high; therefore we constructed the models and tested their predictions only for periods when θamp ≥ 10◦ .
Figure 7: The spike-triggered average and spike-triggered covariance feature vectors for the response of thalamic cell 57 in the rat vibrissa system. (A) The STA feature and the same feature computed for the whitened
stimulus, along with the leading STC features calculated with and without whitening. The dashed curves are
after projecting out the STA vector from the STC modes. (B) Comparing the eigenvalues of ∆C, without whitening, to the null eigenvalue distribution computed from randomly shifted spike trains demonstrates the statistical
significance of the leading STC eigenvectors; red denotes significant eigenvectors and black not significant. For
the case of ∆C with whitening, regularization led to only three eigenvectors of which one was significant. (C)
A two dimensional model of the nonlinearity for φ̂sta and the leading STC feature φ̂stc,1 , both computed after
whitening. We further plot the two marginals.
mutual information between stimulus and spikes. To understand this approach, we return to the definition of the nonlinearity based on Bayes’ rule (Eq. 7), which we will recall just for a single feature
and the corresponding projection of the stimulus, i.e., z1 = φ1 · s, so that:
r(t) ∼
p(z1 |spike)
p(z1 )
One wishes to find a feature φ1 such that this function varies strongly with z1 . If it is constant, the
observation of a spike gives no information about the presence of the feature in the input, and conversely that feature is not predictive of the occurrence of a spike. The mutual information between
spike and stimulus will be maximized when the two distributions, p(z1 |spike) and p(z1 ), are as different as possible. This can be measured through the Kullback-Leibler divergence. In this approach,
one searches for the direction that maximizes the divergence between the distribution of all stimuli,
projected onto φ1 , and the spike-conditional distribution of these projections. Unlike the STC procedure, this approach requires no assumptions about the structure of the stimulus space and has been
applied to derive features from natural images. It can also be extended to multiple features. In general, however, this method is computationally expensive and prone to local minima, so we do not
implement this analysis here; the code can be downloaded from http://cnl-t.salk.edu/Code/.
Models with constrained nonlinearities
The ability to find nonparametric stimulus features and nonlinearity can be severely constrained by
data size. As we have seen, with realistic amounts of data, such models are often under-sampled,
particularly if one wants to incorporate dependence on multiple features and other factors such as
the history of spiking and, potentially, network effects. The methods we will discuss next instead
make specific assumptions about the form of the nonlinearity that simplify the fitting problem.
In this approach, one poses a so-called noise model for the responses given the stimulus and the choice
of model parameters and then estimates the parameters of the model that best account for the data.
Once the noise model is specified the likelihood of a given set of parameters given the data can be
computed. Maximization of the likelihood function then provides an estimate of the model that best
accounts for the data. This maximization can be achieved reliably when the likelihood is convex. A
convex function, one whose curvature does not change sign, can have no local minima or maxima,
thus maximization can be performed using local gradient information and ascending the likelihood
function to a unique peak. There are many convex optimization algorithms available, for instance the
conjugate gradient ascent algorithm (Malouf, 2002).
An important consideration in fitting these models is that, even in cases in which the solution is
unique due to convexity, the model may be accounting for variation that is specific to the data used
for the fit. This is a phenomenon known as over-fitting and it manifests as a decrease in predictability
of the model on novel datasets relative to the quality of the fit obtained in the training data. To ensure
that the model is not simply capturing noise terms specific to the training set, a comparison between
performance on test and training data is, for all approaches, a critical validation step. To minimize
overfitting, one can increase the tolerance of the fitting function such that the gradient ascent stops
when the model parameters have not yet reached the global minimum. Alternatively, one can partition the data into different random choices of training and test sets, known as jackknife resampling,
Table 1: Moments for MNE models
[i]-th component of a vector
[i, j]-th component of a matrix
hr(t) s[i](t)i
t n(t)s[i](t)
nT /N
hr(t) s[i](t) s[j](t)i
t n(t)s[i](t) s[j](t)
ts s[i](ts )p(spike)p(spike|s(ts ))
ts s[i](ts )s[j](ts )p(spike)p(spike|s(ts ))
where nT is as before the total number of spikes and ts are the spike times.
and run the optimization repeatedly on these different partitions. The resulting parameters may then
be averaged over the repetitions; the variability of the estimates may also be quantified.
Maximum Noise Entropy (MNE) method
A theoretically principled way to specify a noise model is by assuming a conditional probability
distribution of stimuli and responses, p(spike|s), that is as agnostic as possible about the relationship
between input and output, while remaining consistent with well-defined measurements on the data.
This can be done by assuming that the variability in the response is described by a maximum entropy
distribution; that is, a distribution that has the maximum possible variability given the stimulus and
constraints set by measurements of the data. In this approach, called the Maximum Noise Entropy
method, we compute moments of the measured spiking response with respect to the stimulus and
equate these with the same moments calculated with the joint probability distribution from the model
(Table 1). A full list of moments across the N dimensions of the stimulus space contains complete
information about the neuronal response. However, as in other approaches, it is typically difficult to
go beyond two moments.
The functional form of the maximal noise entropy joint distribution, with constraints to second order,
(Globerson et al., 2009; Fitzgerald et al., 2011b,a) is given by
p(spike|s) =
1 + exp {a + h · s + s> Js}
The parameters of the model are a, a scalar needed to satisfy the zeroth order constraint; h, an
N -component vector needed to satisfy the first order constraints and J, an N × N symmetric matrix needed to satisfy the second order constraints. The distribution (Eq. 31) is found through a
convex minimization procedure that evaluates the moments (Model in Table 1) with the constraint
ts p(spike|s(ts )) = 1. There is no need to use a spectrally white stimulus with MNE.
Interpreting the model.
How does the MNE model (Eq. 31) ensure the maximal variability in the spike rate? Consider the
maximum entropy distribution (Eq. 31) without any constraints, i.e., a = h = J = 0. The probability of
a spike given a stimulus then is p(spike|s) = 1/2 and can be thought of as the least structured spiking
model. At every time bin the neuron will fire or not fire with equal probability. The next simplest
model is the one where the probability of a spike is independent of the stimulus p(spike|s) = p(spike),
but the overall firing rate is constrained to be the experimentally measured rate r0 . Now the goal of
the fitting procedure is to find a such that r0 = p(spike|s) = 1/(1 + ea ), which yields a = log (1/r0 − 1).
In general, when there are multiple parameters and spiking depends on the stimulus, a numerical
fitting procedure is required to fit the value of the constraints computed from the data and return the
value of the parameters for the second order model (Eq. 31). The zeroth order term, hr(t)i, has no
stimulus dependence and, as explained above, enforces that the average firing rate of the MNE model
will equal that of the neuron. The parameters h and J act as linear feature vectors analogous to φsta
and the φstc,i :
• Setting J = 0, equivalent to choosing a first order MNE model, results in the model
p(spike|s) =
1 + exp {a + h · s}
This is equivalent to a STA model with feature φsta ≈ h and a sigmoidal nonlinearity.
• The matrix J can be decomposed in terms of its eigenvalues λi and eigenvectors, denoted ui ,
with i = 1, . . . , K, i.e.,
λi ui u>
i .
Defining the projection of a stimulus vector onto an eigenvector as zi = ui · s allows us to
rewrite the quadratic term in Eq. (31) as:
s> Js =
λi (s · ui )(ui · s) =
λi zi2 .
Therefore, the eigenvectors of J with large eigenvalues, in absolute value, can be viewed as
analogues of the STC features φstc,i with a quadratic-sigmoidal nonlinearity. The match is not
exact as the φstc,i were calculated from the covariance difference matrix (Eq. 18), which is taken
relative to φsta . Similarly to the STC method, the eigenvectors of J are orthogonal to each other
and thus we may not interpret these spatiotemporal vectors as independent receptive fields that
drive the cell’s response.
In the STC approach, the significance of a given feature was determined by comparing the corresponding eigenvalue of ∆C (Eq. 18) to the null distribution constructed using shuffled spike trains.
Here, because the model parameters are estimated using a gradient ascent algorithm, we cannot construct a null model using shuffled spike trains. It is still possible, however, to estimate which of
the eigenvalues of J correspond to features, denoted ui , that significantly modulate the spiking output. We accomplish this by shuffling the entries of J and computing the eigenvalues of the shuffled
matrix. Note that the shuffled matrix must remain symmetric and the diagonal and off-diagonal elements should be shuffled separately. Eigenvalues of the matrix J obtained from the real data are
said to be significant only if they exceed the range calculated using this shuffling procedure, since the
shuffled matrix represents a set of features with the same statistics as the components of the MNE
models, but without the structure.
Features from thalamic spiking during whisking in rat.
We applied the MNE procedure to the datasets obtained from thalamic recordings while rats whisked
in air, as shown for a representative unit in Fig. 8. As expected, the calculated first-order feature, h,
closely approximated the STA feature (Fig. 8A). We found that the top nine of N = 150 eigenvectors
Figure 8: The dominant features calculated by the Maximum Noise Entropy method for example thalamic
cell 57. (A) We fit a MNE model to the spike train with the same stimulus representation, with N = 150, and
plot the first feature, i.e., h, and statistically significant second-order feature vectors, i.e., eigenvalues of J (Eq.
3.1.1). We also plot the STA feature next to the first order mode for comparison. (B) The number of significant
second order features was found by comparing the eigenvalues of J to a null distribution.
of J were statistically significant (Fig. 8B). The dominant feature, u1 , makes a substantial contribution
at short times, like the dominant STC feature φstc,1 (Fig. 7A), but decays much more rapidly than the
STC feature. The higher order features calculated from J, i.e., u2 through u9 , correspond to variations
in the stimulus from whisk to whisk and have no clear interpretation.
A number of practical matters arise. First, in its raw form, the fitting procedure can generate high
frequency components. In the present case, we filtered the significant features by removing the components orthogonal to the first 15 principal components of the stimulus. Second, we use the full
matrix J that was found by the fitting procedure to generate the predictions using this model (discussed later under Model Evaluation). Removing the insignificant eigenvectors often leads to poor
predictions because the average spike rate predicted from the model no longer exactly matches the
zeroth moment, i.e., the average firing rate, since the projections onto the insignificant eigenvalues
of J do not sum exactly to zero. Second, while potential issues with over-fitting are always an issue,
they did not arise with this dataset, possibly because of the rapid fall-off of the eigenvalues for the
covariance of the stimulus matrix (Fig. 6E). We return to the issue of overfitting when we discuss
validation of the models and note that the MNE method was particularly susceptible to overfitting
for white noise stimuli.
The feature vectors in the first two models we discussed, namely STA and STC, are computed directly
from the spike-triggered and prior stimulus distributions, and do not require a fitting procedure to be
applied. As such they do not suffer significantly if the the stimulus space is expanded, for example
by assuming that the spiking depends on the stimulus history further back into the past. However,
if the cell’s response is found to be modulated by a large number of features, e.g., multiple STC
modes, the number of spikes will severely limit how many of these can be incorporated in a predictive
model. In the second-order MNE model, on the other hand, the number of parameters scales as the
dimensionality of the stimulus squared, i.e., N 2 . Therefore it may suffer from overfitting as a large
number of spikes is required to accurately fit the parameters.
Here we discuss two forms of separability, which can be thought of as approximations that two or
more of the model components act independently. If these approximations are accurate for a given
cell, they may greatly reduce the number of spikes needed to fit the model or help prevent overfitting.
Separability of a feature vector
Many stimuli, such as the checkerboard presented for the retinal studies, consist of both spatial and
temporal components. Yet only a small number of these NX × NT components (Eq. 9) are likely
to be significant. The spatiotemporal features φi may, in general, be expanded in a series of outer
products of spatial modes and temporal modes (Golomb et al., 1994). We define these as φiX,d
i , respectively, where d labels the mode.
NX spatial positions
We express φi in the same form of a matrix for the space time stimulus (Eq. 9), i.e.,
 x
 
 
φi (1, NT ) 
  φi (1, 1)
φi (x, t) = 
 
 
 
 φ (N , 1) · · · φ (N , N )
y i X
NT time points
min(NX ,NT )
λd 
(NX )
 T,d
 φ (1)
i (NT )
where λd is the weight of the dth mode of the feature, also referred to as the singular value in singular
value decomposition.
A great simplification occurs if the dependence on spatial components and temporal components is
separable. In this case, the spatiotemporal features are well approximated by the product of a single,
i.e., d = 1, spatial and temporal contribution. This corresponds to a single spatial pattern that is
modulated equally at all pixels by a single function of time. This assumption reduces the number of
parameters one needs to estimate, per feature, from NX × NT to NX + NT .
Separability of the nonlinearity
Another important form of separability relates to the nonlinear function g(·). While the nonlinearity
g(·) can be any positive function of the K stimulus components zi , the amount of data required to
fit g(·) over multiple dimensions is prohibitive. It is possible to get around this data requirement
by making assumptions about g(·). First, one might assume that the nonlinearity is separable with
respect to its linear filters (Slee et al., 2005). Under this assumption, g(·) can be written as:
g (z1 , . . . , zK ) = g1 (z1 ) × · · · × gK (zK ).
This approximation is equivalent to assuming that the joint conditional probability distribution over
the projections of the stimulus on the filters, p(z1 , z2 , . . . , zK |spike), is equal to the product of the
marginal distributions, p(z1 |spike) . . . p(zK |spike). The validity and quality of this approximation can
be quantified using mutual information (Adelman et al., 2003; Fairhall et al., 2006), which is a measure
of the difference between joint and independent distributions.
Beyond the enormous reduction in the number of spikes sufficient to accurately fit the model, a separable model that makes reasonably good predictions can help us interpret the model and potentially
relate it to circuit and biophysical properties of the system. A successful separable model implies
that the cell is driven by processes that are, to a good approximation, independent. These could be,
for example, inputs from parallel pathways such as separate dendrites or subunits, or the effects of
feed-forward versus feedback processing. A specific example of a model whose typical application
generally assumes that different factors influencing the firing of the neuron contribute independently
and multiplicatively is the generalized linear model.
Generalized Linear Models (GLM)
While the models so far only consider stimulus dependence, the biophysical dynamics of the neuron
or local circuit properties might alter the ability of the cell to respond to stimuli as a function of
its recent history of activity. For example, all neurons have a relative refractory period that could
prevent them from spiking immediately after a previous spike, even if the stimulus at that time is
one that normally strongly drives the cell (Berry and Meister, 1998). Further, projection neurons
have a tendency to emit bursts of spikes, such that the probability of a spike will be increased if the
cell has recently spiked (Magee, 2003). These effects, and other more general dependencies, can be
incorporated in the framework of a generalized linear model (GLM) (Nelder and Wedderburn, 1972;
Brown et al., 1998).
Generalized linear models are a flexible extension of standard linear models that allow one to incorporate non-linear dependencies on any chosen set of variables, including the cell’s own spiking
history. They gain this ability to incorporate a richer set of inputs by taking an explicit form for the
nonlinear function g(·) to reduce demands on data. A GLM is characterized by the choice of g(·) and
by a noise model that specifies the distribution of spiking, required to be within a class of distributions known as the exponential family. This includes many appropriate probability distributions, e.g.,
binomial, normal, and Poisson. As in previous approaches, we choose a Poisson process, for which
the probability of counting n spikes in a time bin of width ∆t at time t is determined by the predicted
firing rate r(t) averaged over that time bin, i.e.,
p(n spikes between t − ∆t and t) =
(r(t)∆t)n (t) −r(t)∆t
The firing probability is taken to be a function g(·) of a linear combination of the stimulus, the recent
spiking of the cell, and potentially other factors (Fig. 1A). In its simplest form, the spike rate is given
r(t) = g a +
φglm (t ) · s(t ) +
t0 <t
ψ(t )n(t ) .
t0 <t
where the parameter a sets the overall level of the firing rate, the sum
t0 <t
φglm (t0 ) · s(t0 ) is the
familiar projection of the stimulus onto the spatiotemporal feature φglm (t), and we have now included
a temporal spike history filter, denoted ψ(t), which is a Nh dimensional vector that weights the recent
activity of the neuron. Together we refer to the set of parameters for the GLM as Θ.
As before, r(t) is a function of the stimulus and depends on all parameters, Θ, of the model. The task
is to determine the optimal value of Θ given the specific observed sequence of spike counts. This is
done by maximizing the likelihood, i.e., the probability of the data given the parameters viewed as a
function of the parameters, L(Θ) = P (n(t)|Θ), over choices of Θ.
When the nonlinearity g(·) is both convex and log-concave, the likelihood function will itself be a
convex function. This means that the likelihood L(Θ) has a single, global optimum that can obtained
through any convex optimization routine. Fortunately nonlinearities that satisfy this property include
common choices like the exponential and the piecewise linear-exponential function (Paninski, 2004).
Thus we maximize the log-likelihood, which for Poisson spiking is
log L(Θ) =
log (r (s(t)|Θ) ∆t) −
(r(t)∆t) .
where r (s(t)|Θ) is the predicted firing rate. With this, the computational fitting problem we solve is
argmax (log L(Θ)) ,
which can be maximized through a convex optimization routine of choice.
Overfitting and regularization
As for other methods, the fitted model may best fit the training data but not generalize to test datasets.
In a likelihood framework, overfitting is simple to understand: one can always improve the loglikelihood simply by adding more parameters. Indeed, if the number of parameters encompassed by
Θ is the same as the dimensionality of n(t) we can construct a model that fits the observed data exactly.
But this is not the aim of constructing a model. Rather, we seek to find a model that captures trends
in the data that are common across different samples, rather than details of individual fluctuations.
Overfitting arises either as a result of insufficient training data relative to the number of parameters
being estimated or from the model containing more parameters than are needed to describe the relationship under consideration. As discussed with respect to natural stimuli, correlations in the input
reduce its effective dimensionality of the data and thus the number of parameters required in the
model. A common effect in GLMs that are fit to slowly-varying stimuli is the presence of high frequency components in the feature vector, as occurred for the MNE model, since such fast variations
projected onto the slowly-varying stimulus cancel out and minimally effect the predicted spike trains
and log-likelihood (Eqs. 40 and 41). While their effect on the log-likelihood may be minimal, they obstruct interpretation of the feature vectors φglm . Such overfitting can be avoided by penalizing models
that are over-parameterized by adding a penalty term Q(Θ) to the quantity we are maximizing:
argmax log L(Θ) − Q(Θ).
For instance, to avoid overfitting we might choose the term Q(Θ) to be large for models that contain
a large number of non-zero parameters. The simple choice,
argmax log L(Θ) − NΘ ,
where NΘ is the number of parameters of the model, is known as the Akaike Information Criterion
(Akaike, 1973; Boisbunon et al., 2014). This and related criteria provide a simple, principled means to
choose between competing models of differing numbers of parameters and may be used to determine
the optimal stimulus and history filter sizes (Shoham et al., 2005).
Penalty terms may be interpreted as representing prior knowledge relevant to the estimation problem.
In particular, if one has a prior distribution on the space of parameter estimates, pΘ (Θ), one can use
Bayes’ rule to find an estimate that maximizes the a posteriori probability, denoted ΘM AP , where
argmax log p(Θ|s; r)
argmax (L(Θ; s, r) + log pΘ (Θ)) .
Then we can identify the penalty term as the negative logarithm of the prior, i.e., Q(Θ) = − log pΘ (Θ).
For instance, if one expects the feature vector to be smooth, one might apply a Gaussian prior:
Q(Θ) = λΘ> DΘ.
The function Q(Θ) will penalize feature vectors that are not smooth or that vary excessively when
D is chosen to be a second-derivative operator (Linden et al., 2003). The weight λ is often chosen to
maximize the model’s performance on data withheld from the optimization procedure.
Finally, a very simple heuristic that sometimes mimics the effect of these regularization methods
to avoid overfitting is early stopping. Here we simply limit the number of iterations in the fitting
process to effectively stop the fitting before the unique solution is found. This approach assumes
that solutions near the optimal one for the training data are good and also lead to generalization.
This involves monitoring the form of the solution at each step of the optimization and choosing the
number of iterates that recovers a reasonable solution.
Choice of basis
For completeness, overfitting can be avoided by forbidding rather than just penalizing models that
are over-parameterized. This is achieved by reducing the number of parameters of the model to a
value known through experience to be reasonable. While we have discussed previously the simple
expedient of downsampling or truncating the data, more generally one can project the stimulus into
a subspace that captures important properties of the data; the basis vectors for this subspace then
define the number of parameters of the stimulus feature vector. One natural choice is to use the
leading principal components of the stimulus (Eq. 16) as the basis set. In the case of the spike-history
filter, one can choose basis functions that are appropriate to capture the expected biophysics of the
neuron, such as refractoriness or burstiness. A common set of basis functions for representing spike
history filters is a ’raised cosine’ basis:
 1 (cos [a log(t − ψi ) − φi ] + 1) , φi − π < a log(t − ψi ) < φi + π;
ki (t) = 2
that describes a sequence of bumps whose peaks are tightly spaced near the time of the spike, and
become increasingly sparse for earlier times. In this way the basis is well resolved where the spike
history filter changes most rapidly (Pillow et al., 2008).
Despite much theory surrounding their application (Paninski, 2004), correctly specifying a GLM using appropriate timescales and basis functions remains as much an art as a science. Particular care
must be taken in correctly parameterizing spike history filters. One approach is to initially fit the
model with no special basis functions, examine the resulting filters, and then choose a parameterization of a reduced basis, e.g., raised cosines or exponentials that allows for the form obtained in the full
dimensional case. While this involves fitting a full dimensional model, a lower dimensional model is
ultimately obtained that is less likely to be overfit.
Unfortunately nothing guarantees that the maximum likelihood estimate of a GLM will be stable.
Unstable models diverge when used to simulate novel spike trains. While such models may still
provide insight from the form of their feature vectors, they are not able to simulate spike trains on
novel stimulus datasets, the essence of model validation. If unstable GLMs are encountered, one
should first check that the parameterization of the spike history filter accurately characterizes the
neuron’s refractory period. In this regard, improper spike sorting that leads to the presence of spike
intervals that are less than the refractory period (Hill et al., 2011b) can cause misestimation of the
spike history term and lead to instability.
Features from retina and thalamic cells.
We fit GLMs for the set of retinal ganglion cells stimulated with white noise (Fig. 3) and thalamic
neurons stimulated by self-motion of the vibrissae (Fig. 6). We consider the white noise case first. A
sequence of delta functions, i.e., independent pixels, was used as the basis functions for the stimulus feature vector and raised cosines were used as basis of the spike history filter (Eq. 46). For the
same representative neuron used previously, the feature vector φglm corresponds to a transient spot
of illumination that is similar to the STA feature yet slightly delayed in time (Fig. 9A). This shift is
presumably the effect of the spike history dependence, which leads to increased firing rate approximately 40 ms after the previous spike, a time scale similar to the stimulus refresh, ∆t. Since the effect
of the spike history filter is exponentiated, we plot both the result of the fit (black line, Fig. 9B) and
the exponent of the filter (gray line, Fig. 9B) to illustrate the effect that this component of the model
has on spiking.
The GLM fit in the case of correlated noise gives a less intuitive, but thus perhaps more interesting,
result. Here the twelve leading terms of a PCA of the stimulus were used as the basis functions for
the stimulus feature vector and raised cosines were used as the basis of the spike history filter (Eq.
46). Here the feature vector φglm oscillates for one cycle then returns to baseline very quickly, before
even a single whisk is completed (Fig. 9C), and thus is quite different than the φsta . Further, while the
spike history shows a significant excitatory component, this component is extremely short lived (Fig.
9D). The GLM analysis therefore suggests that the thalamic cell is very responsive to instantaneous
Figure 9: The fit of the generalized linear model for the responses of retina ganglion cell 3 and thalamic
vibrissa cell 57. (A,C) The stimulus feature φglm compared with the previously calculated STA feature. (B,D)
The spike history filters ψ (black curves). We also plot the exponent of the filter (gray), as it is exponentiated in
the model, to better illustrate the effect this component of the model has on spiking.
changes in position of the vibrissae but has little dependence on the history of the stimulus or past
spiking beyond around 80 ms, corresponding to about half of a whisk.
Model evaluation
We now consider how well each of the models performs in predicting the spike rate for a fraction
of the stimuli, designated the test set, that has the same statistical properties as the training set but
is otherwise novel. In every case, 80% of the data was used as the training set for fitting the model,
and the remaining 20% was reserved for testing. A number of measures are available to test the
quality of the model in predicting spikes. The most direct and intuitive is the root mean square of the
difference between the recorded firing rate rs (t) and that predicted by the model. Ideally this would
be computed for responses to a repeated but rich stimulus so that one could estimate the intrinsic
variability of the neuronal spiking response. However, here and generally for natural stimuli, one
only has a single presentation of the stimulus, or the relationship between the external variable and
the spike train may be inherently non-repeatable, as during behavior when the stimulus is under the
animal’s control.
In this case, one can compare the log-likelihood of the data given the model for different models. For
Poisson spiking, (Eq. 38), this is
log L (φi ) =
ns (t) log
φi · s(t)∆t
φi · s(t)∆t − log (ns (t)!) .
Typically, the log-likelihood estimate has a common large offset that depends only on the firing rate
and a small range of variation of the term log ( i φi · s(t)) among different models because of the
logarithmic compression. To estimate a lower bound on the log-likelihood, we replace the calculated
rate with the measured rate to form a null hypothesis, i.e.,
log L (null) =
(ns (t) (log (ns (t)) − 1) − log (ns (t)!)) .
Spectral coherence
A complementary metric for the fidelity of the predicted spike trains is the spectral coherence between
the predicted and measured responses. This measure can distinguish the performance of different
models across different frequency bands, each of which may have particular behavioral relevance.
We define r̃(f ) and r̃s (f ) as the Fourier transform of the predicted and measured rates, respectively.
The spectral coherence, denoted C̃(f ), is:
C̃(f ) = p
hr̃? (f )r̃s? (f )i
h|r̃(f )|2 i h|r̃s (f )|2 i
where the multi-taper method is used for averaging, h. . . i, over a spectral bandwidth that is larger
than the Raleigh frequency 1/(NT ∆t) (Thomson, 1982; Kleinfeld and Mitra, 2011). The magnitude of
the coherence reports the tendency of two signals to track each other within a spectral band and is
normalized by the power in either signal. The phase of the coherence reports the relative lag or lead
of the two signals. There are no assumptions on the nature of the signals. The confidence level is
determined by a jack-knife procedure (Thomson, 1982).
Spectral coherence may be viewed in analogy to the Pearson correlation coefficient in linear regression, i.e., to the extent that real and imaginary parts of both r̃(f ) and r̃s (f ) may be considered as
Gaussian variables, C̃(f ) forms part of the regression coefficient. The expected value of the predicted
rate given the observed rate is:
E (r̃(f )|r̃s (f )) = b̃(f )r̃s (f )
where the coefficient b̃(f ) is:
b̃(f ) = C̃(f )
h|r̃(f )|2 i
h|r̃s (f )|2 i
The variance of the expectation, denoted V (r̃(f )|r̃s (f )), is given by
V (r̃(f )|r̃s (f )) = 1 − |C̃(f )|2 |r̃(f )|2
and, of course, goes to zero when measured and predicted signals are the same.
Validation of models with white noise stimuli
The predictions with the STA model, the STC plus STA model, and the GLM capture the gross variations in spike rate (Fig. 10A,B). The GLM yields representative spike trains, as opposed to rates,
so that we computed predicted rates by averaging over many spike trains computed by repeatedly
presenting the same stimulus to the same model. In these predictions, many spikes are unaccounted
for, while the spike probability also indicates spikes when none occur. Interestingly, the STA plus STC
model has the highest value of the log-likelihood (Eq. 47) while the GLM has the lowest, lower even
than the STA (Fig. 10C). This may imply overfitting of the training data with the GLM, as models that
Figure 10: Summary of the performance of model predictions for the retinal ganglion cell 13; three methods,
STA, STC plus STA, and GLM, are compared. (A) A part of the spike train cut out from the test set for illustration purposes. Insert Expanded temporal scale to highlight the slight delay inherent with the GLM. (B) The
predicted spike count per frame obtained by computing the probability of a spike corresponding to each stimulus frame (top, STA; middle, STC; bottom, GLM). Note that to generate a prediction from the GLM at time t we
need the history of the spike train up to that point t0 < t, which is not deterministic due to the Poisson variability.
Thus, the trace presented here (orange) is the average spike count over 500 simulations of the GLM on the test
set. (C) The log-likelihood (Eq. 47) of each model given the test set, which quantifies the quality of the prediction.
We also include the log-likelihood for the null condition (Eq. 48). (D) The spectral power of individual pixels in
the stimulus (black) and the recorded spike train (gray), as well as those of the predicted spike trains. The mean
value has been removed, so that the initial data point represents an average over the spectral half-bandwidth.
Spectra were computed with a half-bandwidth of 0.087 Hz as an average over 159 spectral estimators for 920 s
of data (E) The phase and magnitude of the spectral coherence between the recorded and predicted spike train
for each method. Coherence was computed with a half-bandwidth of 0.065 Hz as an average over 119 spectral
involve more parameters have a "higher" log-likelihood. All models, however, perform better than
the null expectation (Eq. 48) (Fig. 10C).
Greater insight into fitting of the models is provided by a spectral decomposition. First, the spectral
power of the stimulus is constant, by design (Fig. 10D), and the power of the spike train decreases
only weakly with increasing frequency, consistent with a Poisson process. The spectral power for
the spike rates predicted from three models, STA, STC plus STA, and GLM, show a rather strong
frequency dependence. The coherence is substantially below |C̃(f )| = 1 at all frequencies yet it is
statistically significant (Fig. 10E). Consistent with expectations from the log-likelihood (Fig. 10C), the
STC plus STA model has an approximately 5% improved coherence at all frequencies (Fig. 10E). The
GLM yielded the worst predictions. Further, while the phase for the STA and STC plus STA models
is close to zero, which implies that the predicted spikes arrive at the correct time, the phase is a
decreasing function of frequency for the GLM model (Fig. 10E). This implies that the predicted spikes
arrive with a brief time delay, as noted earlier (Fig. 10A), that is estimated to be (1/2π)(∆phase/∆f )
= -25 ms or less than ∆t (inset Fig. 10A).
For the white noise stimulus and this particular set of retinal ganglion cells, the data appear to be
adequately modeled by the single STA feature and the accompanying nonlinearity (Fig. 4C,D). The
coherence shows an improvement with the STC plus STA model (Fig. 10E). The GLM gives the worst
predictions by all measures, and the predicted spikes occur with a shift in timing compared to the test
data. Time delays relative to reverse correlation approaches have been seen in past implementations
of the GLM as well (Mease et al., 2014).
Not surprisingly, the MNE model, with a large number of parameters, was susceptible to over-fitting.
The parameters from fitting the stimulus set with n = 600 (Figs. 4C, 5A, and 10A) led to a stable
calculation of the linear feature, h, and three statistically significant second-order features (Eq. 31),
but the model gave poor predictions. The log-likelihood metric was lower for the MNE model than for
the null hypothesis (Fig. 10C) and the spectral coherence was relatively small. To reduce overfitting,
we truncated the stimulus from six to two frames to reduce the number of stimulus components to
n = 200. This procedure led to a linear feature and a single, statistically significant second-order
feature. The log-likelihood for this reduced model was now greater than that of the null hypothesis,
although still less than that for all other models. Similarly, truncation of the stimulus led to an increase
in the spectral coherence at all frequencies, although the coherence was still lower than that achieved
with the other models.
Validation of models with correlated noise from self-motion
We now consider the case of models for whisking cells in thalamus (Figs. 6 and 11). Here, the underlying stimulus is highly correlated and strongly rhythmic (Fig. 11A) with a broad spectral peak at the
fundamental and harmonic frequencies of whisking (Fig. 11D); recall that the stimulus has its slowly
varying midpoint removed (Fig. 6D). Despite the structure in the stimulus, the spectrum of the spike
train of our example thalamic cell, (Fig. 11D) was largely featureless.
Figure 11: Summary of the performance of predicted spike trains for thalamic neuron 3. Seven means of
analysis are compared, i.e., STA and STC plus STA, STA and STC plus STA after whitening of the stimulus,
MNE, GLM, and a phase tuning curve model. (A) The stimulus corresponds to vibrissa position with slowly
carrying changes in the set-point removed. (B) The predicted probability of spiking per 2 ms time bin obtained
by computing by each model and the corresponding stimulus. Note that to generate a prediction from the GLM
at time t we need the history of the spike train up to that point t0 < t, which is not deterministic due to the
Poisson variability. Thus, the trace presented here (orange) is the average spike count over 500 simulations of the
GLM on the test set. (C) The log-likelihood (Eq. 47) of each model given the test set, which quantifies the quality
of the prediction. (D) The power spectra of individual pixels in the stimulus (black) and the recorded spike train
(gray), as well as those of the predicted spike trains. Spectra were computed with a half-bandwidth of 0.6 Hz as
an average over 23 spectral estimators.(E) The phase and magnitude of the coherence between the recorded and
predicted spike train for each method (Eq. 49). Coherence was computed with a half-bandwidth of 1.2 Hz as an
average over 49 spectral estimators.
We first ask if whitening the stimulus does indeed lead to an improved prediction. We computed
the predicted rate from the feature vector for the STA model, i.e., φsta , and the feature vector after
whitening φ̂sta (Figs. 7A and 11B). As expected, the log-likelihood is greater after whitening (Fig.
11C). The spectral power for the φ̂sta is greater at the harmonics, but not the fundamental, compared
to the nonwhitened feature vector (Fig. 11D). Interestingly, whitening increases the coherence between the predicted and the measured rates at the whisking frequency, with |C̃(f )| increasing from
0.70 to 0.75, as well as at other frequencies (Fig. 11E). The exception is that the coherence below about
1 Hz, where variations in the envelope of the whisk may be coded, is better for the nonwhitened STA
feature vector.
We further computed the predicted rate for the feature vector for the STC model, i.e, φstc,2 , and the
feature vector after whitening, i.e., φ̂stc,2 (Figs. 7A and 11B). Unlike for the STA, the log-likelihood for
the STC plus STA model is diminished after whitening (Fig. 11C). As for the STA alone, whitening
increases the coherence between the predicted and the measured rates at the whisking frequency,
with |C̃(f )| increasing from 0.70 to 0.75, as well as at other frequencies, with the exception that the
coherence below about 1 Hz is better for the nonwhitened STC plus STA feature vectors (Fig. 11E).
Across all models, the best predictability at the whisking frequency occurred with the whitened STA
and the MNE models, albeit only by 5 to 10% compared with the STC plus STA model and the GLM.
All of the models exhibited a slight phase advance at the whisking frequency. This corresponds to a
time shift of approximately (1/2π)(∆phase/fwhisk ) = 20 ms, which is worrisome, although short compared to the approximately 160 ms period of a whisk. All told, none of the models was clearly “best”
at all frequencies, although the MNE model appeared to be strongly coherent with the measured train
at all but the lowest frequencies (Fig. 11E).
It has been shown that whisking may be characterized in terms of a rapidly varying phase (Hill et al.,
2011a), denoted Φ(t). If the firing of neurons is sensitive to phase in the whisk cycle independent
of frequency, then a linear feature vector will be a poor representation. We therefore constructed an
additional model in which we first applied a nonlinear transformation, the Hilbert transform (Hill
et al., 2011a), to the stimulus to extract Φ(t). We then used Bayes’ rule to construct a phase tuning
model to compare with the LN approaches (Fig. 6B):
p(spike|Φ) =
The phase model achieves the same high level of coherence at the whisking frequency as the whitened
STA and MNE models (Fig. 11D). This suggests that the feature vectors are largely acting as broadband filters. Of course, the tuning model performs badly for frequencies away from the approximately 6 Hz whisking peak (Fig. 11E).
Finally, we consider two additional thalamic neurons that had extreme response properties (Fig. 12).
The first is a neuron that tended to spike with respect to changes in the amplitude of whisking (Fig.
12A-C). Here the whitened STA and STC plus STA models did well, the MNE model was most impressive with greatest coherence over the broadest frequency range, and the GLM did poorly (Fig.
12D). On the other hand, we consider a neuron that responds almost solely to the phase of whisking
(Fig. 12E-G). All models performed well at the whisking frequency; the phase model performs particularly well (Fig. 12H), and here too the MNE model has higher coherence with the measured rate
at both lower and higher frequencies.
Figure 12: Summary of the performance of predicted spike trains for two additional thalamic cells, units 88
and 99. (A-D) The whisking stimulus (panel A) and predicted spike probabilities (panel B) for a cell with weak
phase tuning (panel C). Yet this cell was strongly modulated by the amplitude of whisking, which changes on
a slow time-scale, approximately 1 s, compared with changes in phase. The predicted rate is shown for two
models that perform about best, i.e., STA after whitening of the stimulus and MNE. The phase tuning model
performs poorly as it ignores the amplitude (panel D). (E-H) The whisking stimulus (panel E) and predicted
spike probabilities (panel F) for a cell with particularly strong phase tuning (panel G). The predicted rate is
shown for three models that perform about best, i.e., STA after whitening of the stimulus, MNE, and the phase
tuning model. Here the coherence between the predictions and the measurements in the whisking frequency
band is near 1.0 for all models (panel H).
This analysis suggests that for stimuli of this type, a metric for "goodness of fit" based on spectral
decomposition offers far more insight than a scalar measure based on maximum likelihood. This
may be particularly helpful when certain frequencies may have ethological significance. As for the
"best" method with the thalamus data, we were impressed with the results obtained with the MNE
model, which fits well over a broad range of frequencies. This stands in contrast to the difficulties in
using MNE with the white noise data.
Network GLMs
The GLM framework can be readily extended to network implementations of M neurons (Truccolo
et al., 2005; Pillow et al., 2008). Each neuron is considered to be driven by a filtered stimulus, its own
spiking history and also the filtered activity of the rest of the neurons. If ψij (t) (i, j = 1, . . . , M ) is the
filter acting on the spiking history of neuron j driving neuron i, then the model for the ith neuron is:
ri (t) = exp ai +
φi (t0 ) · s(t0 ) +
ψij (t0 )nj (t0 ) .
j=1 t <t
t <t
The incorporation of such network filters have been shown to improve the capability of the model
to account for correlations between neurons in a retinal population (Pillow et al., 2008). While it is
tempting to interpret the network filters as capturing, for example, synaptic or dendritic filtering of
direct interneuronal connections, these terms cannot be taken to imply that two neurons are anatomically connected. For example, correlations might arise from a common input that is not taken into
account through the stimulus filter (Kulkarni and Paninski, 2007; Pillow et al., 2008; Archer et al.,
Prior work found coupling terms, ψij (t) in a network GLM (Eq. 54), that could be interpreted as
functional interaction kernels between cells (Pillow et al., 2008). In that study, model validation of
each neuron was done using the stimulus and the recorded activity of the remainder of the cells. This
procedure is equivalent to fitting a single-cell model where the stimulus is expanded to include the
spiking history of the rest of the network, i.e., the nj (t). As a practical matter, this procedure has value
when one is interested in the precise timing of coupling between cells, e.g., to find whether neurons
are anatomically connected (Gerhard et al., 2013). Yet, in our opinion, expanding the stimulus to
encompass the spiking history of the rest of the network stands in contrast to validation of a true
network GLM, for which the spike histories are based solely on simulations and the only external
variable is the stimulus. We use the full network approach in our validation procedure.
Application to cortical data during a monkey reach task
We present an example of a network GLM based on nine simultaneous recordings from monkey
primary motor cortex in which the monkey performs a grip and reach motor task (Engelhard et al.,
2013). The model consists of feature vectors that relate to hand motion, as measured by a cursor
trajectory and grip force (Fig. 13 A), that were modeled with Gaussian bump’ basis functions. Since
Figure 13: Summary of the three dimensional monkey-based reach task with spike data from unit 36. Analysis is based on one approximately 90 minute recording while performing the task both cursor motion and grip
force are recorded. A Grip-and-reach task involves first moving the cursor to a central position, followed by
gripping the handle with sufficient force. Once gripping at the center, after a variable wait time, a target appears
randomly in one of 8 locations. Following another wait of a variable time, the cue at the origin disappears, acting
as a go signal, after which the monkey may perform the reach movement. Grip on the handle has to be maintained through the duration of the trial. A successful trial requires reaching the target within a set time limit.
Once the target is reached, the monkey needs to hold the cursor at the target for 700 ms, and to release its grip
on the handle. Following a successful trial, the monkey receives a reward, and after an inter-trial period the next
trial begins. B Measured cursor position and grip force. C Stimulus auto-correlation. D Distribution of interspike intervals shows a clear refractory period. Methods: Spikes were recorded from single isolated units in the
contralateral cortex to the task arm using an intracortical multi electrode array (Blackrock Utah array) implanted
in the arm region of M1. Spiking data were binned into millisecond intervals, while both cursor data and grip
force are sampled at 100Hz. Of the isolated units, we selected those which showed no evidence of contamination
based on inspection of the interspike interval distribution. Analysis was performed from the time of the Go
signal until the grip was released; see gray band in panel B.
motor neurons encode future motor outputs, the ‘stimulus’ filter encodes both causal and acausal
relationships, in that it is applied to past and future measurements of cursor and grip, relative to
the current time-bin (Fig. 13B). Similar choices with GLMs have been previously applied to neurons
in motor cortex (Shoham et al., 2005; Truccolo et al., 2005; Saleh et al., 2012). Lastly, we used raised
cosine basis functions (Eq. 46) for the spike history filters and the coupling filters for the histories
other neurons in the network.
The cursor position and grip data varies over hundreds of milliseconds to seconds (Fig. 13C), while
the spike history data varies on the order of milliseconds. Capturing effects on these separate time
scales within the same model requires some care, as the data is non-Gaussian and highly temporally
correlated. As noted previously, such correlation can result in uninterpretable high frequencies in
the feature vectors. This requires some form of regularization. The approach adopted here is to use
only a limited number of basis vectors that sparsely sample the stimulus at regular intervals, with the
interval size on the order of the stimulus auto-correlation time scale.
The fitting was performed only on data within the movement phases of the trials, excluding the hold
periods (Fig. 13A). In order to avoid unnecessary coupling terms, a group least absolute shrinkage
and selection operator (LASSO) (Yuan and Lin, 2006) penalty is applied to the sets of parameters
representing of connections between neurons. This takes the form
argmax log L(Θ) − λ
kΘi,j k2 .
where {Θi,j } are the parameters representing the coupling from neuron j to neuron i. A similar
penalty is applied in prior work (Pillow et al., 2008). The penalized likelihood is still convex, which
ensures global convergence.
As in the previous cases, the model is validated by splitting the data into a training set representing
80% of the total data. A test set representing 20% of the total data, or 4 minutes of recording, is used
for validation. We take a value λ =100 in our network analysis (Eq. 55); smaller values decreased
the log-likelihood while larger values reduced all coupling terms to near zero. We then calculated
the predicted rate for the models, used in log-likelihood estimate (Eq. 47), by averaging repeated
simulations of the GLM given the same stimulus.
With respect to the particular example of cell 36, we find that history filter is the same for the coupled
and uncoupled cases (Fig. 14A), coupling terms are present on a variety of time scales (Fig. 14B),
and the stimulus feature vectors are altered in magnitude by the coupling (Fig. 14C). Interestingly,
for all cells in the network, the log-likelihood of the model evaluated for the observed spike train
shows overall a negligible difference between the coupled and uncoupled models (Fig. 14D). This
is consistent with studies of coupled GLMs applied to retina data (Pillow et al., 2008), in which the
addition of coupling terms yields no observable benefit to predicting the average rate given the same
As seen for the case of the retina and thalamus datasets, more information can be gleaned from the
coherence between the predicted rate and the observed spike train. Significant spectral power in
Figure 14: Network GLM features and validation for monkey reach data using the interval between the start
of the Go signal and the end of the trial. A Spike history filter for sample unit 36, one of nine concurrently
record units in our analysis. The nine were chosen as those, out of 45 units, with no extra spikes in the refractory
period of the inter-spike interval. Green curve shows result for the coupled model (λ = 100 in 55) and black
curve shows the filter in the absence of coupling between units; in this case the two curves are indistinguishable.
B Spike history filters from eight neighboring cells for the coupled model (λ = 100).The coupling terms are
nonzero for three neighbors. C Feature vectors calculated for the network, i.e., coupled, (green) and single cell,
i.e., uncoupled GLM (black). D Scatter plot of Log-likelihood between predicted spike rate and observed spike
train for the coupled and uncoupled model. The black dot refers to the data in panels A to C. E. The spectral
coherence, calculated as an average over all trials with a 0.5 Hz bandwidth. The band is one SE. F Scatter plot of
the coherence between predicted spike rate and observed spike train for the coupled and uncoupled model. The
black dot refers to the data in panels A to C and E.
both coupled and uncoupled cases only occurred for low frequencies, i.e., 0 to 1 Hz, and the coupled
model had a higher coherence in this range for some cells. This increase was particularly strong for
our example cell (Fig. 14E). Thus network interactions through the spike history terms of neighboring
cells improve the ability the predict the spike trains for some cells in this dataset.
Further network GLM methods
A priori, the coupling terms of the network GLM cannot be interpreted as representing direct or
anatomical connectivity. Rather, they are best understood as representing functional interactions between the neurons modeled. Such measures of connectivity can still provide insight into anatomical
connections in small networks (Gerhard et al., 2013) and population dynamics and encoding in large
systems (Stevenson et al., 2012; Chen et al., 2009; Takahashi et al., 2012). In these cases it is useful
to quantify the significance of a coupling term between neurons. A common approach is to employ
an analysis based on Granger causality (Barnett and Seth, 2014). Granger causality is designed to determine when one variable is useful in predicting another (Granger, 1969): if a causal relationship
between two processes exists, then the past values of one process should help to predict the future
values of the other process. One can apply a variant of Granger causality to the network GLM (Eq.
54) to test the connection from neuron l to neuron i (Kim et al., 2011). More generally, the issue of disambiguating direct interactions from interactions that occur through unobserved, or latent, variables
is an important one which is receiving increasing attention. (Pfau et al., 2013; Vidne et al., 2012; Okun
et al., 2015).
We have presented and analyzed a class of methods which summarize the response properties of
neurons in terms of one or a few feature vectors and an associated nonlinear input/output function
(Fig. 15). These methods provide a principled means to describe neuronal responses. They are a
clear improvement over qualitatively described receptive fields, particularly through the inclusion
of time dependence that is often suppressed in "classic" receptive field descriptions. However, the
approaches we discussed are still phenomenological and it is fair to ask what has been gained.
First, these methods provide a largely automatic and objective means to determine neuronal feature
vectors, allowing one to determine how responses "tile" stimulus space. Second, the models are predictive and can be applied to novel stimuli, both as a crucial test of the reliability of the fit of the
model as well as a means to estimate the fraction of the cell’s response that is modeled by one or
a few features. Further, the ability to predict spikes from stimuli will likely play a critical role in
neuroprosthetic devices to restore sensation, such as artificial cochleas (Brown and Balkany, 2007),
retinas (Trenholm and Roska, 2014; Nirenberg and Pandarinath, 2012), semi-circular canals (Merfeld
and Lewis, 2012) and even artificial proprioception (Tabot et al., 2013). Third, the general scheme for
all methods can be mapped to feedforward circuitry (Fig. 1A), with the addition of lateral connections
between neurons for the network GLM. These circuits may be implemented in terms of Perceptron
models. The monotonic nonlinearities found for the STA models resemble those found with spiking
neurons (Connors et al., 1982) and the imposed logistic nonlinearity with the MNE model allows for
Figure 15: Synopsis of the methods discussed in this primer.
high-order features to be directly implemented with biological neurons as well. The parabolic nonlinearities found for some features with the STC model do not have a direct interpretation, yet may be
formed by combining pairs of responses. For example, for complex cells in V1 visual cortex, features
often appear as pairs that can be combined quadratically (Rust et al., 2005).
Model assessment
Generally, one would like to measure neural responses to repeated trials, allowing one to estimate the
intrinsic variability in the responses and thus bound the expected precision of the model predictions.
This results in a observed variance that is a continuous function of time and could be compared to a
"model", in this case the observed mean rate; these values, of course, will depend on the smoothing
scale applied to the data. Within early stages of the visual pathway, modeling based on repetitive
trials capture 80 to 90% of the variance in macaque retina (Pillow et al., 2008), 80 to 90% of the variance
in cat primary visual cortex (Touryan et al., 2002), and 94% of the variance in macaque primary visual
cortex (Rust et al., 2005).
Here we dealt with the more general case of data that did not have repetitions. We therefore chose
to evaluate the accuracy of each model’s prediction in two different ways: the log-likelihood (Eq. 47)
and the coherence (Eq. 49) between the test spike train and the prediction. The log-likelihood, applied
to test data (Figs. 10C and 11C), is a natural choice as it is used as an objective function when fitting
the MNE models and the GLM and can be used with spike trains as well as spike rates. However,
we observed that it is not always satisfactory. It can, in some instances, be a shallow function that
does not clearly discriminate between predictions from models that are rather distinct (Figs. 10C and
11C). Also, as a scalar quantity, the log-likelihood provides no insight into what aspect of the cell’s
response is or is not captured by the model.
Calculating the coherence between the responses and the predictions offers a complementary approach (Figs. 10E and 11E). Coherence has not been used directly as an objective function for model
fitting. In contrast to the log-likelihood, it gives a normalized measure of the portion of the power of
the neuronal responses at a given frequency that is explained by each model. It also indicates timing
errors via phase shifts (insert in Fig. 10A and Figs. 10E and 11E). Therefore, it provides information
about what aspects of the spike rate are captured by the model and may provide insight into how
the model can be improved. Lastly, the normalization allows one to compare results between cells in
addition to comparing models of the same cell.
Caveats on whitening
The pre-whitening procedure for the STA and STC analysis is mathematically sound for random
stimuli that have Gaussian statistics (Paninski, 2003) and a limited number of other distributions
(Samengo and Gollisch, 2013). Even when this constraint does not strictly hold, our experience (Figs.
7) suggests that, despite no convergence guarantees, a whitened STA or STC plus STA model can
give rather good predictions for responses to novel stimuli with natural statistics (Figs. 11). The prewhitening procedure, however, does not always substantially improve predictions over using the raw
stimulus. Since the latter simple approach is easier to construct and less computationally demanding
than models specifically tailored for natural scenes, it is worthwhile to construct them and test their
An intermediate case between natural scenes and Gaussian white noise is when stimuli are drawn
from a highly correlated Gaussian distribution, such that the variance along some dimensions is
much greater than along others. Here the STC method is guaranteed to converge to the correct set of
features, but the large ranges of variances may imply a slow convergence rate. This process can be
improved through a modification of the STC method (Aljadeff et al., 2013).
Adaptation and dependence on stimulus statistics
One significant issue with the fitting of LN models is that the resulting model, including feature, spike
history filter and nonlinearity, often depends on the mean, variance, and correlation structure of the
stimulus that is used to probe the system. For many sensory systems, the changes that are observed in
LN models for different stimulus ensembles (Fairhall, 2013) act to improve information transmission
through the system, i.e., account for the presence of noise (Atick, 1992), match the dynamic range of
the input/output to the range of stimuli (Brenner et al., 2000; Fairhall et al., 2001; Wark et al., 2007),
or cancel out correlations in the input to produce a predictive code (Srinivasan et al., 1982; Hosoya
et al., 2005; Sharpee et al., 2006). In some cases, the timescales under which these changes occur suggest that biophysical or circuit properties are altered through long timescale adaptation to different
stimulus conditions (Hosoya et al., 2005; Sharpee et al., 2006). However, when the stimulus ensemble
is changed abruptly, some corresponding changes in LN models follow close to instantaneously and
need not require changes in any biophysical properties of the system (Rudd and Brown, 1997; Fairhall
et al., 2001; Mease et al., 2013).
This effect can occur because different stimulus ensembles may drive the system through different
parts of its nonlinear regime, and the response behavior is only approximated through the LN model.
Thus the best reduced model describing responses for a particular stimulus ensemble will depend
on how that ensemble drives the system, even without any changes in the system itself (Gaudry
and Reinagel, 2007; Hong et al., 2008; Mease et al., 2014). In some cases these dependencies can be
predicted explicitly (Hong et al., 2008; Famulare and Fairhall, 2010) but more typically are simply
empirically observed. The development of models that incorporate these dependencies on stimulus
statistics would be of great value and would be able to generalize to a wider range of stimuli. One
might have hoped, for example, that the GLM’s dependence on the history of activity might take into
account issues like spike frequency adaptation and allow one to separate out a common stimulus
sensitivity along with a dependence on firing rate that could allow for greater generalization. However, GLMs fit for different stimulus statistics generally differ in all components (Mease et al., 2014)
and do not generalize well to different ensembles. It is likely that incorporating features or dynamics
acting over multiple timescales can provide sensitivity both to rapid fluctuations and slower-varying
statistical properties of the stimulus. For example, a promising current alternative approach is the
development of hybrid models that combine an LN model with a dynamical component modeling,
for example, activity-dependent changes in kinetic parameters (Baccus and Meister, 2002).
Population dimensionality reduction
The potential role of correlation in neuronal firing is widely recognized (Cohen and Kohn, 2011).
The network GLM is just one approach to deciphering how the activity of many neurons in a fully
connected network jointly encodes external inputs/outputs and carries out internal dynamics. More
generally, one might expect to be able to represent measured high-dimensional multi-neuronal activity in terms of a smaller number of spatially distributed activation patterns. One approach toward this goal is to project activity patterns into a low-dimensional space and reveal the dynamics
occurring during computation (Cunningham and Yu, 2014). A natural starting point to determine
this space is to apply PCA to the instantaneous firing patterns (Mazor and Laurent, 2005; Churchland et al., 2010b,a). The method of Gaussian process factor analysis (Yu et al., 2009) further adds
some assumptions on the smoothness of the temporal evolution of firing patterns. Given these reduced descriptions of neural activity, typically one then "reverse correlates" on a generally arbitrary
or experimenter-defined low-dimensional description of the stimulus or behavior to sort and analyze
these patterns according to their external correlates (Churchland et al., 2010b,a). The second strategy
aims to systematically model the multineuronal response distribution, P (r1 , r2 , . . . , rn ), and its correlations using maximum entropy approaches (Schneidman et al., 2006; Ganmor et al., 2011; Fairhall
et al., 2012). In this case, one assumes, similar to the MNE approach (Eq. 31), that responses from
multiple neurons are jointly distributed according to the maximum entropy distribution that satisfies
constraints given by measurements of response correlations. Yet these methods do not yet provide a
full input/output mapping. Hybrid maximum entropy models, where the first moment of the distribution depends on the response and the second on network interactions, have also been proposed
(Granot-Atedgi et al., 2013).
Non-spiking data
Our focus has been confined to the relation of spikes, or more generally point processes, to the ongoing stimulus. Yet many neurological events are smoothly varying. At the macroscopic level this
includes the flow of current in the extracellular space that is measured by field electrodes or by magnetoencephelography, while at the microscopic level this includes second messenger activation, such
as the intracellular concentration of calcium or cyclic AMP. Measurements of intracellular calcium
are of particular importance as the technology to measure such signals with a high signal-to-noise
ratio is pervasive throughout neuroscience (Svoboda et al., 1997; Grienberger and Konnerth, 2012)
and the onset of the calcium signal can often be taken as a surrogate for an electrical spike (Lütcke
et al., 2013). The methods we presented to compute the STA, STC, and MNE features can readily be
used to compute feature vectors by replacing the variable for the number of spikes per sample time,
ns (t), by the intensity of the sampled signal (Ramirez et al., 2014). The challenge arises in computing
the nonlinearity associated with the STA and STC methods. For the case of spiking, the procedures of
spike detection and sorting provide a threshold between no spikes and one or more spikes, although
this discrimination process has an associated uncertainty (Lewicki, 1998; Hill et al., 2011b). For an
analog process like a change in intracellular calcium, one could simply regard the signal as a continuous signal and choose an appropriate noise model, e.g., Gaussian. Alternatively one can represent it
as a point process by selecting a threshold level of detectability. Detection of calcium events, as well
as their mapping to spikes, is a topic of ongoing research (Vogelstein et al., 2010).
We have presented, evaluated, and provided code for a number of methods, all established if not
quite mainstream, that answer a simple question: “What makes a neuron fire?”. We, along with a
plethora of other practitioners, believe that these methods provide a convenient means to obtain insight into the responses of neurons typically obtained in a recording session. In so far as this has
proven useful for measurements of single cells, the development of efficient and effective descriptive
models becomes a necessity for simultaneous measurements across populations of neurons; thousands if not millions of neurons at once if the hopes for new electrical and optical probes bear out
(Alivisatos et al., 2012). As yet, serious limitations apply. When real data does not satisfy certain constraints, such as Gaussian distributed stimulus inputs and monotonic input/output functions, that
guarantee convergence for simpler methods, heuristics need to be used to keep fitting procedures
from becoming numerically unstable. Even in the retina, LN models often fail to generalize to natural
stimuli and do not capture more complex responses such as looming. Responses in neurons that are
far downstream from the sensory periphery often have invariances that are very difficult to capture
by these methods. In primary visual cortex, LN models have added substantially to the richness of
previous descriptions yet leave much unexplained (Olshausen and Field, 2005). Further, real world
stimuli may contain critical yet rare stimulus events (Khouri and Nelken, 2015), at least rare on the
time-scale of typical physiological recordings. By their very nature, rare stimuli will not be captured
by low-order statistics no matter how hard they drive a cell to spike. Despite these caveats, we are
optimistic that continuing advances that extend these approaches will likely to become part of the
standard canon of electrophysiology as recording techniques progress. But the application of spiking
models is still an art form and, like much of electrophysiology (Kleinfeld and Griesbeck, 2005), is not
yet an industrial process. Fortitudine vincimus.
All calculations were performed using Matlab (The MathWorks, MA) running on a single processor
computer. Annotated code is supplied that was used for all calculations and to generate the figures in
the manuscript, along with all datasets, is supplied (download file from http://neurophysics.ucsd.edu/software.php):
fifty three salamander retina sets, seven rat thalamic sets, and nine monkey cortex sets. We recommend that interested individuals first repeat the calculations that we used to generate the figures for
this paper, then modify the code to analyze their own data.
The following commercial software from The MathWorks (www.mathworks.com) is required: Matlab, the Image Processing Toolbox, the Optimization Toolbox, the Signal Processing Toolbox, the
Statistics Toolbox, and the Symbolic Math Toolbox. In addition, the following free software must be
downloaded: Daniel Hill’s code for the Hilbert transform (neurophysics.ucsd.edu/software.php),
Partha Mitra’s Chronux Toolbox (www.chronux.org), Jonathan Pillow’s Generalized Linear Model
(GLM) implementation for spike trains (http://pillowlab.princeton.edu/code_GLM.html), Mark
Schmidt’s L1-norm function L1GeneralGroup_Auxiliary.m downloaded at https://www.cs.ubc.
ca/~schmidtm/Software/thesis.html, and the multi-dimensional histogram function histcn.m downloaded at http://www.mathworks.com/matlabcentral/fileexchange/23897-n-dimensional-histogram/
This Primer evolved from material presented at the “Methods in Computational Neuroscience” and “Neuroinformatics” summer schools at the Marine Biological Laboratory and the program on “Emerging Techniques in
Neuroscience” at the Kavli Institute for Theoretical Physics. We thank Emery N. Brown, Kenneth Latimer, Partha
P. Mitra, Jeffrey D. Moore, Rich Pang, Jonathan W. Pillow, Ryan J. Rowekamp and Tatyana O. Sharpee for valuable discussions, Michael J. Berry II and Ronen Segev for making their retina data available, Ben Engelhard and
Eilon Vaadia for making their motor cortex data available, and Joel Kaardal for help with the computer code.
This effort was supported by grants from the NIH (NS058668 and NS090595 to DK), the NSF (0928251 and EEC1028725 to ALF and EAGER 2144GA to DK), the Allen Family Foundation (ALF), and the US-Israel Binational
Science Foundation (855DBA to DK).
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