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J Neurophysiol. Author manuscript; available in PMC 2010 May 27.

Published in final edited form as:

Published online 2008 March 19. doi: 10.1152/jn.01397.2007

PMCID: PMC2877595

NIHMSID: NIHMS47637

Correspondence to: Tatyana Sharpee, Tel: 858-453-4100 x 1857, Fax: 858-455-7538, Email: ude.klas@eeprahs

The publisher's final edited version of this article is available at J Neurophysiol

See other articles in PMC that cite the published article.

Understanding neural responses with natural stimuli has increasingly become an essential part of characterizing neural coding. Neural responses are commonly characterized by a linear-nonlinear (LN) model, in which the output of a linear filter applied to the stimulus is transformed by a static nonlinearity to determine neural response. To estimate the linear filter in the LN model, studies of responses to natural stimuli commonly use methods that are unbiased only for a linear model (in which there is no static nonlinearity): spike-triggered averages with correction for stimulus power spectrum, with or without regularization. While these methods work well for artificial stimuli, such as Gaussian white noise, we show here that they estimate neural filters of LN models from responses to natural stimuli much more poorly. We studied simple cells in cat primary visual cortex. We demonstrate that the filters computed by directly taking the nonlinearity into account have better predictive power and depend less on the stimulus than those computed under the linear model. With noise stimuli, filters computed using the linear and LN models were similar, as predicted theoretically. With natural stimuli, filters of the two models can differ profoundly. Noise and natural stimulus filters differed significantly in spatial properties, but these differences were exaggerated when filters were computed using the linear rather that the LN model. While regularization of filters computed under the linear model improved their predictive power, it also led to systematic distortions of their spatial frequency profiles, especially at low spatial and temporal frequencies.

Over the course of nearly 50 years neurons in the primary visual cortex have been studied with a variety of stimuli, ranging from the classic studies using edges, bars and moving gratings, to more recent studies with random inputs and stimuli derived from the natural environment, as reviewed by (Felsen and Dan, 2005; Rust and Movshon, 2005). It is becoming increasingly important to develop unified models for neural responses to stimuli with a wide range of statistical properties, ideally extending to the fully natural case.

To accomplish this, it is important to be able to derive a neural response model from responses to natural stimuli. One cannot simply derive a response model from responses to a simpler ensemble, such as noise, and then extrapolate to the natural case. Typically there are significant differences between response models derived for a given cell from responses to different stimulus ensembles. In particular, neural response models derived from one stimulus ensemble often provide better predictions for neural responses to novel stimuli of the same type than to novel stimuli with different statistical properties (David et al., 2004; Sharpee et al., 2006; Woolley et al., 2006). The two general reasons for this are that neural responses are nonlinear and that they are adaptive. Even if neural responses were stationary and non-adaptive, it could be difficult to build a single model that adequately describes responses to different stimulus ensembles that evoke different regimes of neural responses. In addition, neurons appear capable of adapting to many statistical properties of the stimulus ensemble, meaning that response properties can change over time of exposure to a single ensemble (e.g., (Webster et al., 2002; Sharpee et al., 2006; Webster et al., 2006; Lesica et al., 2007)),

A common model for characterizing neural responses to a given ensemble is the linear-nonlinear (LN) model. In this model, a neuron’s response is determined in two steps. First, the stimulus is linearly weighted with the neuron’s spatiotemporal filter and this linearly weighted stimulus is summed to produce a number, the filter output‥ The firing rate is then given by an arbitrary static nonlinear function (such as a sigmoidal function) of the filter output, which we can call the neuron’s input-output function. Here we compare two commonly used methods for estimating the spatiotemporal filter when fitting the LN model to a neuron’s response, which we refer to as the linear and LN methods. The linear methods give unbiased answers (meaning answers that are correct in the limit of infinite data) for an arbitrary stimulus ensemble only for a neuron whose responses are determined by a linear model, that is an LN model with a linear input-output function, cf. Figure 1. For an LN model with a nonlinear input-output function, the linear methods give an unbiased estimate of the filter only if the stimulus ensemble is uncorrelated or correlated Gaussian noise (Bussgang, 1952; de Boer and Kuyper, 1968; Ringach et al., 1997; Chichilnisky, 2001; Agüera y Arcas et al., 2003; Paninski, 2003a; Sharpee et al., 2004; Bialek and de Ruyter van Steveninck, 2005; Schwartz et al., 2006) LN methods give unbiased answers for arbitrary stimulus ensembles for neurons whose responses are determined by an arbitrary LN model. Natural stimuli are strongly correlated and non-Gaussian (Field, 1987; Ruderman and Bialek, 1994; Simoncelli and Olshausen, 2001). Thus, the static nonlinearity causes bias in the spatiotemporal filters estimated from natural stimuli by the linear methods, but not by the LN methods. The more general conditions under which each method gives biased or unbiased answers are well known (Paninski, 2003a; Sharpee et al., 2004).

The linear methods are often used to estimate filters for responses to natural stimuli, in spite of the bias they might have with such stimuli. Presumably this is due to the computational simplicity of the linear methods in comparison to the LN methods, and the belief that the bias is not too great a problem. Errors in estimation have at least two origins: systematic bias of the method used, which is independent of data size, and sampling error, meaning error in the estimation due to the finite amount of data. For realistic data sizes, it might be that the sampling error overwhelms the bias, so that the bias would be an insignificant source of error. Thus, it is important to compare, with realistic amounts of data, the performance of the linear and the LN methods of spatiotemporal filter estimation, as we do here.

Here we find, using data recorded from simple cells in cat primary visual cortex (V1), that the spatiotemporal filter computed for natural stimuli in the LN model is, in general, significantly different from that computed in the linear model. We show that, while spatiotemporal filters computed in the LN model change with stimulus statistics, these changes are exaggerated when the spatiotemporal filters are computed in the purely linear model. Here, we have compared neural filters derived from responses to two stimulus sets, white noise and natural stimuli. Each stimulus set had the same mean luminance and contrast, but the two stimulus sets had different power spectra and higher-order statistical correlations. We find that neural filters computed in the LN model provide a consistently better description of the responses of simple cells in the primary visual cortex than those computed in the purely linear model. This was true for predicting responses to novel stimuli both within the same stimulus set and across different stimulus sets.

All experimental recordings were conducted under a protocol approved by the University of California, San Francisco on Animal Research with procedures previously described (Emondi et al., 2004; Sharpee et al., 2004; Sharpee et al., 2006). Briefly, spike trains were recorded using tetrode electrodes from the primary visual cortex of anesthetized adult cats and manually sorted off-line. After manually estimating the size and position of the receptive fields, neurons were probed with full-field moving periodic patterns (gratings). In later off-line analysis, cells were selected as simple if, under stimulation by a moving sinusoidal grating with optimal parameters, the ratio of their response modulation (F1, i.e. amplitude of the Fourier transform of the response at the temporal frequency of the grating) to the mean response (F0) was larger than one (Skottun et al., 1991). The rest of the protocol typically consisted of presenting an interlaced sequence of 3 different noise input ensembles of identical statistical properties (only seed values for the random number generator differed among the three input ensembles), and 3 different natural input ensembles. Visual stimulus ensembles of white noise and natural scenes were each 546 sec long. The interval between presentations varied in duration as necessary to provide adequate animal care. All natural input ensembles were recorded in a wooded environment with a hand-held digital video camera in similar conditions on the same day (Sharpee et al., 2006). The noise ensembles were white overall, but each particular frame was limited to spatial frequencies within a certain band. There were 8 spatial frequency bands total. The intent of such design was to increase the number of elicited spikes. The mean luminance and contrast of the noise ensembles were adjusted to match those of the natural ensembles. Contrast was defined as the standard deviation of luminance values relatively to the mean. Both noise and natural inputs were shown at 128×128 pixel resolution, with angular resolution of approximately 0.12 degrees/pixel.

Figure 1 compares the structure of the linear and LN model; each has a linear filter that is convolved with the stimulus (illustrated are purely spatial filters, although spatiotemporal filters will be analyzed for real neurons). The difference between models is that in the LN model the result of this convolution between stimulus and the linear filter is transformed by a nonlinear function into spike probability, whereas in the linear model only the slope of the transformation can be adjusted.

To calculate spatiotemporal filters of real neurons, stimuli were down-sampled from 128×128 pixels (angular resolution 0.12 degrees/pixel) to 32×32 pixels (angular resolution 0.48 degrees/pixel). All spatiotemporal receptive field analysis was carried out at this resolution. To find the center of the receptive field, we computed the spike-triggered average stimulus (STA) for noise and natural ensembles. To make analysis computationally feasible and to minimize effects due to undersampling [we strove to have the number of spikes greater than the dimensionality of the receptive fields (Paninski, 2003a; Sharpee et al., 2003; Sharpee et al., 2004)], a patch of 16×16 pixels (angular resolution of 0.48 degrees/pixel) was selected around the maximum in the STA for each ensemble. All of the filter computations were confined to this patch, which was identical for noise and natural stimuli on a cell-by-cell basis. In all cases, subsequent analysis of the filter verified that it was fully contained within the selected patch.

In the case of white noise stimuli, neural spatiotemporal filters were computed as STAs (de Boer and Kuyper, 1968; Rieke et al., 1997; Chichilnisky, 2001) and as maximally informative dimensions (MIDs) (Sharpee et al., 2004; Sharpee et al., 2006). The STA represents the stimulus dimension along which the mean of the stimuli associated with spikes differs from the mean of all stimuli. The MID represents the stimulus dimension that carries the maximal amount of information about the arrival times of individual spikes. These two analyses should give the same answer in the case of white Gaussian noise or more generally of uncorrelated stimuli (by which we mean that each pixel’s luminance at each time is drawn independently from a single luminance distribution; this is equivalent to the ensemble with mean luminance set to zero being spherically symmetric spherically symmetric (Chichilnisky, 2001)), provided only one stimulus feature is relevant for determining spike probability. This is because in this case the spatiotemporal filter of the linear model coincides with that of the LN model with one relevant dimension (Bussgang, 1952; de Boer and Kuyper, 1968; Ringach et al., 1997; Chichilnisky, 2001; Agüera y Arcas et al., 2003; Paninski, 2003a; Sharpee et al., 2004; Bialek and de Ruyter van Steveninck, 2005). Because more than one stimulus dimension may be relevant for spikes of real neurons (de Ruyter van Steveninck and Bialek, 1988; Brenner et al., 2000a; Touryan et al., 2002; Agüera y Arcas and Fairhall, 2003; Bialek and de Ruyter van Steveninck, 2005; Felsen et al., 2005b; Rust et al., 2005; Slee et al., 2005; Touryan et al., 2005; Fairhall et al., 2006), the relevant dimensions may combine differently to form the dimension along which the mean changes most and that which is most informative.

In our case, the noise stimulus ensemble was composed of eight bandlimited Gaussian ensembles that together produced white noise (within each spatial frequency band, signals were Gaussian and white). Therefore there were small non-Gaussian effects and deviations from spherical symmetry, though these deviations were unlikely to affect the argument for the validity of the STA. For convenience, and in anticipation of its role for a natural ensemble, we refer to the STA as the spatiotemporal filter of the linear model for our noise inputs. The MID gives the spatiotemporal filter of the LN model regardless of the statistical properties of the stimulus ensemble (Paninski, 2003a; Sharpee et al., 2003; Sharpee et al., 2004; Sharpee, 2007). As we show below, the differences between STA and MID filters computed for noise inputs were miniscule or absent.

In the case of natural stimuli, to compute spatiotemporal filters of the linear or LN model it is necessary to account for stimulus correlations, both pair-wise and higher order. In the LN model, this is automatically done by the MID method. In the linear model, pair-wise stimulus correlations can be accounted for by multiplying the STA by the inverse of the stimulus covariance matrix (Rieke et al., 1997; Theunissen et al., 2000; Theunissen et al., 2001; Ringach et al., 2002; Smyth et al., 2003; David et al., 2004; Machens et al., 2004; Sharpee et al., 2004; Felsen et al., 2005b; Touryan et al., 2005; Woolley et al., 2006). We will follow the common convention and refer to the resulting filter as the decorrelated STA (dSTA).

The dSTA, and thus the linear model, in principle is the correct filter for the LN model with an arbitrary nonlinearity if the stimulus ensemble is a multidimensional Gaussian, but it is biased for correlated non-Gaussian ensembles (Bussgang, 1952; de Boer and Kuyper, 1968; Ringach et al., 1997; Chichilnisky, 2001; Agüera y Arcas et al., 2003; Paninski, 2003a; Sharpee et al., 2004; Bialek and de Ruyter van Steveninck, 2005; Schwartz et al., 2006; Sharpee, 2007; Ahrens et al., 2008; Christianson et al., 2008). By a multidimensional Gaussian, we mean that the probability of a stimulus **s** (a vector of pixel luminances minus the mean luminance) is given by $P(\mathbf{s})\propto \text{exp}(-\frac{1}{2}{\mathbf{s}}^{T}{\mathbf{C}}^{-1}\mathbf{s}),$ where C is the matrix of pixel-pixel covariance. For a multidimensional Gaussian, any one-dimensional slice through the distribution also has a Gaussian distribution, including, in particular, the distribution of luminances for any single pixel. Thus, measuring the degree to which such a one-dimensional slice or the distribution across all pixels deviates from Gaussian is one measure of the degree to which the overall distribution deviates from Gaussian. We use this fact in the Discussion where we use the kurtosis of the distribution of luminances at individual pixels across time as one measure of the deviation of a natural scenes distribution from a Gaussian distribution.

Because the procedure of inverting the stimulus covariance matrix tends to amplify sampling noise at the relatively underrepresented spatial and temporal frequencies, dSTA may not work well in practice. To correct this, several regularization strategies have been proposed (Theunissen et al., 2000; Theunissen et al., 2001; Smyth et al., 2003; David et al., 2004; Machens et al., 2004; Touryan et al., 2005; Woolley et al., 2006). To produce the regularized decorrelated STA filter (RdSTA) we first compute a pseudo-inverse of the covariance matrix. Data were separated into three parts, (1/8, 1/8, 3/4). One of the two smaller datasets was set aside for later use in evaluating the predictive power of the model. The largest data set was used to compute the STA. To form the pseudo-inverse for a given cutoff value λ, we diagonalized the covariance matrix, finding its eigenvectors and the corresponding eigenvalues, and then computed the pseudo-inverse based on all eigenvectors that had eigenvalues larger than λ (Press et al., 1992; Woolley et al., 2006). The candidate spatiotemporal filter of the linear model was then obtained by applying the pseudo-inverse to the STA, and its performance was evaluated on the remaining small dataset (1/8 of the whole dataset) by the amount of mutual information it provided (Adelman et al., 2003; Agüera y Arcas et al., 2003; Sharpee et al., 2004). By trying all of the possible cutoff values λ, we selected that value for the cutoff that resulted in the best prediction. The corresponding filter was the RdSTA. Simulations on model cells (Sharpee, 2007) showed that increasing the validation set size to 25% of the available data resulted in similar relative performance of the different methods (dSTA, RdSTA, MID) as we report here for cortical cells. Using percentage of explained variance instead of the mutual information also did not change performance of these methods on model neurons.

We considered both the dSTA and RdSTA as two alternative methods for estimating spatiotemporal filters of the linear model in the case of natural stimuli. Spatiotemporal filters of the LN model probed with natural stimuli were computed as MIDs (Sharpee et al., 2004; Sharpee et al., 2006). We note that computing STA, dSTA, and MID filters required setting aside only one validation dataset, while computing the RdSTA required, in some calculations, setting aside two validation datasets: one of the datasets was used in selecting the optimal cutoff on eigenvalues of the covariance matrix that contributed to its pseudo-inverse, and the other to later evaluate the predictive power of the linear model based on the RdSTA filters. Computing the regularization without separate datasets for cutoff selection and validation would artifactually enhance the apparent predictive power.

To be able to perform statistical analysis of the properties of neural spatiotemporal filters, we computed 8 jackknife estimates for each of the methods and stimulus ensembles (Efron, 1998). To obtain a jackknife estimate, the data was split in the same manner as described above for the validation purposes: a jackknife estimate was made using 7/8 of the data, and the predictive power of the estimate was assessed using the remaining 1/8 of the data. The 8 jackknife estimates were obtained by dividing the data into 8 segments and using each 1/8-long segment as the omitted/validation segment for one estimate. As described above for the case RdSTA filter estimates, each of the 8 jackknife estimates of RdSTA filter was obtained by further separating the training part of the dataset (7/8 of the data) into 3/4 and 1/8, of which the latter small data subset was used to select the optimal cutoff value. The remaining 1/8 of the data was used to estimate the percentage of information or variance explained by the given jackknife estimate (Fig. 7). Comparisons between filter estimates (Fig. 2–Fig 6) that did not involve computing predictive power on a novel dataset were based on the RdSTA filters computed using 7/8 of the overall data set to find the STA and 1/8 to find the optimal cutoff λ.

To find the preferred orientation and spatial frequency we performed a one-dimensional Fourier transform in time, selecting a 2Hz temporal frequency to match the temporal frequency used in stimulation with moving gratings. Then, the two-dimensional spatial Fourier transform was computed and the position of the peak was used as an initial guess for the preferred spatial frequency and orientation of the receptive field. Starting with this initial guess, the fitting of Gabor functions (Ringach, 2002) to the spatiotemporal filter was always successful. The parameters of the best fitting Gabor function were used as the estimate of the preferred orientation and spatial frequency. Such a procedure was repeated for each of the jackknife estimates, so that the mean and standard deviation were obtained [according to jackknife Eq. 11.5 in (Efron, 1998)] for both preferred orientation and spatial frequency values, as shown in Figure 3 and Figure 4. Comparisons between parameters of the neural filters derived from different stimuli and models were evaluated using linear fits that took into account error-bars in both x- and y-axes, using Mathematica software by Wolfram Research. Point-by-point comparison between spatiotemporal filters from different models and stimulus ensembles, cf. Figure 6, was also done based on 8 jackknife estimates.

Spatial frequency profiles shown in Figure 5 were obtained by taking the Fourier transform in time and, with zero-padding to 32×32, in space. Linear interpolation between pixels of the 2D transform was used to derive one-dimensional profiles along the preferred orientation of each cell. Before averaging across cells, the frequency profiles of individual cells were normalized to unit length (sum of squares equal to 1) across all spatial and temporal frequencies.

We used mutual information between the stimulus convolved with a particular filter and the firing rate as a measure of the filter’s predictive power (Adelman et al., 2003; Agüera y Arcas et al., 2003; Sharpee et al., 2004; Fairhall et al., 2006; Sharpee et al., 2006). Specifically, mutual information accounted for by a spatiotemporal filter is computed as:

$$I(\mathbf{L})={\displaystyle \int dx{P}_{\mathbf{L}}(x|\text{spike}){\text{log}}_{2}\left[\frac{{P}_{\mathbf{L}}(x|\text{spike})}{{P}_{\mathbf{L}}(x)}\right]}.$$

(1)

Here, *P*_{L} (*x*) is the probability distribution of the projections × onto the filter **L** of all of the presented stimuli. Similarly, *P*_{L}(*x*|spike) is the probability distribution of projections onto the filter **L** of all stimuli that lead to a spike. Information along any stimulus dimension, including the relevant spatiotemporal filter, may not exceed the overall information carried in the times of occurrence of single spikes. This overall information can be evaluated as (Brenner et al., 2000b)

$${I}_{\mathit{\text{spike}}}=\frac{1}{T}{\displaystyle \int dt}\frac{r(t)}{\overline{r}}{\text{log}}_{2}\frac{r(t)}{\overline{r}},$$

(2)

where *r(t)* is the firing rate over multiple repetitions of a single stimulus segment that is characteristic of the stimulus ensemble of interest, and is the mean firing rate. The ratio *I( L)/I_{spike}* can be used to measure predictive power. Neural responses to 50–150 repetitions of an approximately 11s-long segment of the natural or noise ensemble were used to compute

In addition to mutual information, we also computed variance accounted for by a given spatiotemporal filter together with the nonlinear transformation from filter outputs to spike probability. This is done by finding the best nonlinearity for a given filter and the recorded sequence of spikes and then computing the predicted amount of variation based on the reconstructed LN model. The two steps can be combined into one equation (Paninski, 2003a; Sharpee, 2007), which gives the predicted variance normalized by ${\overline{r}}^{2}$:

$$V(\mathbf{L})={\displaystyle \int dx{P}_{\mathbf{L}}(x){\left[\frac{{P}_{\mathbf{L}}(x|\text{spike})}{{P}_{\mathbf{L}}(x)}\right]}^{2}-1}$$

(3)

This equation is similar to the above expression (1) for the mutual information and relies on the same probability distributions. Variance accounted by a given LN model cannot be larger than the overall variance in the firing rate:

$${V}_{\text{max}}=\frac{1}{T}{\displaystyle \int dt}\phantom{\rule{thinmathspace}{0ex}}{\left[\frac{r(t)}{\overline{r}}\right]}^{2}-1.$$

(4)

As was pointed out by (Sahani and Linden, 2003; Machens et al., 2004), both the variance in the firing rate and variance accounted by a given LN model have to be corrected for the positive bias due to finite size of the dataset in order to determine the amount of explainable variance. Procedures for correcting for this bias are described next.

To avoid overestimation in predictive power due to overfitting, mutual information and explained variance were computed using the remaining 1/8 of the dataset not used to derive the spatiotemporal filters themselves [this dataset was also not used to select the optimal cut-off for RdSTA filters, as described above]. Mutual information values are positively biased (Treves and Panzeri, 1995; Strong et al., 1998; Brenner et al., 2000b; Paninski, 2003b). Similar effects happen for variance (Sahani and Linden, 2003; Machens et al., 2004). To correct for this bias, we extrapolated the relationship between mutual information (variance) and the inverse of the dataset size to the infinite data limit using linear extrapolation based on 80%, 85%, 90%, 95%, and 100% of the data from the test set. This procedure was carried out for each jackknife estimate, model and type of stimulus, as well for the total value of information *I _{spike}* between unfiltered stimuli and spikes.

We note that the measures of predictive power we are using, the mutual information between filtered stimuli and spikes and the variance in the firing rate by the LN model based on a given spatiotemporal filter, reflect the predictive power based on the best possible nonlinear transformation between filtered stimuli and the spike probability. In other words, the percentage of the information (variance) explained quantifies the best predictive power achievable by a given spatiotemporal filter and arbitrary nonlinearities. Thus, although an LN model is more powerful than a linear model by virtue of its nonlinear input-output function, this is not the cause of lower predictive power of the spatiotemporal filters computed in the linear model. Instead, our method assays how accurate a filter a given model (linear or LN) can produce, with an understanding that the predictive power will be compared taking nonlinear gain functions into account even for spatiotemporal filters computed using the linear model.

We computed the spatiotemporal filters of simple cells probed with natural and noise stimuli according to the assumptions of the linear and LN models. Our goal was to compare how the spatiotemporal filters computed using the linear and LN model changed with the stimulus ensemble. The analysis is based on 40 simple cells in the primary visual cortex recorded in four animals. Spatiotemporal filters of the linear model were estimated as the spike-triggered average stimulus (STA) in the case of white noise stimuli, and as the decorrelated STA (dSTA) or its regularized version (RdSTA) for natural stimuli (see Materials and Methods). Spatiotemporal filters of the LN model were estimated as the most informative dimension (MID). In Figure 2, we show spatiotemporal filters computed according to the linear and LN model for six example simple cells. In agreement with previous findings (Smyth et al., 2003; David et al., 2004; Felsen et al., 2005b; Sharpee et al., 2006), we observed that the various filter estimates were qualitatively similar to each other, even when computed from different stimulus ensembles. This was evident in the overall spatial extent of the filters and in the variation of their peak amplitudes in time. For each spatiotemporal filter, we also show the best nonlinearity that relates stimuli convolved with the filter to the neural firing rate, which is given by associating each filter output value with the mean evoked firing rate averaged over all stimuli having that filter output value.

To compare the spatiotemporal filters quantitatively, we begin by examining preferred orientation values associated with different filters, cf. Figure 3. We found no significant differences in preferred orientation between the STA and MID filters for white noise stimuli (R^{2}=0.96). This is to be expected because for white Gaussian inputs, the STA provides the filter of both linear and the LN model and non-Gaussian effects in the white noise stimulus ensemble were small. The STA and MID filters should therefore coincide, unless multiple relevant dimensions are present and contribute differently to these filters (see Materials and Methods). More importantly, we found no significant differences between the preferred orientations of MID filters obtained from neural responses to natural stimuli and those of either the MID filter (R^{2}=0.92; p=0.46, paired Wilcoxon test) or the STA filter (R^{2}=0.90; p=0.77, paired Wilcoxon test) obtained from neural responses to the noise ensemble. The preferred orientations of filters computed as the dSTA for natural stimuli were much less correlated with, and differed significantly (p<0.01, paired Wilcoxon test) from, those derived from noise stimuli using either the STA or the MID filters for noise stimuli, with R^{2}=0.18 and 0.20, respectively. Regularizing the linear filter for natural stimuli (RdSTA) did not produce much improvement, resulting in correlations R^{2}=0.29 and 0.28 with the white noise STA or MID filters, respectively. The differences in orientation values derived from the rdSTA and either the white noise STA or MID remained significant (p<0.01, paired Wilcoxon test). Thus, the filters produced by the LN model, but not the linear model, from neural responses to natural stimuli produce similar preferred orientations to filters produced by either model from neural responses to noise stimuli.

Similar results are found when comparing preferred orientations computed from neural filters to those determined by neural responses to moving square gratings (Figure 3). The filters produced by the LN model in response to natural stimuli and those produced by either model in response to noise stimuli showed no significant difference in preferred orientation from the orientations determined from studies with gratings, in agreement with previous studies (Smyth et al., 2003; Usrey et al., 2003). However, the correlation values are smaller than for comparisons between different filters (noise STA: R^{2}=0.29, p=0.3, paired Wilcoxon test; noise MID: R^{2}=0.24, p=0.3, paired Wilcoxon test, not shown in Figure 3; natural MID, R^{2}=0.30, p=0.12, paired Wilcoxon test). We also note that while estimates of the preferred orientation from gratings and the noise STA agree well for 90% of the cells, the 4 cells that exhibited a large disparity between the two estimates had either a firing rate or a preferred spatial frequency within the lowest 10% of the population.

In contrast to the performance of filters derived from responses to noise stimuli or from the LN model in response to natural stimuli, the filters produced by the linear model in response to natural stimuli resulted in preferred orientations that were significantly different from those determined by responses to gratings (dSTA: R^{2}=0.19, p=0.002, paired Wilcoxon test; RdSTA: R^{2}=0.24, p=0.005, paired Wilcoxon test).

Next, we examined differences in preferred spatial frequency values, cf. Figure 4. Altogether there are six estimates of preferred spatial frequency: one value derived from neural responses to moving gratings and five values derived from 5 different filter estimates for each neuron. The five filter estimates include two for noise stimuli (STA, MID) and three for natural stimuli (dSTA, RdSTA, MID). We present our results as the upper half of a 6×6 matrix of pair-wise comparisons. Each row has the same dataset on the y-axis, and each column has the same dataset on the x-axis. Preferred spatial frequency for the two filter estimates derived from neural responses to white noise (STA and MID) were strongly correlated, with R^{2}=0.65 and the value for the best fitting slope 1.03±0.02 (sd.). Comparing filters derived from noise and natural stimuli, we found that preferred spatial frequencies of filters derived from noise inputs were slightly, but statistically significantly, higher than those of filters derived from natural inputs with the LN model. The slope of the best fitting line (taking into account error bars) was 1.20±0.05 when the noise MID filter was compared with the natural MID filter and 1.09±0.04 when the noise STA filter was compared with the natural MID filter. Measurements derived from neural responses to gratings were not significantly different from those based on the noise MID, somewhat smaller than those based on the noise STA (best fitting slope of 1.09±0.06) and larger than those based on the natural MID (best fitting slope of 0.8±0.1). .Filters derived from natural inputs under the linear model (dSTA or RdSTA) had preferred spatial frequencies that were poorly correlated with those derived from either of the two noise filters, from the MID filters for natural stimuli, or from neural responses to moving gratings (all R^{2}<0.03). We also note that error bars on the preferred spatial frequency values were substantially larger for filters derived from natural inputs under the linear model than for filters derived from natural inputs under the LN model or filters derived from noise inputs.

Having found no changes in the preferred orientation between noise and natural stimuli and some change in the preferred spatial frequency, we proceeded to examine differences in the frequency composition of the spatiotemporal filters, measured at the preferred orientation (see Methods). Previous results (Sharpee et al., 2006) showed that spatial frequency profiles can change profoundly between noise and natural inputs (when estimated as MID filters), without large changes in the preferred spatial frequency values. In Figure 5 we show the relative frequency composition, averaged across our population of cells, for the three different methods (dSTA, RdSTA, and MID) of estimating spatiotemporal filters with natural stimuli. Even though the dSTA filters are known to be subject to noise amplification, their spatial frequency profiles (shown in red) at low spatial frequency closely resemble the behavior of the MID filters (shown in blue). Some noise amplification does indeed take place at higher spatial frequencies around 1 cycle/degree, and this is more pronounced at the larger temporal frequency f=10Hz than at the low temporal frequency f=0Hz. A common strategy to deal with the problem of noise amplification at higher spatial frequency is to impose a smoothness constraint, which effectively filters out higher spatial frequencies where signal-to-noise ratio is low. The RdSTA is an example of this strategy. In agreement with previous reports (Theunissen et al., 2000; Theunissen et al., 2001; Smyth et al., 2003; David et al., 2004; Felsen et al., 2005b; Woolley et al., 2006), spatial frequency profiles of the RdSTA filters (shown in orange) were strongly biased towards low spatial frequencies, and to some extent, to low temporal frequencies.

Thus, not only do the three methods of estimating filters with natural stimuli produce spatial frequency profiles that are profoundly different, but also different implementations of the linear model (dSTA and RdSTA) profoundly differ from each other. For comparison, we replot spatial frequency profiles from the noise MID filters in grey solid line (Sharpee et al., 2006). Most notably, for zero temporal frequency, the differences in spatial frequency composition between the dSTA and the RdSTA profiles far exceeded the differences between spatial profiles computed for noise and natural stimuli using the LN model (the MIDs). At low spatial frequencies, the RdSTA shows higher amplitude spectra than both noise and natural MIDs, while the dSTA shows smaller amplitude spectra than both noise and natural MIDs (although it is close to the frequency composition of the natural MID filters). At higher spatial frequencies, the situation is the reverse. Here, for both zero and 10 Hz temporal frequencies, the RdSTA shows less high-frequency content than MID filters computed for noise and natural stimuli, while the dSTA overestimates the frequency content.

The frequency compositions of the MID filters computed for natural and noise stimuli are approximately identical at higher spatial frequencies. This can be viewed as providing additional support for the computation underlying the LN model, because, in the absence of an external smoothness constraint, as in the case of computing the RdSTA, artifacts would tend to accumulate at the higher spatial frequencies, which have much less power than low frequencies in natural stimuli and hence are relatively undersampled.

The previous section showed the much greater reliability of the MID method in comparison to the various STA methods for measuring spatial frequency selectivity from natural stimuli. Describing the relative sensitivity to different spatial frequencies is an important characterization of neural filters. However, the spatiotemporal filters may also be compared point-by-point, using correlation coefficients between pairs of filters. We will use the spatiotemporal filters computed for noise stimuli, where the different methods agree, as a reference. Upon discretization in time and two spatial coordinates, as is necessary for any practical computations, the spatiotemporal filter becomes a multidimensional vector in the stimulus space, where each pixel is a separate dimension (in our case, the dimensionality is 16×16×3, i.e. 16×16 spatial sampling and 3 time lags). Therefore, it is only natural to measure the similarity of two filters as a normalized dot product between them, that is, as a correlation coefficient (c.c.). Two identical filters have a c.c. of one. Very dissimilar filters will have c.c. of zero. While there is only one way for the c.c. between two filters to be 1, i.e. when they are identical, there are many filters that describe dimensions that are orthogonal to each other, and therefore have c.c.’s of 0. We note that the sign of the spatiotemporal filter can always be changed to the opposite if accompanied by a simultaneous inversion of the x-axis on the nonlinear gain functions (shown in Figure 2). For example, a filter with large positive peak together with increasing firing rate with increasing stimulus similarity to the filter is equivalent a contrast inverted filter having a negative peak together with firing rate that decreases with stimulus similarity. This means that the sign of the correlation between two filters is not meaningful, so the correlations can be taken to always be non-negative.

The results of such an analysis are given in Figure 6. We first compare similarity between spatiotemporal filters of the LN model computed for noise vs. natural stimuli (as MID filters) to the similarity of the filters of the linear model computed for noise stimuli (STA) vs. natural stimuli (dSTA). As can be seen in panel A, for all cells, the spatiotemporal filters of the LN model were more similar to each other across stimulus type than filters of the linear model. This was also true if filters computed for the natural stimuli in the linear model were regularized, panel B. Because non-Gaussian effects in the white noise stimulus ensemble were small, the STA for noise stimuli also approximates the filter of the LN model, and to that extent is interchangeable with the noise MID filter. In panels C and D, we use the noise MID filter instead of the noise STA filter to quantify changes in filters between noise and natural stimuli. Here the only difference between x and y axes is in the method for computing filters for natural stimuli. In panel C we show that for all cells the noise MID filter is closer to the natural MID filter than to the dSTA filter computed for natural stimuli. In panel D this comparison is carried out between the natural MID filter and the RdSTA. While there is more variability associated with the RdSTA filters, for most of the cells (37 out of 40), the noise MID filter is still closer to the natural MID filter than to the RdSTA filter computed for natural stimuli.

Similarity between spatiotemporal filters obtained with noise and natural stimuli was correlated with both similarity of the corresponding nonlinear gain functions (panel E) and the average of the predictive power of the natural MID filter in predicting responses to a novel natural stimulus segment and that of the noise MID filter in predicting responses to a novel noise stimulus segment (panel F). On average there was greater similarity between nonlinear gain functions than between the spatiotemporal filters, as all but 7 out of 40 cells are above the unity line in panel E.

To summarize this section, among the three methods of estimating spatiotemporal filters with natural stimuli, the MID method produces filters that are by far the closest to the noise filters. Thus, spatiotemporal filters of the LN model appear to be more stable under changes between white noise and natural stimuli than do receptive fields of the linear model.

A final criterion for comparing different estimates of spatiotemporal filters is their predictive value. How accurately do filters associated with linear and LN models predict the response to novel stimuli, which were not used to compute the receptive field? Note that, in all cases, we are studying the predictive power of an LN model using the given filter, with the nonlinearity chosen to be optimal for the given filter as described previously. The only difference is how the filter was computed, in particular, whether the nonlinearity was taken into account in computing the filter.

In Figure 7 we show an example of spike responses to 110 repetitions of the same segment from the natural stimulus ensemble (panel A). Panels B and C illustrate comparison between the average firing rate (black line) and its predictions according to the differently reconstructed spatiotemporal filters and nonlinearities (spatiotemporal filters and nonlinearities for this cell are shown in the middle column, bottom row of Figure 1). The data from this segment of the natural stimulus was not used in estimating either the filters or the associated nonlinearities. Comparison between actual firing rate and our predictions based on the natural MID filter and the corresponding nonlinearity (purple, shown inverted for clarity) is shown in panel B. While the reconstruction has difficulty predicting very high peaks in the firing rate, more moderate peaks up to 30 Hz, as well as the timing of the peaks, can be fairly well reconstructed. In panel C we show an expanded of view of the actual firing rates and predictions based on three different filters (MID, dSTA, and RdSTA) and their corresponding nonlinearities.

To quantify the average predictive power, we consider both percentage of explained variance and information, determined as a ratio between variance (information) accounted for by a given filter with the best possible nonlinearity and the explainable variance (information) in neural response. To determine the explainable variance , Previous studies have indicated that filters derived from natural stimuli predict responses to natural stimuli better than responses to noise (Sharpee et al., 2006; Woolley et al., 2006) or gratings (David et al., 2004); and vice versa, filters derived from noise stimuli predict responses to noise better than responses to natural stimuli (Sharpee et al., 2006; Woolley et al., 2006). Therefore we concentrate here on comparing the power of various filters estimates derived from natural stimuli (MID, dSTA, and RdSTA) for predicting responses to either natural or noise stimuli. In the case of natural stimuli, spikes to be predicted were taken from a novel part of the natural stimulus ensemble that was not used for computing the filters or nonlinearities. Predictions for noise stimuli were made for a noise stimulus segment of the same duration as the novel natural stimulus segment.

We found the expected large decrease in predictive power from natural to noise stimuli (Figure 7D). Beyond that, the MID filters computed from natural stimuli provide better predictions than either the dSTA or the RdSTA computed from natural stimuli. This was true for predictions of responses to both natural and noise stimuli, using either percent of information or variance to measure predictive power. In the top part of panel D, we show the percentage of the overall mutual information, *I _{spike}*, between the entire stimulus and spike trains that was explained by linear-nonlinear models with filters obtained by the different methods. The lower half of panel D provides analogous comparisons in terms of the percentage of variance explained. Procedures for determining explainable variance (Sahani and Linden, 2003; Machens et al., 2004) are described in Materials and Methods. Both ratios allow one to infer quantitatively how much predictions could potentially improve if the model were expanded to incorporate neuronal sensitivity to more than one stimulus dimension (de Ruyter van Steveninck and Bialek, 1988; Brenner et al., 2000a; Touryan et al., 2002; Agüera y Arcas and Fairhall, 2003; Bialek and de Ruyter van Steveninck, 2005; Felsen et al., 2005b; Rust et al., 2005; Slee et al., 2005; Touryan et al., 2005; Fairhall et al., 2006). However information may be the more appropriate measurement with natural stimuli, because of their non-Gaussian properties. We note that measurement of

On average, the MID filters derived from natural stimuli accounted for 35±3% (SEM) of *I _{spike}*. Compared to other filter estimates, the MID filters provided significantly better predictions of neural responses to natural stimuli than either the dSTA (p<10

The same results were also obtained using a larger fraction of the data (1/4 instead of 1/8) as the test dataset for computing jackknife estimates for each kind of a filter. In this case, the MID filters derived from natural stimuli accounted for 31±3.6% (SEM) of *I _{spike}* when predicting neural responses to a novel set of natural stimuli, which was significantly better than predictions based on the RdSTA, 26.6±3.5% (p=0.015) and those based on the dSTA, 11 ±1.6% (p<10

While characterizing neural processing in the natural setting remains one of the ultimate goals of sensory neuroscience, considerable technical difficulties exist for correctly estimating neural receptive fields from natural stimuli (Paninski, 2003a; Sharpee et al., 2003; Sharpee et al., 2004; Rust and Movshon, 2005). Here we have examined in detail two models for computing spatiotemporal filters, the linear model and the LN model. With noise inputs, there was very little difference, if any, between the spatiotemporal filters of the two models. This is as expected theoretically for white Gaussian or other uncorrelated (spherically symmetric) stimuli (de Boer and Kuyper, 1968; Rieke et al., 1997; Chichilnisky, 2001; Paninski, 2003a; Sharpee et al., 2004). In contrast, spatiotemporal filters of the linear and the LN model computed with natural stimuli can be profoundly different (Figure 5). Despite the added complexity in the LN model, it produces spatiotemporal filters that are more stable under changes in the stimulus statistics between noise and natural inputs (Figure 6). Spatiotemporal filters obtained using the LN model also better predicted spikes elicited by a novel segment of either noise or natural stimuli (Figure 7), even though predictions based on the spatiotemporal filters computed using the linear model also took into account the best possible nonlinearity for those filters.

The two standard methods of computing spatiotemporal filters of the linear model with natural stimuli have their limitations. As has been pointed out previously (Theunissen et al., 2000; Theunissen et al., 2001; Smyth et al., 2003; David et al., 2004; Touryan et al., 2005; Woolley et al., 2006), computing the spatiotemporal filter as the dSTA tends to amplify noise at spatial and temporal frequencies that are relatively undersampled. For natural stimuli, this noise amplification happens at higher temporal and spatial frequencies. In agreement with previous results, (Theunissen et al., 2000; Theunissen et al., 2001; Smyth et al., 2003; David et al., 2004; Touryan et al., 2005), we show here that introducing smoothness constraints and regularization leads to greater predictive power of the RdSTA spatiotemporal filter compared to that of the dSTA. However in some cases, the RdSTA can produce spatiotemporal filters that substantially overestimate the contribution of low frequency components (Fig. 5) to neural filtering. In this respect, spatiotemporal filters of the LN model computed as the MID seem to find the middle ground: at low spatial and temporal frequencies they are similar to the dSTA filters, while at higher spatial frequencies their amplitude spectra are intermediate between those of the dSTA and RdSTA filters. At these higher spatial frequencies the MID filters computed for natural and noise stimuli had identical amplitude spectra, which provided additional support for the computations of the LN model.

Theoretical arguments indicate that advantages of computing the MID filters compared with the RdSTA filters should increase with increasing deviations of the stimulus ensemble from the correlated Gaussian distribution (Ringach et al., 1997; Sharpee et al., 2004) or when neural noise level decreases (Sharpee, 2007). One measure of the deviation from a Gaussian ensemble is the kurtosis of the distribution of light intensity values of individual pixels, which is zero for a Gaussian distribution but positive for distributions that are more heavy-tailed than Gaussian. The natural stimulus ensemble used in this study had a mean kurtosis value across pixels of ~0.4 (range from 0.19 to 0.64) measured for the distribution of light intensity at single pixels across ~50,000 frames. By comparison, one can expect to find kurtosis values less than 0.04 for a sequence of the same size taken from the uncorrelated Gaussian distribution (Press et al., 1992). Our kurtosis measurement is consistent with previous studies of the statistics of natural stimuli where kurtosis values with a mean of ~2, range [0.2 : 22] were obtained after correcting for finite-size effects (Thomson, 1999). Therefore, while our stimulus ensemble is non-Gaussian, typical natural stimulus ensembles are even more strongly non-Gaussian by this measure.

Overall, these findings suggest that stimulus-dependent changes in neural spatiotemporal filters are better characterized by the LN model compared to the linear model. For example, both the dSTA and RdSTA filters computed for natural stimuli show changes in tuning across spatial and temporal frequencies relative to white noise filters, but these changes are generally of opposite sign for the dSTA vs. the RdSTA. A point-by-point comparison between spatiotemporal filters, as quantified by the correlation coefficient, also shows that spatiotemporal filters of the linear model differed much more between noise and natural stimuli than did the spatiotemporal filters of the LN model.

The LN model considered here can be extended to account for multiple relevant stimulus features (de Ruyter van Steveninck and Bialek, 1988; Brenner et al., 2000a; Touryan et al., 2002; Agüera y Arcas et al., 2003; Bialek and de Ruyter van Steveninck, 2005; Felsen et al., 2005b; Rust et al., 2005; Slee et al., 2005; Touryan et al., 2005; Fairhall et al., 2006). Our best predictions using the LN model with a single spatiotemporal filter accounted for ~ 40% of the overall information contained in the stimulus about the arrival times of single spikes, *I _{spike}*. Including additional dimensions has the potential to increase the percentage of

In any case, it is helpful to use Shannon mutual information as a measure of predictive power, particularly in the setting of natural (non-Gaussian) stimuli, because it measures the degree to which model output accounts for neural response independently of the statistical distributions of these variables. For any distributions, it tells the number of bits of information given, on average, by the model output about the neural response. Although the percentage of variance explained provides an intuitive measure of the size of the error relative to the size of the response, it might not reflect deviations in accounting for the higher-than-second order moments of the response.

Stimulus-dependent changes in neural spatiotemporal filters have been observed in several sensory modalities, including auditory (Theunissen et al., 2000; Theunissen et al., 2001; Theunissen and Shaevitz, 2006; Woolley et al., 2006), visual (Smirnakis et al., 1997; Chander and Chichilnisky, 2001; David et al., 2004; Felsen et al., 2005a; Felsen et al., 2005b; Hosoya et al., 2005; Sharpee et al., 2006) and somatosensory (Maravall et al., 2007). It is intriguing that such stimulus-dependent effects increase precision of temporal coding and are tuned to emphasize the most informative features of natural sounds (Woolley et al., 2005; Theunissen and Shaevitz, 2006; Woolley et al., 2006). In this respect, such stimulus-dependent tuning of auditory neurons is reminiscent of how visual neurons adapt their filtering properties to match the statistics of stimuli (Smirnakis et al., 1997; Hosoya et al., 2005; Sharpee et al., 2006), and suggest that there may be general principles for sensory processing common to different modalities. It will be interesting to know whether taking into account nonlinear transformations between stimuli and spike probabilities when estimating the neural spatiotemporal filter will similarly improve our models of neural coding in other stages of visual processing and sensory modalities, as it does for simple cells in the primary visual cortex.

We thank Matt Caywood, Andrei Kurgansky, Sergei Rebrik and Hiroki Sugihara for help with experiments. This research was supported by research grants EY13595 and EY11001 from the National Eye Institute, grants from the Swartz Foundation and the Mentored Quantitative Career Development Award MH068904 from NIMH. Computing resources were provided by the National Science Foundation through Partnerships for Advanced Computational Infrastructure at the Distributed Terascale Facility and Terascale Extensions.

- RF
- receptive field
- LN model
- linear-nonlinear model
- STA
- spike-triggered average
- dSTA
- decorrelated spike-triggered average
- RdSTA
- regularized decorrelated spike-triggered average
- MID
- most informative dimension

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