Cortical sensitivity to visual features in natural scenes

Abstract

A central hypothesis concerning sensory processing is that the neuronal circuits are specifically adapted to represent natural stimuli efficiently. Here we show a novel effect in cortical coding of natural images. Using spike-triggered average or spike-triggered covariance analyses, we first identified the visual features selectively represented by each cortical neuron from its responses to natural images. We then measured the neuronal sensitivity to these features when they were present in either natural images or random stimuli. We found that in the responses of complex cells, but not of simple cells, the sensitivity was markedly higher for natural images than for random stimuli. Such elevated sensitivity leads to increased detectability of the visual features and thus an improved cortical representation of natural scenes. Interestingly, this effect is due not to the spatial power spectra of natural images, but to their phase regularities. These results point to a distinct visual-coding strategy that is mediated by contextual modulation of cortical responses tuned to the spatial-phase structure of natural scenes.

Figure 1. Measurement of Preferred Features and Feature Sensitivity for V1 Complex Cells

(A) Upper panel: example natural images. White boxes (12 × 12 pixels) indicate area presented in experiments. Lower panel: schematic spike train, binned at stimulus frame rate (24 Hz, dotted lines). Arrow indicates temporal delay (1 frame) at which preferred features were estimated, which was determined in preliminary studies to be the optimal temporal delay (see Figure S2). (B) Estimation of preferred features (significant eigenvectors) using STC analysis (see Materials and Methods). Left panel: preferred features of a neuron, with light and dark regions represented by red and blue; dashed ovals delineate the first feature to facilitate comparison with the images. Right panel: 30 largest eigenvalues of STC matrix. Dashed lines: control confidence intervals (mean ± 12 standard deviation of control eigenvalues). Filled circles: significant eigenvalues corresponding to eigenvectors shown on left. (C) Upper panel: natural images. Dashed ovals correspond to those in (B). Middle panel: contrast of the first preferred feature (F.C. denotes feature contrast; see Materials and Methods). Lower panel: responses of the neuron (in spikes/s) to natural images. Black dots: feature contrasts (middle) and neuronal responses (lower) for the example images. (D) Contrast-response function. Error bar: ± standard error of the mean.

Figure 2. Matching of Feature Contrasts in Natural and Random Ensembles

(A) Example images in the natural (upper row) and the random (lower row) ensembles, which were matched frame by frame for both global and feature contrasts. (B) Contrasts of a preferred feature of a complex cell (inset at center) in each frame of the natural (squares) and random (circles) ensembles in (A). F.C. denotes feature contrast. (C) Distributions of feature contrasts in the natural (left) and random (middle) ensembles, and the distribution of the difference in feature contrast between the two ensembles (right).

Figure 3. Feature Sensitivity of Complex Cells in Response to Natural Images and Random Stimuli

(A) Contrast-response functions for both preferred features (insets above) of a complex cell. Curves: fits of data with quadratic functions. (B) Gain of contrast-response function (in spikes/s per unit feature contrast) for natural ensemble versus that for contrast-matched random ensemble. For this population of cells, the gain was significantly higher for the natural than for the random ensemble (n = 24, from 14 cells; p < 10⁻⁴, Wilcoxon signed rank test).

Figure 4. Feature Sensitivity of Simple Cells in Response to Natural Images and Random Stimuli

(A) Contrast-response function for the preferred feature (inset above) of a simple cell. Curves: fits of data with quadratic functions (for positive feature contrasts only). (B) Gain of contrast-response function, as in Figure 3B (n = 14, from 14 cells).

Figure 5. Difference in Feature Sensitivity between the Responses to Natural and Random Stimuli as a Function of F ₁ /F ₀

Each symbol represents data from one cell. For complex cells with two significant eigenvectors, the sensitivity difference was averaged between the two eigenvectors. Dashed line: linear fit.

Figure 6. Detectability of Features from Neuronal Responses to Natural Images and Random Stimuli

(A) Probability distribution of feature contrast in a natural ensemble (or, equivalently, its matched random ensemble). For simplicity, only the positive side (feature contrast >0) is shown. Gray shading: feature contrasts near zero (<T ₀, here T ₀ = 0.007, “feature absent”); black shading: high feature contrasts (>T ₁, here T ₁ = 0.04, “feature present”). (B) Conditional probability distributions of responses evoked by natural images (upper) and random stimuli (lower). Solid lines: response distributions when the feature was present in stimulus (black shading in [A]); dashed lines: distributions when the feature was absent (gray shading in [A]). (C) Feature detectability in natural images versus that in matched random stimuli, for the same population of cells shown in Figure 3B. Detectability was measured as the percentage of trials in which stimuli were correctly classified as “feature present” or “feature absent” (see Materials and Methods).

Figure 7. Effects of Power and Phase Spectra of Stimuli on Cortical Feature Sensitivity

(A) Four classes of stimulus ensembles with distinct combinations of power (P) and phase (φ) characteristics; +: natural; −: random. Example stimuli from each class are shown. The P⁻/φ⁻ and P⁻/φ⁺ stimuli are matched for both the global contrast and the feature contrasts for a particular complex cell. (B) Summary of cortical feature sensitivity (contrast-response gain; see Figure 3B) for the stimulus classes in (A). In each experiment, a random (P⁻/φ⁻) stimulus ensemble was generated to match P ⁺/φ⁺, P ⁺/φ⁻, or P⁻/φ⁺ in global and feature contrasts (see Figure 2 and Materials and Methods), and the measured contrast-response gain was plotted against the gain for P⁻/φ⁻ (as in Figure 3B). Bar represents slope of linear regression (through origin); >1 indicates higher contrast-response gain relative to P⁻/φ⁻. Error bar: ± standard deviation. P ⁺/φ⁺ bars for simple (S) and complex (C) cells were computed from data in Figures 3B and 4B, respectively, and P ⁺/φ⁻ (n = 10, from six cells) and P⁻/φ⁺ (n = 11, from six cells) were from largely nonoverlapping populations of complex cells (one cell was used in two separate experiments).