Robust temporal coding of contrast by V1 neurons for transient but not for steady-state stimuli

Abstract

We show that spike timing adds to the information content of spike trains for transiently presented stimuli but not for comparable steady-state stimuli, even if the latter elicit transient responses. Contrast responses of 22 single neurons in macaque V1 to periodic presentation of steady-state stimuli (drifting sinusoidal gratings) and transient stimuli (drifting edges) of optimal spatiotemporal parameters were recorded extracellularly. The responses were analyzed for contrast-dependent clustering in spaces determined by metrics sensitive to the temporal structure of spike trains. Two types of metrics, cost-based spike time metrics and metrics based on Fourier harmonics of the response, were used. With both families of metrics, temporal coding of contrast is lacking in responses to drifting sinusoidal gratings of most (simple and complex) V1 neurons. However, two-thirds of all neurons, mostly complex cells, displayed significant temporal coding of contrast for edge stimuli. The Fourier metrics indicated that different response harmonics are partially independent, and their combined use increases information about transient stimuli. Our results demonstrate the importance of stimulus transience for temporal coding. This finding is significant for natural vision because moving edges, which are present in moving object boundaries, and saccades induce transients. We think that an abrupt change in the adapted state of the local visual circuitry triggers the temporal structuring of spike trains in V1 neurons.

Fig. 1.

Quantifying the dissimilarity of spike trains via the spike time metricsD^spike[q] andD^spike,circ[q]. The distance between spike trains S_a andS_b is the minimum cost of transformingS_a into S_b via a sequence of elementary steps, as detailed in Materials and Methods.A, Spike time metricD^spike[q]. Spike trains are considered to be segments of time, and the periodicity of the stimulus is ignored. The direction of time is indicated at thebottom left. Transformation of spike trainS_a into S_binvolves the deletion of the first spike ofS_a (marked by asterisk), insertion of the last two spikes of S_b (also marked by asterisk), and shifts of the other spikes, as diagrammed by the arrows fromS_a to the virtual spike trainS′. B, The spike time metric adapted to periodic stimuli,D^spike,circ[q]. The spike trains S_a andS_b are now considered to be cyclic, with a period corresponding to that of the stimulus. This modification allows shifts of spikes to wrap around across the cycle boundaries. In the example illustrated, this modification changes the minimum-cost set of transformations to one in which the initial spike ofS_a (marked by asterisk) is shifted across a cycle boundary to coincide with the last spike ofS_b. For spike trains that differ in the illustrated manner, the distances in this modified “circular”D^spike,circ[q] metric will be smaller than those defined by the open-ended form,D^spike[q].

Fig. 2.

Analysis of temporal coding in contrast responses of a layer 6 nondirectional complex cell (mt926).A, Cycle-by-cycle raster plots of responses to edges (0.2 cycle/°, 1 Hz, 24 cycles at each contrast) at the seven different contrasts indicated on the right. Only a subset of the blank runs are shown. B, Semilogarithmic plot of the contrast–response function for edges based on the DC component of responses. Error bars are ±1 SD. C, Level of contrast-dependent clustering (uncorrected for chance clustering),H(q), for edge responses (thick line with plus symbols), and the estimated level of chance clustering (dashed line) for the metricsD^spike,circ[q], as a function of the cost parameter (q). For the estimate of the level of chance clustering, 10 random reassignments were used, and the SE values of these estimates are indicated by theshaded region. D, Cycle-by-cycle raster plots of responses of the same complex cell to gratings (1.6 cycle/°, 5 Hz, 40 cycles at each contrast) at seven different contrasts. Only a subset of the blank runs are shown. E, Contrast–response function for gratings, based on the DC component of responses. F, H(q) for the grating responses, plotted as in C.

Fig. 3.

Analysis of temporal coding in contrast responses of a layer 6 nondirectional simple cell (mt928). Data are plotted as in Figure 2. A–C, Edge responses (0.3 cycle/°, 3 Hz, 24 cycles at each contrast).D–F, Grating responses (1.2 cycle/°, 5 Hz, 40 cycles at each contrast). A, D, Cycle-by-cycle rasters of spikes. Only a subset of the blank runs are shown. B, E, Contrast–response function.C, F,H(q) (uncorrected, thick lines with symbols; correction for the level of chance clustering, dashed line; SE values of the correction estimates, shaded region).

Fig. 4.

Comparison of the level of contrast-dependent clustering,H(q), for edges (plus symbols), and gratings (filled circles), for three V1 neurons.H(q) is determined for the metricsD^spike,circ[q], and the level of chance clustering has been subtracted. Anarrowhead points to the maximum of eachH(q) curve. A andB are typical examples, C is an exceptional cell for which temporal structure contributes more strongly to the maximum of H(q) for responses to gratings than to edges. A, Layer 4Cα nondirectional complex cell (mt918). Edges, 0.3 cycles/°, 0.5 Hz, 12 cycles at each contrast; gratings, 4 cycles/°, 6 Hz, 48 cycles at each contrast. B, Layer 4Cα nondirectional simple cell (mt838). Edges, 0.2 cycle/°, 3 Hz, 24 cycles at each contrast; gratings, 0.6 cycle/°, 5 Hz, 80 cycles at each contrast.C, Layer 4B nonoriented simple cell (mt942). Edges, 0.15 cycle/°, 3 Hz, 24 cycles at each contrast; gratings, 0.5 cycle/°, 10 Hz, 80 cycles at each contrast.

Fig. 5.

Comparison of the levels of contrast-dependent clustering obtained with spike counts and spike time metrics in 22 V1 neurons. H_count, the level achieved with D^count =D^spike,circ[0], is plotted againstH_max, the level achieved with the optimal spike time metricD^spike,circ[q_max]. Each neuron is represented by two data points: one for edges (plus symbols) and one for gratings (filled circles). Symbols lying above the diagonal H_count =H_max indicate a contribution of the temporal pattern of spikes to coding of contrast.

Fig. 6.

Comparison of the temporal contribution to the maximum level of contrast-dependent clustering in the responses of 22 V1 neurons to gratings (ΔH_G =H_{max, Grating} − H_{count, Grating}) versus edges (ΔH_E =H_{max, Edge} −H_{count, Edge}). Neurons for which temporal pattern contributes to contrast-dependent responses for edges but not gratings occupy the top left region along the ΔH_E axis in the scatter plot. Neurons with the opposite behavior are represented by symbols scattered near the ΔH_G axis. As the scatter shows, most V1 neurons belong to the first group. Simple cells are represented withopen symbols, complex cells with filled symbols.

Fig. 7.

A, Distribution of the cost parameter (q_max, plotted logarithmically except for q_max = 0) that maximizes H(q) based onD^spike,circ[q_max]. Each of the 22 neurons is represented by two values, one for edges and one for gratings. The geometric mean of the nonzero values ofq_max was 17 sec⁻¹, indicating an average temporal resolution of 60 msec for the optimal spike time metric in those neurons in which temporal pattern contributed to stimulus-dependent clustering. The dark portion of the histogram is the distribution of complex cells.B, Distribution of the temporal frequency (tf_max = n_maxω) of the highest Fourier harmonic that was needed to maximizeH(nω) based on the metric familyF^all (Fig. 8). The geometric mean of the nonzero values of tf_max was 9.6 Hz. Distribution of complex cells is indicated by the dark portion as in A.

Fig. 8.

Comparison ofH(nω) based on four families of Fourier metrics (F^single, thin dotted lines; F^all, thin linewith asterisk;F^even, thin lines withplus symbols; F^odd,thin lines with open circles), andH(q) based on spike time metrics (thick line with no symbols) in two typical V1 neurons.A, B, Analysis of grating (A) and edge (B) responses of a layer 4Cα complex cell (mt918, stimulus conditions as in Fig.4A). C, D, Analysis of grating (C) and edge (D) responses of a layer 6 simple cell (mt829; edges, 0.36 cycle/°, 1.25 Hz, 15 cycles at each contrast; gratings, 6.1 cycles/°, 4 Hz, 32 cycles at each contrast). For each data set, the five Hcurves are shown on comparable abscissae; for the Fourier metrics, as a function of the frequency (in Hertz) of the highest harmonic used, for the spike time metrics, as a function of the cost parameterq (sec⁻¹).