Burst Firing and Modulation of Functional Connectivity in Cat Striate Cortex

Abstract

We studied the influences of the temporal firing patterns of presynaptic cat visual cortical cells on spike generation by postsynaptic cells. Multiunit recordings were dissected into the activity of individual neurons within the recorded group. Cross-correlation analysis was then used to identify directly coupled neuron pairs. The 22 multiunit groups recorded typically showed activity from two to six neurons, each containing between 1 and 15 neuron pairs. From a total of 241 neuron pairs, 91 (38%) had a shifted cross-correlation peak, which indicated a possible direct connection. Only two multiunit groups contained no shifted peaks. Burst activity, defined by groups of two or more spikes with intervals of </=8 ms from any single neuron, was analyzed in terms of its effectiveness in eliciting a spike from a second, driven neuron. We defined effectiveness as the percentage of spikes from the driving neuron that are time related to spikes of the driven neuron. The effectiveness of bursts (of any length) in eliciting a time-related response spike averaged 18.53% across all measurements as compared with the effectiveness of single spikes, which averaged 9.53%. Longer bursts were more effective than shorter ones. Effectiveness was reduced with spatially nonoptimal, as opposed to optimal, stimuli. The effectiveness of both bursts and single spikes decreased by the same amount across measurements with nonoptimal orientations, spatial frequencies and contrasts. At similar firing rates and burst lengths, the decrease was more pronounced for nonoptimal orientations than for lower contrasts, suggesting the existence of a mechanism that reduces effectiveness at nonoptimal orientations. These results support the hypothesis that neural information can be emphasized via instantaneous rate coding that is not preserved over long intervals or over trials. This is consistent with the integrate and fire model, where bursts participate in temporal integration.

Cooperative synchronized assemblies enhance orientation discrimination

“…Similar results have been documented with other methods (19,22). We also measured positive synergy (i.e., cooperation; information available only in the joint activity of the cells) among pairs of cells, at least for discriminating fine differences in orientation (21).…”

“…The dependency quantifies how much the response varies based on the null hypothesis that if the cells in an assembly fire independently, the probability of all cells firing within the same bin should be equal to the product of the probabilities of each neuron firing within a bin (i.e., mutual exclusivity). We find the dependency to indicate synchronous or correlated firing by comparing our measurements with gravitational clustering (28) and cross-correlation analysis (19,29). The latter measurements are, however, limited to calculations between pairs of cells, so any direct comparisons are not practical.…”

“…1A) shows the preferred orientations for a population of 22 cells recorded simultaneously in the primary visual cortex of a cat with a 5 ϫ 5 electrode array. We first identified cell assemblies based on similarity of preferred orientation as well as synchronized firing, which was determined with both gravitational clustering (28) and cross-correlation (19,29) analysis. Cells that had the same preferred orientation were usually synchronized.…”

“…However, temporal information is relevant only when an appropriate decoding mechanism exists (15). Synchronization between neural assemblies provides one such mechanism for encoding, and possibly decoding, temporal structure because synchrony is dependent on temporal patterns (17)(18)(19) and is correlated with the orientation (17-19) and coherence (17,18,20) of visual stimuli.We have previously shown (21) that the orientation selectivity of dependency (a numeric measure reflecting synchrony) between two cells is on average 35.5% narrower than the selectivity of their individual rate tuning. Similar results have been documented with other methods (19,22).…”

“…However, temporal information is relevant only when an appropriate decoding mechanism exists (15). Synchronization between neural assemblies provides one such mechanism for encoding, and possibly decoding, temporal structure because synchrony is dependent on temporal patterns (17)(18)(19) and is correlated with the orientation (17)(18)(19) and coherence (17,18,20) of visual stimuli.…”