CHAOS AND SYNCHRONY IN A MODEL OF A HYPERCOLUMN IN VISUAL-CORTEX

Neurons in cortical slices emit spikes or bursts of spikes regularly i n response to a suprathreshold current injection. This behavior is in marked contrast to the behavior of cortical neurons in vivo, whose res ponse to electrical or sensory input displays a strong degree of irreg ularity. Correlation measurements show a significant degree of synchro ny in the temporal fluctuations of neuronal activities in cortex. We e xplore the hypothesis that these phenomena are the result of the synch ronized chaos generated by the deterministic dynamics of local cortica l networks. A model of a ''hypercolumn'' in the visual cortex is studi ed. It consists of two populations of neurons, one inhibitory and one excitatory. The dynamics of the neurons is based on a Hodgkin-Huxley t ype model of excitable voltage-clamped cells with several cellular and synaptic conductances. A slow potassium current is included in the dy namics of the excitatory population to reproduce the observed adaptati on of the spike trains emitted by these neurons. The pattern of connec tivity has a spatial structure which is correlated with the internal o rganization of hypercolumns in orientation columns. Numerical simulati ons of the model show that in an appropriate parameter range, the netw ork settles in a synchronous chaotic state, characterized by a strong temporal variability of the neural activity which is correlated across the hypercolumn. Strong inhibitory feedback is essential for the stab ilization of this state. These results show that the cooperative dynam ics of large neuronal networks are capable of generating variability a nd synchrony similar to those observed in cortex. Auto-correlation and cross-correlation functions of neuronal spike trains are computed, an d their temporal and spatial features are analyzed. In other parameter regimes, the network exhibits two additional states: synchronized osc illations and an asynchronous state. We use our model to study cortica l mechanisms for orientation selectivity. It is shown that in a suitab le parameter regime, when the input is not oriented, the network has a continuum of states, each representing an inhomogeneous population ac tivity which is peaked at one of the orientation columns. As a result, when a weakly oriented input stimulates the network, it yields a shar p orientation tuning. The properties of the network in this regime, in cluding the appearance of virtual rotations and broad stimulus-depende nt cross-correlations, are investigated. The results agree with the pr edictions of the mean field theory which was previously derived for a simplified model of stochastic, two-state neurons. The relation betwee n the results of the model and experiments in visual cortex are discus sed.