On the operating point of cortical computation

In this paper, we consider a class of network models of Hodgkin-Huxley type neurons arranged according to a biologically plausible two-dimensional topographic orientation preference map, as found in primary visual cortex (V1). We systematically vary the strength of the recurrent excitation and inhibition relative to the strength of the afferent input in order to characterize different operating regimes of the network. We then compare the map-location dependence of the tuning in the networks with different parametrizations with the neuronal tuning measured in cat V1 in vivo. By considering the tuning of neuronal dynamic and state variables, conductances and membrane potential respectively, our quantitative analysis is able to constrain the operating regime of V1: The data provide strong evidence for a network, in which the afferent input is dominated by strong, balanced contributions of recurrent excitation and inhibition, operating in vivo. Interestingly, this recurrent regime is close to a regime of "instability", characterized by strong, self-sustained activity. The firing rate of neurons in the best-fitting model network is therefore particularly sensitive to small modulations of model parameters, possibly one of the functional benefits of this particular operating regime.