Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies

TL;DR

This study demonstrates that linear mechanisms within recurrent spiking neuron networks with diverse connectivity patterns can explain the broad distribution of orientation selectivity observed in the visual cortex.

Contribution

It shows that a linear firing rate theory accurately predicts orientation selectivity distributions in various biologically plausible recurrent network models.

Findings

Linear theory matches numerical simulations across connectivity patterns.

Distance-dependent connectivity does not affect the linear analysis validity.

Orientation selectivity distribution arises from linear stimulus processing mechanisms.

Abstract

Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity.