Recurrent Excitation in Neocortical Circuits

TLDR

Most cortical synapses arise from cortical neurons, with the largest input coming from neighboring excitatory cells, yet prevailing models ignore feedback excitation and focus solely on serial feedforward pathways. The study aims to show that cat visual cortex neurons can use excitatory feedback, modeled as network conductance, to amplify feedforward inputs while keeping firing rates proportional to stimulus strength. The authors incorporate this feedback conductance into models of cortical direction and orientation selectivity to illustrate the underlying design principles. The results reveal that neuronal discharge remains proportional to stimulus strength despite strong recurrent connections, thereby avoiding runaway excitation.

Abstract

The majority of synapses in the mammalian cortex originate from cortical neurons. Indeed, the largest input to cortical cells comes from neighboring excitatory cells. However, most models of cortical development and processing do not reflect the anatomy and physiology of feedback excitation and are restricted to serial feedforward excitation. This report describes how populations of neurons in cat visual cortex can use excitatory feedback, characterized as an effective "network conductance", to amplify their feedforward input signals and demonstrates how neuronal discharge can be kept proportional to stimulus strength despite strong, recurrent connections that threaten to cause runaway excitation. These principles are incorporated into models of cortical direction and orientation selectivity that emphasize the basic design principles of cortical architectures.