Modelling divisive inhibition in the primary visual cortex from optogenetic circuit mapping data

Abstract

Recurrent networks are abundant in the neocortex and are recognised as a means of amplifying feedforward thalamic sensory inputs. However, when operating at high gain, which is necessary for this signal amplification, the standard recurrent network firing rate model suffers from increased reaction times to rapidly changing stimuli. Divisive inhibition has been proposed as a means of bypassing this coupling of system gain and time constant. In my thesis I focus on the importance of inhibition in recurrent networks in visual information processing. This was motivated by a recent study where the presence and absence of translaminar inhibition distinguished cells in the primary visual cortex. I apply several divisive inhibition schemes to an existing recurrent network model of simple and complex cells. The schemes are studied analytically and also simulated to assess how well they can be integrated into this existing model whilst simultaneously solving the coupled system gain and time constant problem. Though each scheme has its benefits, I propose that a mixture of schemes is likely in real physiology.This thesis is not currently available via ORA