Dynamics and orientation selectivity in a cortical model of rodent V1 with excess bidirectional connections

Shrisha Rao,Carl Van Vreeswijk,David Hansel

doi:10.1038/s41598-019-40183-8

Abstract

Recent experiments have revealed fine structure in cortical microcircuitry. In particular, bidirectional connections are more prevalent than expected by chance. Whether this fine structure affects cortical dynamics and function has not yet been studied. Here we investigate the effects of excess bidirectionality in a strongly recurrent network model of rodent V1. We show that reciprocal connections have only a very weak effect on orientation selectivity. We find that excess reciprocity between inhibitory neurons slows down the dynamics and strongly increases the Fano factor, while for reciprocal connections between excitatory and inhibitory neurons it has the opposite effect. In contrast, excess bidirectionality within the excitatory population has a minor effect on the neuronal dynamics. These results can be explained by an effective delayed neuronal self-coupling which stems from the fine structure. Our work suggests that excess bidirectionality between inhibitory neurons decreases the efficiency of feature encoding in cortex by reducing the signal to noise ratio. On the other hand it implies that the experimentally observed strong reciprocity between excitatory and inhibitory neurons improves the feature encoding.

Highlights

Cortical neurons with similar functional properties have a high probability of being connected[1,2,3,4,5,6,7]
We study the effect of increasing the amount of bidirectionality for excitatory to excitatory (EE), inhibitory to inhibitory (II) and between excitatory and inhibitory (EI) connections
Previous theoretical studies of cortical networks have mostly considered connectivities described by a directed Erdös-Rényi like graph where reciprocal connections occur by chance

Summary

Introduction

Cortical neurons with similar functional properties have a high probability of being connected[1,2,3,4,5,6,7]. Previous theoretical studies investigating the dynamics of model cortical networks have assumed that the probabilities of connection are independent. A great deal of theoretical studies assume a directed Erdös-Rényi graph as the network architecture, in which the probability of connection depends solely on the neuronal type, excitatory or inhibitory, of the pre and postsynaptic neuron. It should be noted, that if the probability of connections are independent, the network has no fine structure. Hansel[18], the average number of feedforward inputs from layer 4 in excitatory and inhibitory neurons are different: K

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Conclusion