Guiding Synchrony through Random Networks

Sven Jahnke,Raoul-Martin Memmesheimer,Marc Timme

doi:10.1103/physrevx.2.041016

Abstract

Sparse random networks contain structures that can be considered as diluted feed-forward networks. Modeling of cortical circuits has shown that feed-forward structures, if strongly pronounced compared to the embedding random network, enable reliable signal transmission by propagating localized (sub-network) synchrony. This assumed prominence, however, is not experimentally observed in local cortical circuits. Here we show that nonlinear dendritic interactions as discovered in recent single neuron experiments, naturally enable guided synchrony propagation already in random recurrent neural networks exhibiting mildly enhanced, biologically plausible sub-structures.

Highlights

Cortical neural networks generate a ground state of highly irregular spiking activity whose dynamics is sensitive to small perturbations such as missing or additional spikes [1,2,3,4]
How can diluted feed-forward networks (FFNs) propagate synchrony? FFNs consist of a sequence of layers, each composed of ! excitatory neurons; they forward connections to neurons in the subsequent layer randomly present with probability p
In the absence of synchronous activity, each neuron of the FFN receives a large number of inputs from an emulated external network and only very few inputs from the previous layer, such that its dynamics is practically identical to the ground state of balanced networks

Summary

INTRODUCTION

Cortical neural networks generate a ground state of highly irregular spiking activity whose dynamics is sensitive to small perturbations such as missing or additional spikes [1,2,3,4]. Neurons are capable of generating fast dendritic spikes In the soma, these spikes induce rapid, strong depolarizations [8] that are nonlinearly enhanced compared to depolarizations expected from linear summation of single inputs. Other experiments have found slow dendritic spikes that are comparably insensitive to input synchrony [9] These slow dendritic spikes endow single neurons with computational capabilities comparable to multilayered feed-forward networks of simple-rate neurons [10]. They provide a possible mechanism underlying neural bursting and its propagation, which have been shown to enhance reliability and temporal precision of signal propagation [11,12]. Using large-scale simulations of more detailed recurrent network models, we show that feed-forward networks that occur naturally as part of random circuits enable persistent guided synchrony propagation due to dendritic nonlinearities

Analytically tractable model

Biologically more detailed model

Feed-forward chains with linear coupling

Feed-forward chains with nonlinear coupling

Recurrent networks

DISCUSSION