Predicting single-neuron activity in locally connected networks

TL;DR

This paper demonstrates that in locally connected neuronal networks, the activity of a single neuron can be predicted using its own history and that of nearby neurons, reflecting realistic cortical architecture.

Contribution

It introduces a point process model showing baseline predictability in simulated cortical networks with realistic connectivity and bursting behavior.

Findings

Single-neuron activity can be predicted from local spiking histories.

The model exhibits realistic cortical bursting episodes.

Baseline predictability may be a general feature of local networks.

Abstract

The characterization of coordinated activity in neuronal populations has received renewed interest in the light of advancing experimental techniques which allow recordings from multiple units simultaneously. Across both in vitro and in vivo preparations, nearby neurons show coordinated responses when spontaneously active, and when subject to external stimuli. Recent work (Truccolo, Hochberg, & Donoghue, 2010) has connected these coordinated responses to behavior, showing that small ensembles of neurons in arm related areas of sensorimotor cortex can reliably predict single-neuron spikes in behaving monkeys and humans. We investigate this phenomenon utilizing an analogous point process model, showing that in the case of a computational model of cortex responding to random background inputs, one is similarly able to predict the future state of a single neuron by considering its own spiking history, together with the spiking histories of randomly sampled ensembles of nearby neurons. This model exhibits realistic cortical architecture and displays bursting episodes in the two distinct connectivity schemes studied. We conjecture that the baseline predictability we find in these instances is characteristic of locally connected networks more broadly considered.