Predicting Single-Neuron Activity in Locally Connected Networks

Abstract

The characterization of coordinated activity in neuronal populations has received renewed interest in the light of advancing experimental techniques that 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 using 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.

Simultaneous Recording of Single-Neuron Activities and Broad-Area Intracranial Electroencephalography

“…Although the studies using single-unit recording were applied to the very restricted regions of the brain, they have provided fundamental and important information on the mechanism of human brain function. [21][22][23][24][25] In contrast, iEEG can cover a broad area of the cortical surface, allowing us to localize the functional and diseased area and to clarify the association of multiple areas and the spread pattern of the disease. 26,27 To elucidate the relationship between the single unit and ensemble, neuron activity is crucial but has not been examined in humans because of technical difficulties.…”