Aggregate input-output models of neuronal populations

Shreya Saxena, Marc H. Schieber, Nitish V. Thakor, Sridevi V. Sarma

Research output: Contribution to journalArticle

Abstract

An extraordinary amount of electrophysiological data has been collected from various brain nuclei to help us understand how neural activity in one region influences another region. In this paper, we exploit the point process modeling (PPM) framework and describe a method for constructing aggregate input-output (IO) stochastic models that predict spiking activity of a population of neurons in the "output" region as a function of the spiking activity of a population of neurons in the "input" region. We first build PPMs of each output neuron as a function of all input neurons, and then cluster the output neurons using the model parameters. Output neurons that lie within the same cluster have the same functional dependence on the input neurons. We first applied our method to simulated data, and successfully uncovered the predetermined relationship between the two regions. We then applied our method to experimental data to understand the input-output relationship between motor cortical neurons and 1) somatosensory and 2) premotor cortical neurons during a behavioral task. Our aggregate IO models highlighted interesting physiological dependences including relative effects of inhibition/excitation from input neurons and extrinsic factors on output neurons.

Original languageEnglish (US)
Article number2196699
Pages (from-to)2030-2039
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume59
Issue number7
DOIs
StatePublished - Jun 29 2012

Keywords

  • Input-output models
  • neural systems
  • point process models

ASJC Scopus subject areas

  • Biomedical Engineering

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