Neuronal assembly detection and cell membership specification by principal component analysis.

Published

Journal Article

In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership.

Full Text

Duke Authors

Cited Authors

  • Lopes-dos-Santos, V; Conde-Ocazionez, S; Nicolelis, MAL; Ribeiro, ST; Tort, ABL

Published Date

  • 2011

Published In

Volume / Issue

  • 6 / 6

Start / End Page

  • e20996 -

PubMed ID

  • 21698248

Pubmed Central ID

  • 21698248

Electronic International Standard Serial Number (EISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0020996

Language

  • eng

Conference Location

  • United States