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Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

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  • Vítor Lopes-dos-Santos
  • Sergio Conde-Ocazionez
  • Miguel A L Nicolelis
  • Sidarta T Ribeiro
  • Adriano B L Tort

Abstract

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.

Suggested Citation

  • Vítor Lopes-dos-Santos & Sergio Conde-Ocazionez & Miguel A L Nicolelis & Sidarta T Ribeiro & Adriano B L Tort, 2011. "Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0020996
    DOI: 10.1371/journal.pone.0020996
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    Cited by:

    1. Aswani Kumar Cherukuri & Radhika Shivhare & Ajith Abraham & Jinhai Li & Annapurna Jonnalagadda, 2021. "A Pragmatic Approach to Understand Hebbian Cell Assembly," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 60-82, April.
    2. Mark D Humphries & Javier A Caballero & Mat Evans & Silvia Maggi & Abhinav Singh, 2021. "Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-22, July.
    3. Dimitri Yatsenko & Krešimir Josić & Alexander S Ecker & Emmanouil Froudarakis & R James Cotton & Andreas S Tolias, 2015. "Improved Estimation and Interpretation of Correlations in Neural Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-28, March.
    4. Aswani Kumar Cherukuri & Radhika Shivhare & Ajith Abraham & Jinhai Li & Annapurna Jonnalagadda, 2021. "A Pragmatic Approach to Understand Hebbian Cell Assembly," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 73-95, April.
    5. Hu Lu & Shengtao Yang & Longnian Lin & Baoming Li & Hui Wei, 2013. "Prediction of Rat Behavior Outcomes in Memory Tasks Using Functional Connections among Neurons," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-11, September.
    6. Giovanni Diana & Thomas T J Sainsbury & Martin P Meyer, 2019. "Bayesian inference of neuronal assemblies," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-31, October.

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