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Revealing cell assemblies at multiple levels of granularity

Billeh, Yazan N. and Schaub, Michael T. and Anastassiou, Costas A. and Barahona, Mauricio and Koch, Christof (2014) Revealing cell assemblies at multiple levels of granularity. Journal of Neuroscience Methods, 236 . pp. 92-106. ISSN 0165-0270. http://resolver.caltech.edu/CaltechAUTHORS:20140904-101746131

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Abstract

Background: Current neuronal monitoring techniques, such as calcium imaging and multi-electrode arrays, enable recordings of spiking activity from hundreds of neurons simultaneously. Of primary importance in systems neuroscience is the identification of cell assemblies: groups of neurons that cooperate in some form within the recorded population. New method: We introduce a simple, integrated framework for the detection of cell-assemblies from spiking data without a priori assumptions about the size or number of groups present. We define a biophysically-inspired measure to extract a directed functional connectivity matrix between both excitatory and inhibitory neurons based on their spiking history. The resulting network representation is analyzed using the Markov Stability framework, a graph theoretical method for community detection across scales, to reveal groups of neurons that are significantly related in the recorded time-series at different levels of granularity. Results and comparison with existing methods: Using synthetic spike-trains, including simulated data from leaky-integrate-and-fire networks, our method is able to identify important patterns in the data such as hierarchical structure that are missed by other standard methods. We further apply the method to experimental data from retinal ganglion cells of mouse and salamander, in which we identify cell-groups that correspond to known functional types, and to hippocampal recordings from rats exploring a linear track, where we detect place cells with high fidelity. Conclusions: We present a versatile method to detect neural assemblies in spiking data applicable across a spectrum of relevant scales that contributes to understanding spatio-temporal information gathered from systems neuroscience experiments.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.jneumeth.2014.08.011DOIArticle
http://www.sciencedirect.com/science/article/pii/S0165027014002957PublisherArticle
ORCID:
AuthorORCID
Koch, Christof0000-0001-6482-8067
Additional Information:© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Received 29 April 2014. Received in revised form 6 August 2014. Accepted 8 August 2014. Available online 26 August 2014. C onflict of interest: None declared. YNB acknowledges support from the William H. Pickering Fellowship. MTS acknowledges support from the Studienstiftung des deutschen Volkes and a Santander Mobility Award. MTS and MB acknowledge support through a grant to MB from the Engineering and Physical Sciences Research Council (EPSRC) of the UK under the Mathematics underpinning the Digital Economy program. YNB, CAA, and CK thank the G. Harold & Leila Y. Mathers Foundation. CAA acknowledges support from the Swiss National Science Foundation (SNSF) and the Human Frontier Sciences Program (HFSP). CAA and YNB thank the National Institute of Neurological Disorders and Stroke (NINDS). CAA and CK thank the Allen Institute for Brain Science. We thank M. Meister and H. Asari for the retinal ganglion cell data, K. Diba for the hippocampal data, and A. Shai, J. Taxidis, E. Schomburg, and S. Mihalas for discussions.
Group:Koch Laboratory, KLAB
Funders:
Funding AgencyGrant Number
William H. Pickering FellowshipUNSPECIFIED
Studienstiftung des deutschen VolkesUNSPECIFIED
Santander Mobility AwardUNSPECIFIED
Engineering and Physical Sciences Research Council (EPSRC)UNSPECIFIED
Swiss National Science Foundation (SNSF)UNSPECIFIED
Human Frontiers Science ProgramUNSPECIFIED
G. Harold and Leila Y. Mathers Charitable FoundationUNSPECIFIED
National Institute of Neurological Disorders and Stroke (NINDS)UNSPECIFIED
Allen Institute for Brain ScienceUNSPECIFIED
Subject Keywords:Biophysically inspired and directed spike-train association metric; Spike-train communities; Hierarchical clustering; Community detection
Record Number:CaltechAUTHORS:20140904-101746131
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20140904-101746131
Official Citation:Yazan N. Billeh, Michael T. Schaub, Costas A. Anastassiou, Mauricio Barahona, Christof Koch, Revealing cell assemblies at multiple levels of granularity, Journal of Neuroscience Methods, Volume 236, 30 October 2014, Pages 92-106, ISSN 0165-0270, http://dx.doi.org/10.1016/j.jneumeth.2014.08.011. (http://www.sciencedirect.com/science/article/pii/S0165027014002957)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:49235
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:04 Sep 2014 18:57
Last Modified:14 Jan 2016 23:36

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