Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains

In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentia...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2015-01, Vol.2015 (2015), p.525-536
Main Authors: Picado Muiño, David, Borgelt, Christian
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Assembly
Automatic Data Processing
Combinatorial analysis
Electrophysiology - methods
Explosions
Models, Neurological
Monitors
Nerve Net - physiology
Neural circuitry
Neurobiology
Neurons
Neurons - physiology
Neurosciences
Performance evaluation
Recording
Spikes
Statistical models
Statistical tests
Tasks
Trains
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Summary:In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which—if it can be demonstrated reliably—would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information).
ISSN:1687-5265
1687-5273
DOI:10.1155/2015/427829