Complexity distribution as a measure for assembly size and temporal precision

The efficient detection of higher-order synchronization in massively parallel data is of great importance in understanding computational processes in the cortex and represents a significant statistical challenge. To overcome the combinatorial explosion of different spike patterns taking place as the...

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Bibliographic Details
Published in:Neural networks 2010-08, Vol.23 (6), p.705-712
Main Authors: Louis, Sebastien, Borgelt, Christian, Grün, Sonja
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Animals
Assembly
Cerebral Cortex - cytology
Cerebral Cortex - physiology
Combinatorial analysis
Complexity
Computer Simulation
Electrophysiology - methods
Electrophysiology - trends
Higher-order correlation
Humans
Massively parallel spike trains
Models, Neurological
Nerve Net - physiology
Neural networks
Neurons
Neurons - physiology
Neurophysiology - methods
Neurophysiology - trends
Population (statistical)
Random Allocation
Signal Processing, Computer-Assisted
Spike synchrony
Spikes
Temporal logic
Time Factors
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Summary:The efficient detection of higher-order synchronization in massively parallel data is of great importance in understanding computational processes in the cortex and represents a significant statistical challenge. To overcome the combinatorial explosion of different spike patterns taking place as the number of neurons increases, a method based on population measures would prove very useful. Following previous work in this direction, we examine the distribution of spike counts across neurons per time bin (‘complexity distribution’) and devise a method to reliably extract the size and temporal precision of synchronous groups of neurons, even in the presence of strong rate covariations.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2010.05.004