2010 Special Issue: Complexity distribution as a measure for assembly size and temporal precision

Authors:
Sebastien Louis;Christian Borgelt;Sonja Grün
Affiliations:
RIKEN Brain Science Institute, Wako-Shi, Japan;European Centre for Soft Computing, Mieres, Asturias, Spain;RIKEN Brain Science Institute, Wako-Shi, Japan
Venue:
Neural Networks
Year:
2010

Citing 7
Cited 1

Discrete and Combinatorial Mathematics: An Applied Introduction

Discrete and Combinatorial Mathematics: An Applied Introduction
Higher-order statistics of input ensembles and the response of simple model neurons

Neural Computation
Information-geometric measure for neural spikes

Neural Computation
Spatially organized spike correlation in cat visual cortex

Neurocomputing
Identifying functional connectivity in large-scale neural ensemble recordings: A multiscale data mining approach

Neural Computation
A rate and history-preserving resampling algorithm for neural spike trains

Neural Computation
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains

Journal of Computational Neuroscience

Random bin for analyzing neuron spike trains

Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.