A new multineuron spike train metric

Authors:
Conor Houghton;Kamal Sen
Affiliations:
School of Mathematics, Trinity College Dublin, Dublin 2, Ireland, and Department of Biomedical Engineering, Boston University, Boston, MA 02115, U.S.A. houghton@maths.tcd.ie;Department of Biomedical Engineering, Boston University, Boston, MA 02115, U.S.A. kamalsen@bu.edu
Venue:
Neural Computation
Year:
2008

Citing 1
Cited 5

A Novel Spike Distance

Neural Computation

Information-geometric measures as robust estimators of connection strengths and external inputs

Neural Computation
2010 Special Issue: Optimization of population decoding with distance metrics

Neural Networks
Quantifying neurotransmission reliability through metrics-based information analysis

Neural Computation
An information-geometric framework for statistical inferences in the neural spike train space

Journal of Computational Neuroscience
Quantifying neurotransmission reliability through metrics-based information analysis

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Victor-Purpura spike train metric has recently been extended to a family of multineuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The metric is one of the two metrics commonly used for quantifying the distance between two spike trains; the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multineuron metric. We believe this gives a metric that is both natural and easy to calculate. Both types of multineuron metric are applied to simulated data and are compared.