Clustering Neural Spike Trains with Transient Responses

Authors

John D. Hunter, University of ChicagoFollow
Jianhong Wu, York UniversityFollow
John Milton, Claremont McKenna College; Pitzer College; Scripps CollegeFollow

Document Type

Conference Proceeding

Department

WM Keck Science

Publication Date

12-9-2008

Abstract

The detection of transient responses, i.e. nonstationarities, that arise in a varying and small fraction of the total number of neural spike trains recorded from chronically implanted multielectrode grids becomes increasingly difficult as the number of electrodes grows. This paper presents a novel application of an unsupervised neural network for clustering neural spike trains with transient responses. This network is constructed by incorporating projective clustering into an adaptive resonance type neural network (ART) architecture resulting in a PART neural network. Since comparisons are made between inputs and learned patterns using only a subset of the total number of available dimensions, PART neural networks are ideally suited to the detection of transients. We show that PART neural networks are an effective tool for clustering neural spike trains that is easily implemented, computationally inexpensive, and well suited for detecting neural responses to dynamic environmental stimuli.

Comments

Published conference presentation.

Previously linked to as: http://ccdl.libraries.claremont.edu/u?/irw,348

Source: Publisher's pdf.

This article can also be found at http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4738560&arnumber=4738729&count=950&index=165

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Recommended Citation

Hunter, J.D., J. Wu, and J.G. Milton. "Clustering neural spike trains with transient responses." Proceedings of the 47th IEEE Conference on Decision and Control (9-11 December 2008): 2000-2005. DOI: 10.1109/CDC.2008.4738729