Decoding Population Neuronal Responses by Topological Clustering

Authors:
Hujun Yin;Stefano Panzeri;Zareen Mehboob;Mathew Diamond
Affiliations:
School of Electrical and Electronic Engineering, University of Manchester, UK;School of Life Sciences, University of Manchester, Manchester, UK and IIT, Genova, Italy 16163;School of Electrical and Electronic Engineering, University of Manchester, UK;Italian Institute of Technology, SISSA unit, Trieste, Italy
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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Abstract

In this paper the use of topological clustering for decoding population neuronal responses and reducing stimulus features is described. The discrete spike trains, recorded in rat somatosensory cortex in response to sinusoidal vibrissal stimulations characterised by different frequencies and amplitudes, are first interpreted to continuous temporal activities by convolving with a decaying exponential filter. Then the self-organising map is utilised to cluster the continuous responses. The result is a topologically ordered clustering of the responses with respect to the stimuli. The clustering is formed mainly along the product of amplitude and frequency of the stimuli. Such grouping agrees with the energy coding result obtained previously based on spike counts and mutual information. To further investigate how the clustering preserves information, the mutual information between resulting stimulus grouping and responses has been calculated. The cumulative mutual information of the clustering resembles closely that of the energy grouping. It suggests that topological clustering can naturally find underlying stimulus-response patterns and preserve information among the clusters.