Decoding population neuronal responses by topological clustering

Hujun Yin, Stefano Panzeri, Zareen Mehboob, Mathew Diamond

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    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. © 2008 Springer-Verlag Berlin Heidelberg.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    Place of PublicationBerlin
    PublisherSpringer Nature
    Number of pages9
    ISBN (Print)3540875581, 9783540875581
    Publication statusPublished - 2008
    Event18th International Conference on Artificial Neural Networks, ICANN 2008 - Prague
    Duration: 1 Jul 2008 → …


    Conference18th International Conference on Artificial Neural Networks, ICANN 2008
    Period1/07/08 → …


    • Barrel cortex
    • Clustering
    • Mutual information
    • Self-organising maps
    • Spike trains


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