Application of Poisson-based hidden Markov models to in-vitro neuronal data

Dimitris Xydas, Matthew C. Spencer, Julia H. Downes, Mark W. Hammond, Victor M. Becerra, Kevin Warwick, Benjamin J. Whalley, Slawomir J. Nasuto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Recent advances in electrophysiological techniques have made it possible to culture in vitro biological networks and closely monitor ensemble neuronal activity using multi-electrode recording techniques. One of the main challenges in this area of research is attempting to understand how intrinsic activity is propagated within these neuronal networks and how it may be manipulated via external stimuli in order to harness their computational capacity. This raises the question of what similarities and differences arise between spontaneous and evoked responses and how external stimulation can be optimally applied in order to robustly control the neuronal plasticity of neuronal cultures. In this paper we present in detail an application of machine learning methods, specifically hidden Markov models with Poisson-based output distributions, with which we aim to perform comparative studies between spontaneous and evoked neuronal activity over different ages of network development.
    Original languageEnglish
    Title of host publication2010 IEEE 9th International Conference on Cybernetic Intelligent Systems (CIS)
    Place of PublicationPiscataway
    PublisherIEEE
    Pages1-6
    ISBN (Electronic)9781424490240
    ISBN (Print)9781424490233
    DOIs
    Publication statusPublished - 2010

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