title: Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models.
abstract: In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli.
authors.affiliation: Cybernetics Research Group, School of Systems Engineering, University of Reading, RG6 6AY Reading, UK. d.xydas@pgr.reading.ac.uk
authors.forename: Dimitris
authors.initials: D
authors.lastname: Xydas
authors.forename: Julia H
authors.initials: JH
authors.lastname: Downes
authors.forename: Matthew C
authors.initials: MC
authors.lastname: Spencer
authors.forename: Mark W
authors.initials: MW
authors.lastname: Hammond
authors.forename: Slawomir J
authors.initials: SJ
authors.lastname: Nasuto
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Whalley
authors.forename: Victor M
authors.initials: VM
authors.lastname: Becerra
authors.forename: Kevin
authors.initials: K
authors.lastname: Warwick
mesh_terms: D000465:Algorithms; D002478:Cells, Cultured; D002755:Choice Behavior; D004566:Electrodes; D008390:Markov Chains; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D014584:User-Computer Interface