Combining ESOMs trained on a hierarchy of feature subsets for single-trial decoding of LFP responses in monkey area V4

  • Authors:

  • Nikolay V. Manyakov;Jonas Poelmans;Rufin Vogels;Marc M. Van Hulle

  • Affiliations:

  • Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium;Faculty of Business and Economics, K.U.Leuven, Leuven, Belgium;Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium;Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium

  • Venue:
  • ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
  • Year:
  • 2010

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Abstract

We develop and combine topographic maps trained on different combinations of feature subsets for visualizing and classifying event-related responses recorded with a multi-electrode array chronically implanted in the visual cortical area V4 of a rhesus monkey. The monkey was trained, during consecutive training sessions, in a classical conditioning paradigm in which one stimulus was consistently paired with a fluid reward and another stimulus not. We opted for features from three categories: time-frequency analysis, phase synchronization between electrodes, and propagating waves in the array. The Emergent Self Organizing Map (ESOM) was used to explore the feasibility of single-trial decoding. Since the effective dimensionality of the feature space is rather high, a series of ESOMs was trained on features selected from different combinations of the three feature categories. For each trained ESOM, a classifier was developed, and classifiers of different ESOMs were combined so as to maximize the single-trial decoding performance.