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.
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Manyakov, N.V., Poelmans, J., Vogels, R., Van Hulle, M.M. (2010). Combining ESOMs Trained on a Hierarchy of Feature Subsets for Single-Trial Decoding of LFP Responses in Monkey Area V4. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_67
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DOI: https://doi.org/10.1007/978-3-642-13232-2_67
Publisher Name: Springer, Berlin, Heidelberg
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