Abstract.
Many recent approaches to decoding neural spike trains depend critically on the assumption that for low-pass filtered spike trains, the temporal structure is optimally represented by a small number of linear projections onto the data. We therefore tested this assumption of linearity by comparing a linear factor analysis technique (principal components analysis) with a nonlinear neural network based method. It is first shown that the nonlinear technique can reliably identify a neuronally plausible nonlinearity in synthetic spike trains. However, when applied to the outputs from primary visual cortical neurons, this method shows no evidence for significant temporal nonlinearities. The implications of this are discussed.
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Received: 29 November 1996 / Accepted in revised form: 1 July 1997
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Fotheringhame, D., Baddeley, R. Nonlinear principal components analysis of neuronal spike train data . Biol Cybern 77, 283–288 (1997). https://doi.org/10.1007/s004220050389
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DOI: https://doi.org/10.1007/s004220050389