Toward data representation with spiking neurons

Michael Gutmann, Kazuyuki Aihara

Research output: Contribution to journalArticlepeer-review

Abstract

Notable advances in the understanding of neural processing were made when sensory systems were investigated from the viewpoint of adaptation to the statistical structure of their input space. For this purpose, mathematical methods for data representation were used. Here, we point out that emphasis on the input structure has been at the cost of the biological plausibility of the corresponding neuron models which process the natural stimuli. The signal transformation of the data representation methods does not correspond well to the signal transformations happening at the single-cell level in neural systems. Hence, we now propose data representation by means of spiking neuron models. We formulate the data representation problem as an optimization problem and derive the fundamental quantities for an iterative learning scheme.
Original languageEnglish
Pages (from-to)223-226
Number of pages4
JournalArtificial Life and Robotics
Volume12
Issue number1
DOIs
Publication statusPublished - 2008

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