Optimal properties of analog perceptrons with excitatory weights.

Published

Journal Article

The cerebellum is a brain structure which has been traditionally devoted to supervised learning. According to this theory, plasticity at the Parallel Fiber (PF) to Purkinje Cell (PC) synapses is guided by the Climbing fibers (CF), which encode an 'error signal'. Purkinje cells have thus been modeled as perceptrons, learning input/output binary associations. At maximal capacity, a perceptron with excitatory weights expresses a large fraction of zero-weight synapses, in agreement with experimental findings. However, numerous experiments indicate that the firing rate of Purkinje cells varies in an analog, not binary, manner. In this paper, we study the perceptron with analog inputs and outputs. We show that the optimal input has a sparse binary distribution, in good agreement with the burst firing of the Granule cells. In addition, we show that the weight distribution consists of a large fraction of silent synapses, as in previously studied binary perceptron models, and as seen experimentally.

Full Text

Duke Authors

Cited Authors

  • Clopath, C; Brunel, N

Published Date

  • 2013

Published In

Volume / Issue

  • 9 / 2

Start / End Page

  • e1002919 -

PubMed ID

  • 23436991

Pubmed Central ID

  • 23436991

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1002919

Language

  • eng

Conference Location

  • United States