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Optimal properties of analog perceptrons with excitatory weights.

Publication ,  Journal Article
Clopath, C; Brunel, N
Published in: PLoS Comput Biol
2013

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.

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Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

2013

Volume

9

Issue

2

Start / End Page

e1002919

Location

United States

Related Subject Headings

  • Synapses
  • Purkinje Cells
  • Neuronal Plasticity
  • Neural Networks, Computer
  • Nerve Fibers
  • Models, Neurological
  • Mice
  • Computer Simulation
  • Bioinformatics
  • Animals
 

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Clopath, C., & Brunel, N. (2013). Optimal properties of analog perceptrons with excitatory weights. PLoS Comput Biol, 9(2), e1002919. https://doi.org/10.1371/journal.pcbi.1002919
Clopath, Claudia, and Nicolas Brunel. “Optimal properties of analog perceptrons with excitatory weights.PLoS Comput Biol 9, no. 2 (2013): e1002919. https://doi.org/10.1371/journal.pcbi.1002919.
Clopath C, Brunel N. Optimal properties of analog perceptrons with excitatory weights. PLoS Comput Biol. 2013;9(2):e1002919.
Clopath, Claudia, and Nicolas Brunel. “Optimal properties of analog perceptrons with excitatory weights.PLoS Comput Biol, vol. 9, no. 2, 2013, p. e1002919. Pubmed, doi:10.1371/journal.pcbi.1002919.
Clopath C, Brunel N. Optimal properties of analog perceptrons with excitatory weights. PLoS Comput Biol. 2013;9(2):e1002919.

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

2013

Volume

9

Issue

2

Start / End Page

e1002919

Location

United States

Related Subject Headings

  • Synapses
  • Purkinje Cells
  • Neuronal Plasticity
  • Neural Networks, Computer
  • Nerve Fibers
  • Models, Neurological
  • Mice
  • Computer Simulation
  • Bioinformatics
  • Animals