2013 Articles
Optimal Properties of Analog Perceptrons with Excitatory Weights
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.
Subjects
Files
- pcbi.1002919.pdf application/pdf 341 KB Download File
Also Published In
- Title
- PLOS Computational Biology
- DOI
- https://doi.org/10.1371/journal.pcbi.1002919
More About This Work
- Academic Units
- Neuroscience
- Published Here
- July 10, 2013