ArticlePLoS computational biology2013
Optimal properties of analog perceptrons with excitatory weights.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Purkinje cell models: past, present and future.Frontiers in computational neuroscience · 2024Review
- Article
- A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.Frontiers in neuroscience · 2022Article
- Optimal learning with excitatory and inhibitory synapses.PLoS computational biology · 2020Article
- Article
- Cerebellar learning using perturbations.eLife · 2018Article
- Article
- Purkinje cell intrinsic excitability increases after synaptic long term depression.Journal of neurophysiology · 2016Article
- Is cortical connectivity optimized for storing information?Nature neuroscience · 2016Article
- A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.PLoS computational biology · 2015Article
- Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules.PLoS computational biology · 2015Article
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2 authors.
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Abstract
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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