Optimal properties of analog perceptrons with excitatory weights.
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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.
Neural Networks (Computer)
Published Version (Please cite this version)10.1371/journal.pcbi.1002919
Publication InfoClopath, C; & Brunel, Nicolas (2013). Optimal properties of analog perceptrons with excitatory weights. PLoS Comput Biol, 9(2). pp. e1002919. 10.1371/journal.pcbi.1002919. Retrieved from https://hdl.handle.net/10161/15125.
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Professor of Neurobiology
We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity of a network that maximizes storage capacity reproducestwo key experimentally observed features: low connection proba