Distributed synaptic weights in a LIF neural network and learning rules
| Title: | Distributed synaptic weights in a LIF neural network and learning rules |
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| Authors: | Perthame, Benoît, Salort, Delphine, Wainrib, Gilles |
| Publication Year: | 2017 |
| Collection: | Mathematics Quantitative Biology |
| Subject Terms: | Quantitative Biology - Neurons and Cognition, Mathematics - Analysis of PDEs |
| More Details: | Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connec-tivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorized a learned signal. Comment: Physica D: Nonlinear Phenomena, Elsevier, 2017 |
| Document Type: | Working Paper |
| DOI: | 10.1016/j.physd.2017.05.005 |
| Access URL: | http://arxiv.org/abs/1706.05796 |
| Accession Number: | edsarx.1706.05796 |
| Database: | arXiv |
| DOI: | 10.1016/j.physd.2017.05.005 |
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