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Information Capacity of a Neural Network with Redundant Connections Between Neurons

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dc.date.accessioned 2019-01-22T20:53:25Z
dc.date.available 2019-01-22T20:53:25Z
dc.date.issued 2018
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/149280
dc.description.abstract © 2017 IEEE. In this work the model of a spiking recurrent neural network where any pair of neurons can form several connection lines (axons) with different spike propagation times is studied. Through simulation modeling, it has been shown that a neural network with redundant connections between neurons in the form of delay lines provides storage and playback of a significant number of independent temporal sequences of neural pulses. It has been suggested that multiple synaptic inputs from a single neuron in a natural neural network provide some of the information-processing properties of the network.
dc.subject cue pattern
dc.subject delay lines
dc.subject memory
dc.subject polychronization
dc.subject recurrent neural network
dc.subject redundant connections
dc.subject temporal sequences
dc.title Information Capacity of a Neural Network with Redundant Connections Between Neurons
dc.type Conference Paper
dc.relation.ispartofseries-volume 2018-January
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 16
dc.source.id SCOPUS-2018-2018-SID85046964273

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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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