Encoding the Temporal Statistics of Markovian Sequences of Stimuli in Recurrent Neuronal Networks View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002-08-21

AUTHORS

Alessandro Usseglio Viretta , Stefano Fusi , Shih-Chii Liu

ABSTRACT

Encoding, storing, and recalling a temporal sequence of stimuli in a neuronal network can be achieved by creating associations between pairs of stimuli that are contiguous in time. This idea is illustrated by studying the behavior of a neural network model with binary neurons and binary stochastic synapses. The network extracts in an unsupervised manner the temporal statistics of the sequence of input stimuli. When a stimulus triggers the recalling process, the statistics of the output patterns reflects those of the input. If the sequence of stimuli is generated through a Markov process, then the network dynamics faithfully reproduces all the transition probabilities. More... »

PAGES

204-209

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-46084-5_34

DOI

http://dx.doi.org/10.1007/3-540-46084-5_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048851444


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