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Model architecture for associative memory in a neural network of spiking neurons

Agnes, Everton J. and Erichsen Jr, Rubem and Brunnet, Leonardo G.. (2012) Model architecture for associative memory in a neural network of spiking neurons. Physica. A, Theoretical and statistical physics, 391 (3). pp. 843-848.

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Official URL: https://edoc.unibas.ch/79131/

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Abstract

A synaptic connectivity model is assembled on a spiking neuron network aiming to build up a dynamic pattern recognition system. The connection architecture includes gap junctions and both inhibitory and excitatory chemical synapses based on Hebb’s hypothesis. The network evolution resulting from external stimulus is sampled in a properly defined frequency space. Neurons’ responses to different current injections are mapped onto a subspace using Principal Component Analysis. Departing from the base attractor, related to a quiescent state, different external stimuli drive the network to different fixed points through specific trajectories in this subspace.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Neurobiology
UniBasel Contributors:Agnes, Everton Joao
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:North-Holland
ISSN:0378-4371
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:13 Nov 2020 08:32
Deposited On:13 Nov 2020 08:32

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