Title of article :
Model architecture for associative memory in a neural network of spiking neurons
Author/Authors :
Agnes، نويسنده , , Everton J. and Erichsen Jr.، نويسنده , , Rubem and Brunnet، نويسنده , , Leonardo G.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
6
From page :
843
To page :
848
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.
Keywords :
NEURAL NETWORKS , Chemical synapses , GAP JUNCTIONS , Map-based neuron , Neural coding , Principal component analysis
Journal title :
Physica A Statistical Mechanics and its Applications
Serial Year :
2012
Journal title :
Physica A Statistical Mechanics and its Applications
Record number :
1734957
Link To Document :
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