Int J Neural Syst - Optimal sparse approximation with integrate and fire neurons.

Tópicos

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Resumo

Sparse approximation is a hypothesized coding strategy where a population of sensory neurons (e.g. V1) encodes a stimulus using as few active neurons as possible. We present the Spiking LCA (locally competitive algorithm), a rate encoded Spiking Neural Network (SNN) of integrate and fire neurons that calculate sparse approximations. The Spiking LCA is designed to be equivalent to the nonspiking LCA, an analog dynamical system that converges on a l(1)-norm sparse approximations exponentially. We show that the firing rate of the Spiking LCA converges on the same solution as the analog LCA, with an error inversely proportional to the sampling time. We simulate in NEURON a network of 128 neuron pairs that encode 8 ? 8 pixel image patches, demonstrating that the network converges to nearly optimal encodings within 20 ms of biological time. We also show that when using more biophysically realistic parameters in the neurons, the gain function encourages additional l(0)-norm sparsity in the encoding, relative both to ideal neurons and digital solvers.

Resumo Limpo

spars approxim hypothes code strategi popul sensori neuron eg v encod stimulus use activ neuron possibl present spike lca local competit algorithm rate encod spike neural network snn integr fire neuron calcul spars approxim spike lca design equival nonspik lca analog dynam system converg lnorm spars approxim exponenti show fire rate spike lca converg solut analog lca error invers proport sampl time simul neuron network neuron pair encod pixel imag patch demonstr network converg near optim encod within ms biolog time also show use biophys realist paramet neuron gain function encourag addit lnorm sparsiti encod relat ideal neuron digit solver

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