Neural Comput - A network of spiking neurons for computing sparse representations in an energy-efficient way.

Tópicos

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Resumo

Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/vt for gaussian white noise.

Resumo Limpo

comput spars redund represent import problem appli mathemat neurosci mani applic problem must solv energyeffici way propos hybrid distribut algorithm hda solv problem network simpl node communic lowbandwidth channel hda node perform gradientdescentlik step analog intern variabl coordinatedescentlik step via quantiz extern variabl communic interest oper equival network integrateandfir neuron suggest hda may serv model neural comput show numer perform hda par exist algorithm asymptot regim represent error hda decay time t t hda stabl timevari nois specif represent error decay vt gaussian white nois

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