Neural Comput - Information recall using relative spike timing in a spiking neural network.

Tópicos

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Resumo

We present a neural network that is capable of completing and correcting a spiking pattern given only a partial, noisy version. It operates in continuous time and represents information using the relative timing of individual spikes. The network is capable of correcting and recalling multiple patterns simultaneously. We analyze the network's performance in terms of information recall. We explore two measures of the capacity of the network: one that values the accurate recall of individual spike times and another that values only the presence or absence of complete patterns. Both measures of information are found to scale linearly in both the number of neurons and the period of the patterns, suggesting these are natural measures of network information. We show a smooth transition from encodings that provide precise spike times to flexible encodings that can encode many scenes. This makes it plausible that many diverse tasks could be learned with such an encoding.

Resumo Limpo

present neural network capabl complet correct spike pattern given partial noisi version oper continu time repres inform use relat time individu spike network capabl correct recal multipl pattern simultan analyz network perform term inform recal explor two measur capac network one valu accur recal individu spike time anoth valu presenc absenc complet pattern measur inform found scale linear number neuron period pattern suggest natur measur network inform show smooth transit encod provid precis spike time flexibl encod can encod mani scene make plausibl mani divers task learn encod

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