Neural Comput - Randomly connected networks have short temporal memory.

Tópicos

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Resumo

The brain is easily able to process and categorize complex time-varying signals. For example, the two sentences, "It is cold in London this time of year" and "It is hot in London this time of year," have different meanings, even though the words hot and cold appear several seconds before the ends of the two sentences. Any network that can tell these sentences apart must therefore have a long temporal memory. In other words, the current state of the network must depend on events that happened several seconds ago. This is a difficult task, as neurons are dominated by relatively short time constants--tens to hundreds of milliseconds. Nevertheless, it was recently proposed that randomly connected networks could exhibit the long memories necessary for complex temporal processing. This is an attractive idea, both for its simplicity and because little tuning of recurrent synaptic weights is required. However, we show that when connectivity is high, as it is in the mammalian brain, randomly connected networks cannot exhibit temporal memory much longer than the time constants of their constituent neurons.

Resumo Limpo

brain easili abl process categor complex timevari signal exampl two sentenc cold london time year hot london time year differ mean even though word hot cold appear sever second end two sentenc network can tell sentenc apart must therefor long tempor memori word current state network must depend event happen sever second ago difficult task neuron domin relat short time constantsten hundr millisecond nevertheless recent propos random connect network exhibit long memori necessari complex tempor process attract idea simplic littl tune recurr synapt weight requir howev show connect high mammalian brain random connect network exhibit tempor memori much longer time constant constitu neuron

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