Persistent Activity in Neural Networks with Dynamic Synapses

Persistent Activity in Neural Networks with Dynamic Synapses

Persistent activity states (attractors), observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells i...

Correction: Persistent Activity in Neural Networks with Dynamic Synapses

1 À Jux ¼ ÀJð xd þ ueÞ de dt ¼ a þ b xd þ ue dd dt ¼ Àc À d xd þ ue a ¼ 1 À x t r .0 b ¼ I J 2 .0 c ¼ u À U t f .0 d ¼ UIð1 À uÞ J .0

Neural Networks with Dynamic Synapses

Transmission across neocortical synapses depends on the frequency of presynaptic activity (Thomson & Deuchars, 1994). Interpyramidal synapses in layer V exhibit fast depression of synaptic transmission, while other types of synapses exhibit facilitation of transmission. To study the role of dynamic synapses in network computation, we propose a unified phenomenological model that allows computat...

Pulsed Neural Networks: Temporal Signal Processing Using Artificial Neural Networks with Dynamic Synapses

Learning in Neural Networks with Material Synapses

We discuss the long term maintenance of acquired memory in synaptic connections of a perpetually learning electronic device. This is affected by ascribing each synapse a finite number of stable states in which it can maintain for indefinitely long periods. Learning uncorrelated stimuli is expressed as a stochastic process produced by the neural activities on the synapses. In several interesting...