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Addison-Wesley holds the copyright on this material, but gives permission for free individual use. This does not cover any form of redistribution for sale or proot. Contact Addison-Wesley directly (e-mail D4065@applelink.apple.com) for more information. The price is $29.95 paperbound (ISBN 0{201{51560{1) or $44.25 hardbound (ISBN 0{201{50395{6). Please(More)
We investigate self-sustaining stable states (attractors) in networks of integrate-and-fire neurons. First, we study the stability of spontaneous activity in an unstructured network. It is shown that the stochastic background activity, of 1-5 spikes/s, is unstable if all neurons are excitatory. On the other hand, spontaneous activity becomes(More)
Extensive simulations of large recurrent networks of integrate-and-fire excitatory and inhibitory neurons in realistic cortical conditions (before and after Hebbian unsupervised learning of uncorrelated stimuli) exhibit a rich phenomenology of stochastic neural spike dynamics and, in particular, coexistence between two types of stable states: spontaneous(More)
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(More)
Recordings from cells in the associative cortex of monkeys performing visual working memory tasks link persistent neuronal activity, long-term memory and associative memory. In particular, delayed pair-associate tasks have revealed neuronal correlates of long-term memory of associations between stimuli. Here, a recurrent cortical network model with Hebbian(More)
We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short timescales and preserves the memory of two stable states (efficacies) on long timescales. The transitions (LTP/LTD) are stochastic because both the number and the distribution of neural spikes in any finite(More)
It is shown that a simple modification of synaptic structures (of the Hop­ field type) constructed to produce auto-associative attractors, produces neural networks whose attractors are correlated with several (learned) patterns used in the construction of the matrix. The modification stores in the matrix a fixed sequence ofuncorrelated pattems. The network(More)
In a psychophysics experiment, monkeys were shown a sequence of two to eight images, randomly chosen out of a set of 16, each image followed by a delay interval, the last image in the sequence being a repetition of any (one) of the images shown in the sequence. The monkeys learned to recognize the repetition of an image. The performance level was studied as(More)