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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)

This volume links field theory methods and concepts from particle physics with those in critical phenomena and statistical mechanics, the development starting from the latter point of view. Rigor and lengthy proofs are...

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)

- Daniel J Amit, Hanoch Gutfreund, H Sompolinsky
- 2011

The Hopfield model for a neural network is studied in the limit when the number p of stored patterns increases with the size N of the network, as p =Q.N. It is shown that, despite its spin-glass features, the model exhibits associative memory for a & n " u, & 0.14. This is a result of the existence at low temperature of 2p dynamically stable degenerate… (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)

- D J Amit, A Bernacchia, V Yakovlev
- Cerebral cortex
- 2003

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)

Interpreting recent single-unit recordings of delay activities in delayed match-to-sample experiments in anterior ventral temporal (AVT) cortex of monkeys in terms of reverberation dynamics, we present a model neural network of quasi-realistic elements that reproduces the empirical results in great detail. Information about the contiguity of successive… (More)