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- Daniel J. Amit
- 1989

Following your need to always fulfil the inspiration to obtain everybody is now simple. Connecting to the internet is one of the short cuts to do. There are so many sources that offer and connect us to other world condition. As one of the products to see in internet, this website becomes a very available place to look for countless modeling brain function… (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)

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...

- Daniel J. Amit, Stefano Fusi
- Neural Computation
- 1994

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)

- Stefano Fusi, Mario Annunziato, Davide Badoni, Andrea Salamon, Daniel J. Amit
- Neural Computation
- 2000

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)

- Gianluigi Mongillo, Daniel J Amit, Nicolas Brunel
- The European journal of neuroscience
- 2003

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)

- D J Amit, N Brunel, M V Tsodyks
- The Journal of neuroscience : the official…
- 1994

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)

- D J Amit
- Proceedings of the National Academy of Sciences…
- 1988

It is shown that the ideas that led to neural networks capable of recalling associatively and asynchronously temporal sequences of patterns can be extended to produce a neural network that automatically counts the cardinal number in a sequence of identical external stimuli. The network is explicitly constructed, analyzed, and simulated. Such a network may… (More)

- Daniel J. Amit
- 1995

A learning attractor neural network (LANN) with a double dynamics of neural activities and synaptic eecacies, operating on two diierent time scales is studied by simulations in preparation for an electronic implementation. The present network includes several quasi-realistic features: neurons are represented by their aaerent currents and output spike rates;… (More)