Learning in neural networks with material synapses
Neural Computation
Hebbian learning of context in recurrent neural networks
Neural Computation
Temporally asymmetric Hebbian learning, spike timing and neuronal response variability
Proceedings of the 1998 conference on Advances in neural information processing systems II
Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation
Neural Computation
Spike-Driven Synaptic Plasticity for Learning Correlated Patterns of Asynchronous Activity
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Creating hierarchical categories using cell assemblies
Connection Science
Optimizing one-shot learning with binary synapses
Neural Computation
Memory dynamics in attractor networks with saliency weights
Neural Computation
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The collective behavior of a network, modeling a cortical module of spiking neurons connected by plastic synapses is studied. A detailed spike-driven synaptic dynamics is simulated in a large network of spiking neurons, implementing the full double dynamics of neurons and synapses. The repeated presentation of a set of external stimuli is shown to structure the network to the point of sustaining working memory (selective delay activity). When the synaptic dynamics is analyzed as a function of pre- and postsynaptic spike rates in functionally defined populations, it reveals a novel variation of the Hebbian plasticity paradigm: in any functional set of synapses between pairs of neurons (e.g., stimulated-stimulated, stimulated-delay, stimulated-spontaneous), there is a finite probability of potentiation as well as of depression. This leads to a saturation of potentiation or depression at the level of the ratio of the two probabilities. When one of the two probabilities is very high relative to the other, the familiar Hebbian mechanism is recovered. But where correlated working memory is formed, it prevents overlearning. Constraints relevant to the stability of the acquired synaptic structure and the regimes of global activity allowing for structuring are expressed in terms of the parameters describing the single-synapse dynamics. The synaptic dynamics is discussed in the light of experiments observing precise spike timing effects and related issues of biological plausibility.