Name
Abstract | ||
|---|---|---|
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 selective delay activity. When the synaptic dynamics is analyzed as a function of pre- and post-synaptic 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 - (stimulated-stimulated; stimulated-delay; stimulated-spontaneous etc...) 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, preventing the uncontrolled growth of the number of potentiated synapses. When one of the two probabilities is very high relative to the other, the familiar Hebbian mechanism is recovered. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-46084-5_23 | ICANN |
Keywords | Field | DocType |
cortical module,collective behavior,external stimulus,plastic cortical network,synaptic dynamic,detailed spike-driven synaptic dynamic,hebbian plasticity paradigm,spiking neuron,functional set,familiar hebbian mechanism,large network,network model | Long-term potentiation,Synapse,Neuronal memory allocation,Computer science,Recurrent neural network,Hebbian theory,Non-spiking neuron,Artificial intelligence,Stimulus (physiology),Spiking neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
2415 | 0302-9743 | 3-540-44074-7 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianluigi Mongillo | 1 | 85 | 9.03 |
| Daniel J. Amit | 2 | 9 | 4.36 |