Name
Abstract | ||
|---|---|---|
The dynamics of a network of randomly connected inhibitory linear integrate and fire (LIF) neurons (with a floor for the depolarization), in the presence of stochastic external afferent input, is considered in various parameter regimes of the neurons and of the network. Applying a technique recently introduced by Brunel and Hakim, we classify the regimes in which such a network has stable stationary states and in which spike emission rates oscillate. In the vicinity of the bifurcation line, the oscillation frequency and its amplitude are computed and compared with simulations. As for leaky IF neurons, the space of parameters can be compacted into two. Yet despite significant technical differences between the two models, related to both the different dynamics of the depolarization as well as to the different boundary conditions, the qualitative behavior is rather similar. The significance of LIF neurons and of the differences with leaky IF neurons is discussed. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1023/A:1013775115140 | Journal of Computational Neuroscience |
Keywords | Field | DocType |
spiking neuron,recurrent networks,collective oscillations | Boundary value problem,Oscillation,Control theory,Inhibitory postsynaptic potential,Artificial intelligence,Depolarization,Spiking neural network,Amplitude,Stationary state,Machine learning,Bifurcation,Physics | Journal |
Volume | Issue | ISSN |
11 | 3 | 0929-5313 |
Citations | PageRank | References |
4 | 0.79 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianluigi Mongillo | 1 | 85 | 9.03 |
| D J Amit | 2 | 4 | 0.79 |