Name
Abstract | ||
|---|---|---|
There is mounting experimental evidence that the nervous system utilizes neural noise to improve sensory signal transmission. Here, we investigate the response properties of a noisy neuron using an integrate-fire model. When the neuron is driven by periodic input, noise optimally improves the signal-to-noise ratio of the elicited spike train, if the driving frequency is in a certain range. This phenomenon, called bona fide stochastic resonance, is analyzed in a Markov chain formalism which avoids implausible assumptions made in earlier studies. The bandpass property of the transmission function of the neuron may explain why certain oscillation frequencies are prevalent in cortex. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0925-2312(99)00076-4 | Neurocomputing |
Keywords | Field | DocType |
Integrate-fire neuron,Stochastic resonance,Ornstein–Uhlenbeck process,Markov chain | Transmission (telecommunications),Oscillation,Control theory,Artificial intelligence,Stochastic resonance,Sensory system,Topology,Spike train,Band-pass filter,Pattern recognition,Markov chain,Ornstein–Uhlenbeck process,Mathematics | Journal |
Volume | ISSN | Citations |
26-27 | 0925-2312 | 3 |
PageRank | References | Authors |
0.78 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hans E Plesser | 1 | 133 | 20.47 |
| Theo Geisel | 2 | 314 | 40.09 |