Name
Title | ||
|---|---|---|
Firing statistics of inhibitory neuron with delayed feedback. II: Non-Markovian behavior. |
Abstract | ||
|---|---|---|
The instantaneous state of a neural network consists of both the degree of excitation of each neuron the network is composed of and positions of impulses in communication lines between the neurons. In neurophysiological experiments, the neuronal firing moments are registered, but not the state of communication lines. But future spiking moments depend essentially on the past positions of impulses in the lines. This suggests, that the sequence of intervals between firing moments (inter-spike intervals, ISIs) in the network could be non-Markovian. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.biosystems.2013.02.002 | Biosystems |
Keywords | Field | DocType |
Inhibitory neuron,Delayed feedback,Poisson process,Interspike intervals probability density,Non-Markovian stochastic process | Markov process,Control theory,Inhibitory postsynaptic potential,Artificial intelligence,Poisson distribution,Artificial neural network,Neuron,Combinatorics,Markov chain,Excitatory postsynaptic potential,Machine learning,Conditional probability density,Mathematics | Journal |
Volume | Issue | ISSN |
112 | 3 | 0303-2647 |
Citations | PageRank | References |
2 | 0.39 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. G. Kravchuk | 1 | 5 | 0.82 |
| Alexander K. Vidybida | 2 | 30 | 3.89 |