| Title |
|
|
|
Stability of asynchronous firing states in networks with synaptic adaptation
| |
| Author |
|
|
|
| |
| Abstract |
|
|
|
| We construct a mean field theory for low-rate asynchronous firing states in networks consisting of excitatory and inhibitory populations of integrate-and-fire neurons with synaptic depression or facilitation. The theory is exact when each neuron receives input from K randomly chosen ones, with 1 much less than K much less than N, where N is the total number of neurons. Changes in firing rates produce changes in synaptic strengths and vice-versa, potentially leading to instabilities. We prove that depression of synapses within a population (excitatory or inhibitory) always tends to stabilize the asynchronous state against such fluctuations, while depression acting between populations destabilizes it. Facilitation has the opposite effect. (C) 2001 Elsevier Science B.V. All rights reserved. |
| |
| Language |
|
|
|
English
| |
| Source (journal) |
|
|
|
Neurocomputing: an international journal. - Amsterdam | |
| Source (book) |
|
|
|
9th Annual Computational Neuroscience Meeting (CNS*00), JUL, 2000, BRUGGE, BELGIUM | |
| Publication |
|
|
|
Amsterdam : Elsevier science bv, 2001
| |
| ISSN |
|
|
|
0925-2312
| |
| Volume/pages |
|
|
|
38(2001), p. 915-920
| |
| ISI |
|
|
|
000169129200122
| |
| Full text (Publisher's DOI) |
|
|
| | |
|