Name
Abstract | ||
|---|---|---|
We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions of the local fields felt by neurons--akin to electrical stimulation--and of the global average field--representing total energy consumption. We find that initial degree distributions then evolve towards stationary states which can either be fairly homogeneous or highly heterogeneous, depending on parameters. The critical cases--which can result in scale-free distributions--are shown to correspond, under a mean-field approximation, to nonlinear drift-diffusion equations. We show how appropriate choices of parameters yield good quantitative agreement with published experimental data concerning synaptic densities during brain development (synaptic pruning). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02478-8_29 | IWANN (1) |
Keywords | Field | DocType |
drift-diffusion equation,general nonlinear function,bio-inspired probability,biological mechanisms,critical case,appropriate choice,synaptic density,neural network structure,electrical stimulation,brain development,experimental data,synaptic pruning,mean field approximation,scale free,neural network,local field,stationary state | Brain development,Statistical physics,Mathematical optimization,Nonlinear system,Simulation,Computer science,Homogeneous,Mechanism (biology),Artificial neural network,Stationary state,Energy consumption,Synaptic pruning | Conference |
Volume | ISSN | Citations |
5517 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Johnson | 1 | 4 | 1.23 |
| Joaquín Marro | 2 | 11 | 3.75 |
| Jorge F. Mejías | 3 | 38 | 5.30 |
| Joaquín J. Torres | 4 | 142 | 19.57 |