Structural phase transitions in neural networks
| Autor: | Tatyana S. Turova |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Bootstrap percolation
Structural phase Time Factors Computer science Models Neurological Action Potentials Topology Birds Animals Humans Computer Simulation Stochastic neural network Neurons Random graph Stochastic Processes Quantitative Biology::Neurons and Cognition Artificial neural network Stochastic process Applied Mathematics Brain General Medicine Electrophysiology Computational Mathematics Modeling and Simulation Synapses Graph (abstract data type) Neural Networks Computer Vocalization Animal General Agricultural and Biological Sciences Algorithm Algorithms Excitation |
| Zdroj: | Mathematical Biosciences and Engineering. 11:139-148 |
| ISSN: | 1551-0018 |
| DOI: | 10.3934/mbe.2014.11.139 |
| Popis: | A model is considered for a neural network that is a stochastic process on a random graph. The neurons are represented by "integrate-and-fire" processes. The structure of the graph is determined by the probabilities of the connections, and it depends on the activity in the network. The dependence between the initial level of sparseness of the connections and the dynamics of activation in the network was investigated. A balanced regime was found between activity, i.e., the level of excitation in the network, and inhibition, that allows formation of synfire chains. |
| Databáze: | OpenAIRE |
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