Dynamic properties of a biologically motivated neural network model
| Autor: | François Chapeau-Blondeau, Gilbert Chauvet |
|---|---|
| Přispěvatelé: | LISA - Laboratoire d'Ingénierie des Systèmes Automatisés, Laboratoire d'Ingéniérie des Systèmes Automatisés (LISA), Université d'Angers (UA) |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Physical neural network
Computer Networks and Communications Computer science Stability (learning theory) 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] 01 natural sciences Transfer function Random neural network Simple (abstract algebra) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering [NLIN]Nonlinear Sciences [physics] 010306 general physics ComputingMilieux_MISCELLANEOUS Spiking neural network Quantitative Biology::Neurons and Cognition Artificial neural network business.industry [SCCO.NEUR]Cognitive science/Neuroscience General Medicine 020201 artificial intelligence & image processing Artificial intelligence Biological system business Nervous system network models |
| Zdroj: | International Journal of Neural Systems International Journal of Neural Systems, World Scientific Publishing, 2011, 03 (04), pp.371-378. ⟨10.1142/S0129065792000280⟩ |
| ISSN: | 0129-0657 |
| DOI: | 10.1142/S0129065792000280⟩ |
| Popis: | We develop a neural network model based on prominent basic features of biological neural networks. The description keeps a simple but coherent link between the subneuronal, neuronal and network levels. In addition, the variables of the model are endowed with realistic numerical values together with their physical units. This permits to reach quantitative significance for the results. To describe the operation of the neuron, a transfer function is used that is believed to convey more biological significance compared to the usual sigmoid transfer function. It is shown that the dynamic properties of the network, which can vary from stability to chaos, are significantly influenced by the choice of the neuron transfer function. Constraints on the synaptic efficacies, as imposed by Dale’s rule, are also shown to modify the dynamic properties by increasing the stability of the network. A simple neural architecture is presented that leads to a controllable time evolution of the network activities. |
| Databáze: | OpenAIRE |
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