Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity
| Autor: | R. Erichsen, Beatriz E. P. Mizusaki, Leonardo G. Brunnet, Everton J. Agnes |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Statistics and Probability Synaptic scaling Artificial neural network Process (engineering) Computer science Condensed Matter Physics Inhibitory postsynaptic potential Synapse 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Recurrent neural network Homeostatic plasticity Metaplasticity Synaptic plasticity Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | Physica A: Statistical Mechanics and its Applications. 479:279-286 |
| ISSN: | 0378-4371 |
| DOI: | 10.1016/j.physa.2017.02.035 |
| Popis: | The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents. |
| Databáze: | OpenAIRE |
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