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Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

Mizusaki, Beatriz E. P. and Agnes, Everton J. and Erichsen Jr, Rubem and Brunnet, Leonardo G.. (2017) Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity. Physica. A, Theoretical and statistical physics, 479. pp. 279-286.

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Official URL: https://edoc.unibas.ch/79142/

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Abstract

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.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Neurobiology
UniBasel Contributors:Agnes, Everton Joao
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:North-Holland
ISSN:0378-4371
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:13 Nov 2020 08:42
Deposited On:13 Nov 2020 08:42

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