Spike timing analysis in neural networks with unsupervised synaptic plasticity

Mizusaki, Beatriz E. P. and Agnes, Everton J. and Brunnet, Leonardo G. and Erichsen Jr, Rubem. (2013) Spike timing analysis in neural networks with unsupervised synaptic plasticity. AIP Conference Proceedings, 1510 (1). pp. 213-215.

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Official URL: https://edoc.unibas.ch/79139/

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The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Neurobiology
05 Faculty of Science > Departement Biozentrum > Neurobiology > Theoretical and computational neuroscience (Agnes)
UniBasel Contributors:Agnes, Everton Joao
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:AIP Publishing
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:13 May 2021 03:10
Deposited On:13 Nov 2020 08:39

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