A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00503755" target="_blank" >RIV/67985807:_____/19:00503755 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/IJCNN.2019.8852043" target="_blank" >http://dx.doi.org/10.1109/IJCNN.2019.8852043</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2019.8852043" target="_blank" >10.1109/IJCNN.2019.8852043</a>
Alternative languages
Result language
angličtina
Original language name
A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks
Original language description
We consider a simplified Boolean model of the basal ganglia-thalamocortical network, and study the effect of a spiketiming- dependent plasticity (STDP) rule on the stabilization ofits attractor dynamics. More precisely, we introduce an adaptive STDP rule which constantly updates its learning rate based on the attractors that the network encounters during a window of past time steps. This so-called network memory is assumed to be dynamic: its duration is step-wise increased every time a trigger input pattern is detected, and is decreased otherwise. In this context, we show that well-adjusted trigger inputs can fine tune the network memory and its associated STDP rule in such a way to drive the network into stable and rich attractor dynamics. We discuss how this feature might be related to reward learning processes in the neurobiological context
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytical Foundations of Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IJCNN 2019. International Joint Conference on Neural Networks Proceedings
ISBN
978-1-7281-1985-4
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
N-20311
Publisher name
IEEE
Place of publication
New York
Event location
Budapest
Event date
Jul 14, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000530893802104