A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytické základy neurovýpočtů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IJCNN 2019. International Joint Conference on Neural Networks Proceedings

ISBN

978-1-7281-1985-4

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

N-20311

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Budapest

Datum konání akce

14. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000530893802104