Sökning: onr:"swepub:oai:DiVA.org:kth-331435" >
Reinforcement learn...
-
González-Redondo, ÁlvaroResearch Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, Calle Periodista Rafael Gómez Montero 2, E18071 Granada, Spain
(författare)
Reinforcement learning in a spiking neural model of striatum plasticity
- Artikel/kapitelEngelska2023
Förlag, utgivningsår, omfång ...
-
Elsevier BV,2023
-
printrdacarrier
Nummerbeteckningar
-
LIBRIS-ID:oai:DiVA.org:kth-331435
-
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-331435URI
-
https://doi.org/10.1016/j.neucom.2023.126377DOI
-
http://kipublications.ki.se/Default.aspx?queryparsed=id:153171814URI
Kompletterande språkuppgifter
-
Språk:engelska
-
Sammanfattning på:engelska
Ingår i deldatabas
Klassifikation
-
Ämneskategori:ref swepub-contenttype
-
Ämneskategori:art swepub-publicationtype
Anmärkningar
-
QC 20230710
-
The basal ganglia (BG), and more specifically the striatum, have long been proposed to play an essential role in action-selection based on a reinforcement learning (RL) paradigm. However, some recent findings, such as striatal spike-timing-dependent plasticity (STDP) or striatal lateral connectivity, require further research and modelling as their respective roles are still not well understood. Theoretical models of spiking neurons with homeostatic mechanisms, lateral connectivity, and reward-modulated STDP have demonstrated a remarkable capability to learn sensorial patterns that statistically correlate with a rewarding signal. In this article, we implement a functional and biologically inspired network model of the striatum, where learning is based on a previously proposed learning rule called spike-timing-dependent eligibility (STDE), which captures important experimental features in the striatum. The proposed computational model can recognize complex input patterns and consistently choose rewarded actions to respond to such sensorial inputs. Moreover, we assess the role different neuronal and network features, such as homeostatic mechanisms and lateral inhibitory connections, play in action-selection with the proposed model. The homeostatic mechanisms make learning more robust (in terms of suitable parameters) and facilitate recovery after rewarding policy swapping, while lateral inhibitory connections are important when multiple input patterns are associated with the same rewarded action. Finally, according to our simulations, the optimal delay between the action and the dopaminergic feedback is obtained around 300 ms, as demonstrated in previous studies of RL and in biological studies.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
-
Garrido, JesúsResearch Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, Calle Periodista Rafael Gómez Montero 2, E18071 Granada, Spain
(författare)
-
Naveros Arrabal, FranciscoResearch Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, Calle Periodista Rafael Gómez Montero 2, E18071 Granada, Spain
(författare)
-
Hellgren Kotaleski, JeanetteKTH,Beräkningsvetenskap och beräkningsteknik (CST)(Swepub:kth)u13pnwqn
(författare)
-
Grillner, StenKarolinska Institutet
(författare)
-
Ros, EduardoResearch Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, Calle Periodista Rafael Gómez Montero 2, E18071 Granada, Spain
(författare)
-
Research Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, Calle Periodista Rafael Gómez Montero 2, E18071 Granada, SpainBeräkningsvetenskap och beräkningsteknik (CST)
(creator_code:org_t)
Sammanhörande titlar
-
Ingår i:Neurocomputing: Elsevier BV5480925-23121872-8286
Internetlänk
Hitta via bibliotek
Till lärosätets databas