Emerging Bayesian priors in a self-organizing recurrent network
DC Element | Wert | Sprache |
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dc.contributor.author | Lazar, A. | |
dc.contributor.author | Pipa, G. | |
dc.contributor.author | Triesch, J. | |
dc.date.accessioned | 2021-12-23T16:31:20Z | - |
dc.date.available | 2021-12-23T16:31:20Z | - |
dc.date.issued | 2011 | |
dc.identifier.isbn | 9783642217371 | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/17027 | - |
dc.description | Conference of 21st International Conference on Artificial Neural Networks, ICANN 2011 ; Conference Date: 14 June 2011 Through 17 June 2011; Conference Code:85226 | |
dc.description.abstract | We explore the role of local plasticity rules in learning statistical priors in a self-organizing recurrent neural network (SORN). The network receives input sequences composed of different symbols and learns the structure embedded in these sequences via a simple spike-timing-dependent plasticity rule, while synaptic normalization and intrinsic plasticity maintain a low level of activity. After learning, the network exhibits spontaneous activity that matches the stimulus-evoked activity during training and thus can be interpreted as samples from the network's prior probability distribution over evoked activity states. Further, we show how learning the frequency and spatio-temporal characteristics of the input sequences influences network performance in several classification tasks. These results suggest a novel connection between low level learning mechanisms and high level concepts of statistical inference. © 2011 Springer-Verlag. | |
dc.language.iso | en | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.subject | Bayesian inference | |
dc.subject | Inference engines | |
dc.subject | intrinsic plasticity | |
dc.subject | Network performance | |
dc.subject | recurrent networks | |
dc.subject | Recurrent neural networks, Probability distributions | |
dc.subject | Spontaneous activity | |
dc.subject | statistical priors | |
dc.subject | STDP | |
dc.subject | STDP, Bayesian networks | |
dc.title | Emerging Bayesian priors in a self-organizing recurrent network | |
dc.type | conference paper | |
dc.identifier.doi | 10.1007/978-3-642-21738-8_17 | |
dc.identifier.scopus | 2-s2.0-79959353033 | |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959353033&doi=10.1007%2f978-3-642-21738-8_17&partnerID=40&md5=2c5f5dc8ce14a335ab754533af9d89dd | |
dc.description.volume | 6792 LNCS | |
dc.description.issue | PART 2 | |
dc.description.startpage | 127 | |
dc.description.endpage | 134 | |
dc.publisher.place | Espoo | |
dcterms.isPartOf.abbreviation | Lect. Notes Comput. Sci. | |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.orcid | 0000-0002-3416-2652 | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | PiGo340 | - |
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geprüft am 02.05.2024