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Probabilistic computation underlying sequence learning in a spiking attractor memory network
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Institutet, Sweden; University of Edinburgh, UK.ORCID iD: 0000-0001-8796-3237
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Institutet, Sweden.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Stockholm; Karolinska Institutet, Sweden.ORCID iD: 0000-0002-2358-7815
2013 (English)In: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, no 14 (Suppl 1)Article in journal (Refereed) Published
Place, publisher, year, edition, pages
BioMed Central, 2013. no 14 (Suppl 1)
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-205609DOI: 10.1186/1471-2202-14-S1-P236OAI: oai:DiVA.org:kth-205609DiVA, id: diva2:1089498
Note

QC 20170421

Available from: 2017-04-20 Created: 2017-04-20 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
Open this publication in new window or tab >>Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns

do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations. 

 

In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels. 

 

The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. p. 89
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2017:11
Keywords
Bayes' rule, synaptic plasticity and memory modeling, intrinsic excitability, naïve Bayes classifier, spiking neural networks, Hebbian learning, neuromorphic engineering, reinforcement learning, temporal sequence learning, attractor network
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-205568 (URN)978-91-7729-351-4 (ISBN)
Public defence
2017-05-09, F3, Lindstedtsvägen 26, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20170421

Available from: 2017-04-21 Created: 2017-04-19 Last updated: 2017-04-21Bibliographically approved

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