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A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Edinburgh University, UK. (Computational Brain Science)ORCID iD: 0000-0002-7314-8562
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Stockholm University, Sweden. (Computational Brain Science)ORCID iD: 0000-0002-2358-7815
2017 (English)In: Journal of Neuroscience, ISSN 0270-6474, Vol. 37, no 1, p. 83-96Article in journal (Refereed) Published
Abstract [en]

A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism.

Place, publisher, year, edition, pages
Society for Neuroscience , 2017. Vol. 37, no 1, p. 83-96
Keywords [en]
Hebbian plasticity, primacy, recency, short-term potentiation, word list learning, working memory
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:kth:diva-200399DOI: 10.1523/JNEUROSCI.1989-16.2017ISI: 000391143500008PubMedID: 28053032Scopus ID: 2-s2.0-85008951995OAI: oai:DiVA.org:kth-200399DiVA, id: diva2:1069109
Funder
Swedish Research Council, VR-621-2009-3807VinnovaSwedish e‐Science Research Center
Note

QC 20170127

Available from: 2017-01-27 Created: 2017-01-27 Last updated: 2024-03-15Bibliographically approved
In thesis
1. Active Memory Processing on Multiple Time-scales in Simulated Cortical Networks with Hebbian Plasticity
Open this publication in new window or tab >>Active Memory Processing on Multiple Time-scales in Simulated Cortical Networks with Hebbian Plasticity
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis examines declarative memory function, and its underlying neural activity and mechanisms in simulated cortical networks. The included simulation models utilize and synthesize proposed universal computational principles of the brain, such as the modularity of cortical circuit organization, attractor network theory, and Hebbian synaptic plasticity, along with selected biophysical detail from the involved brain areas to implement functional models of known cortical memory systems. The models hypothesize relations between neural activity, brain area interactions, and cognitive memory functions such as sleep-dependent memory consolidation, or specific working memory tasks. In particular, this work addresses the acutely relevant research question if recently described fast forms of Hebbian synaptic plasticity are a possible mechanism behind working memory. The proposed models specifically challenge the “persistent activity hypothesis of working memory”, an established but increasingly questioned paradigm in working memory theory. The proposed alternative is a novel synaptic working memory model that is arguably more defensible than the existing paradigm as it can better explain memory function and important aspects of working memory-linked activity (such as the role of long-term memory in working memory tasks), while simultaneously matching experimental data from behavioral memory testing and important evidence from electrode recordings.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2018. p. 125
Series
TRITA-EECS-AVL ; 2018:91
Keywords
Working memory, Long-term memory, consolidation, spiking, neural network, BCPNN, cortical microcircuit
National Category
Bioinformatics (Computational Biology)
Research subject
Applied and Computational Mathematics; Biological Physics
Identifiers
urn:nbn:se:kth:diva-239041 (URN)978-91-7873-030-8 (ISBN)
Public defence
2018-12-11, Kollegiesalen, Brinellvägen 9, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

Joint Doctoral Program in Neuroinformatics between KTH Royal Institute of Technology, Sweden and University of Edinburgh (UoE), UK, see https://www.kth.se/eurospin

QC 20181115

Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2022-06-26Bibliographically approved

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SpikingWorkingMemory(3196 kB)135 downloads
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Fiebig, FlorianLansner, Anders

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