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Active Memory Processing on Multiple Time-scales in Simulated Cortical Networks with Hebbian Plasticity
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). (Computational Brain Science)ORCID iD: 0000-0002-7314-8562
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 [en]
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: urn:nbn:se:kth:diva-239041ISBN: 978-91-7873-030-8 (print)OAI: oai:DiVA.org:kth-239041DiVA, id: diva2:1263428
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: 2018-11-16Bibliographically approved
List of papers
1. Memory consolidation from seconds to weeks: a three-stage neural network model with autonomous reinstatement dynamics
Open this publication in new window or tab >>Memory consolidation from seconds to weeks: a three-stage neural network model with autonomous reinstatement dynamics
2014 (English)In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 8, p. 64-Article in journal (Refereed) Published
Abstract [en]

Declarative long-term memories are not created in an instant. Gradual stabilization and temporally shifting dependence of acquired declarative memories in different brain regions called systems consolidation- can be tracked in time by lesion experiments. The observation of temporally graded retrograde amnesia(RA) following hippocampal lesions points to a gradual transfer of memory from hippocampus to neocortical long-term memory. Spontaneous reactivations of hippocampal memories, asobserved in place cell reactivations during slow wave- sleep, are supposed to driven eocortical reinstatements and facilitate this process. We proposea functional neural network implementation of these ideas and further more suggest anextended three-state framework that includes the prefrontal cortex( PFC). It bridges the temporal chasm between working memory percepts on the scale of seconds and consolidated long-term memory on the scale of weeks or months. Wes how that our three-stage model can autonomously produce the necessary stochastic reactivation dynamics for successful episodic memory consolidation. There sulting learning system is shown to exhibit classical memory effects seen in experimental studies, such as retrograde and anterograde amnesia(AA) after simulated hippocampal lesioning; further more the model reproduces peculiar biological findings on memory modulation, such as retrograde facilitation of memory after suppressed acquisition of new longterm memories- similar to the effects of benzodiazepines on memory.

Keywords
memory consolidation, working memory, complementary learning systems, synaptic depression, neural adaptation, retrograde amnesia, anterograde amnesia, retrograde facilitation, computational model
National Category
Bioinformatics (Computational Biology) Neurology
Identifiers
urn:nbn:se:kth:diva-148619 (URN)10.3389/fncom.2014.00064 (DOI)000339052300001 ()2-s2.0-84903715018 (Scopus ID)
Funder
Swedish Research Council, VR-621-2012-3502VINNOVASwedish Foundation for Strategic Research EU, FP7, Seventh Framework Programme, 269921
Note

QC 20140812

Available from: 2014-08-12 Created: 2014-08-11 Last updated: 2018-11-15Bibliographically approved
2. A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation
Open this publication in new window or tab >>A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation
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
Keywords
Hebbian plasticity, primacy, recency, short-term potentiation, word list learning, working memory
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-200399 (URN)10.1523/JNEUROSCI.1989-16.2017 (DOI)000391143500008 ()2-s2.0-85008951995 (Scopus ID)
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: 2018-11-15Bibliographically approved
3. An Indexing Theory for Working Memory based on Fast Hebbian Plasticity
Open this publication in new window or tab >>An Indexing Theory for Working Memory based on Fast Hebbian Plasticity
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Working memory (WM) is a key component of human memory and cognitive function. Computational models have been used to uncover the underlying neural mechanisms. However, these studies have mostly focused on the short-term memory aspects of WM and neglected the equally important role of interactions between short- and long-term memory (STM, LTM). Here, we concentrate on these interactions within the framework of our new computational model of WM, which accounts for three cortical patches in macaque brain, corresponding to networks in prefrontal cortex (PFC) together with parieto-temporal cortical areas. In particular, we propose a cortical indexing theory that explains how PFC could associate, maintain and update multi-modal LTM representations. Our simulation results demonstrate how simultaneous, brief multi-modal memory cues could build a temporary joint memory representation linked via an "index" in the prefrontal cortex by means of fast Hebbian synaptic plasticity. The latter can then activate spontaneously and thereby reactivate the associated long-term representations. Cueing one long-term memory item rapidly pattern-completes the associated un-cued item via prefrontal cortex. The STM network updates flexibly as new stimuli arrive thereby gradually over-writing older representations. In a wider context, this WM model suggests a novel explanation for "variable binding", a long-standing and fundamental phenomenon in cognitive neuroscience, which is still poorly understood in terms of detailed neural mechanisms.

Keywords
Memory Computational Neuroscience Indexing Theory Hebbian Plasticity Prefrontal Cortex
National Category
Bioinformatics (Computational Biology)
Research subject
Biological Physics; Applied and Computational Mathematics; Theoretical Chemistry and Biology
Identifiers
urn:nbn:se:kth:diva-239038 (URN)10.1101/334821 (DOI)
Note

QC 20181115

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

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