Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An Indexing Theory for Working Memory based on Fast 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
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).ORCID iD: 0000-0001-6553-823X
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Department of Mathematics, Stockholm University, 10691 Stockholm, Swed.ORCID iD: 0000-0002-2358-7815
(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 [en]
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: urn:nbn:se:kth:diva-239038DOI: 10.1101/334821OAI: oai:DiVA.org:kth-239038DiVA, id: diva2:1263419
Note

QC 20181115

Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2018-11-15Bibliographically 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: 2018-11-16Bibliographically approved

Open Access in DiVA

fulltext(3821 kB)53 downloads
File information
File name FULLTEXT01.pdfFile size 3821 kBChecksum SHA-512
a6ea1a5e4f09c03b7a2589514ca4fae3f878ce5834a479a414b37eedc5de09591dbf91baba2e1e2c202359a0445287dbfe29cb840590552b6f3e4e4350518130
Type fulltextMimetype application/pdf

Other links

Publisher's full textBioRxiv Preprint

Authority records BETA

Herman, PawelLansner, Anders

Search in DiVA

By author/editor
Fiebig, FlorianHerman, PawelLansner, Anders
By organisation
Computational Science and Technology (CST)
Bioinformatics (Computational Biology)

Search outside of DiVA

GoogleGoogle Scholar
Total: 53 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 196 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf