kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Short-term plasticity influences episodic memory recall - an interplay of synaptic traces in a spiking neural network model
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-1290-0351
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-7314-8562
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0002-2358-7815
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0001-6553-823X
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We investigated the interaction of episodic memory processes with the short-term dynamics of recency effects. This work takes inspiration from a seminal experimental work involving an odor-in-context association task conducted on rats (Panoz-Brown et al., 2016). In the experimental task, rats were presented with odor pairs in two arenas serving as old or new contexts for specific odor items. Rats were rewarded for selecting the odor that was new to the current context. These new-in-context odor items were deliberately presented with higher recency relative to old-in-context items, so that episodic memory was put in conflict with a short-term recency effect. To study our hypothesis about the major role of synaptic interplay of plasticity phenomena on different time-scales in explaining rats’ performance in such episodic memory tasks, we built a computational spiking neural network model consisting of two reciprocally connected networks that stored contextual and odor information as stable distributed memory patterns. We simulated the experimental task resulting in a dynamic context-item coupling between the two networks by means of Bayesian-Hebbian plasticity with eligibility traces to account for reward-based learning. We first reproduced quantitatively and explained mechanistically the findings of the experimental study, and further simulated an alternative task with old-in-context items presented with higher recency, thus synergistically confounding episodic memory with effects of recency. Our model predicted that higher recency of old-in-context items enhances episodic memory by boosting the activations of old-in-context items. We argue that the model offers a computational framework for studying behavioral implications of the synaptic underpinning of different memory effects in experimental episodic memory paradigms.

National Category
Neurosciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-362576DOI: 10.1101/2024.06.21.598805OAI: oai:DiVA.org:kth-362576DiVA, id: diva2:1953190
Note

QC 20250422

Available from: 2025-04-17 Created: 2025-04-17 Last updated: 2025-05-12Bibliographically approved
In thesis
1. Neurocomputational mechanisms of memory – Hebbian plasticity across short and long timescales
Open this publication in new window or tab >>Neurocomputational mechanisms of memory – Hebbian plasticity across short and long timescales
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The mammalian brain is a complex structure, capable of processing sensory stimuli through the lens of prior knowledge and experiences to guide behavior and decision-making. This process relies on intricate neural dynamics, synaptic plasticity mechanisms, and interactions across brain networks. Despite the brain's remarkable ability to store information, even seemingly stable memories can be modified by new experiences or forgotten over time.

In this work, we use computational modelling to investigate the mechanisms underlying memory functionality, focusing on how the brain supports short- and long-term memory processes. Our research sheds light on episodic, semantic, and working memory phenomena by employing cortical memory models integrating neural plasticity, Bayesian-Hebbian synaptic plasticity across a range of short and long timescales, together with short-term non-Hebbian mechanisms. Inspired by behavioral memory tasks and experimental evidence, we explore processes such as memory semantization — where associated episodic memories are gradually decoupled, allowing for the extraction of abstract semantic meaning. We also investigate and propose hypothetical underlying neurocomputational mechanisms of verbal omissions (memory forgetting) in odor naming tasks. Additionally, we examine the interplay between episodic memory and recency effects in immediate recall. Expanding our framework to working memory, we investigate how different plasticity mechanisms interact to enable both stability and flexibility in memory maintenance.

By bridging computational models with cognitive neuroscience, this research provides new insights into the neural and synaptic basis of memory processes.

Abstract [sv]

Däggdjurshjärnan är en komplex struktur, kapabel att bearbeta sensoriska stimuli genom linsen av tidigare kunskap och erfarenheter och därigenom styra beteende och beslutsfattande. Denna process bygger på intrikat neural dynamik, synaptiska plasticitetsmekanismer och interaktioner mellan hjärnans olika nätverk. Trots hjärnans anmärkningsvärda förmåga att lagra information kan även till synes stabila minnen förändras av nya erfarenheter eller glömmas bort över tid.

I detta arbete använder vi oss av beräkningsmodeller för att undersöka de mekanismer som ligger till grund för olika minnesfunktioner, med fokus på hur hjärnan stödjer minnesprocesser pa lang och kort sikt. Vår forskning belyser fenomen kopplade till episodiskt, semantiskt och arbetsminne genom att använda kortikala minnesmodeller som integrerar neural dynamik, Bayesiansk-Hebbsk synaptisk plasticitet över både korta och långa tidsskalor tillsammans med kortsiktiga icke-Hebbska mekanismer. Inspirerade av olika minnesexperiment och och fynd från dessa undersöker vi processer såsom semantisering av minnet — där associerade episodiska minnen gradvis frikopplas, vilket möjliggör extrahering av abstrakt semantisk innebörd. Vi undersöker också och föreslår hypotetiska underliggande neuroberäkningsmekanismer för verbala utelämnanden (glömning) i luktidentifieringsuppgifter. Dessutom analyserar vi samspelet mellan episodiskt minne och s k recency-effekter i omedelbart minnesåterkallande. Genom att utvidga vår modell till arbetsminnet undersöker vi hur olika plasticitetsmekanismer samverkar för att möjliggöra både stabilitet och flexibilitet i minneslagring.

Genom att förena beräkningsmodeller med kognitiv neurovetenskap bidrar denna forskning med nya insikter om de neurala och synaptiska grunderna för våra minnesprocesser.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2025. p. 132
Series
TRITA-EECS-AVL ; 2025:52
Keywords
Hebbian-like plasticity, Cortical memory models, Attractor dynamics, Episodic memory, Working memory, Hebbsk-liknande plasticitet, Kortikala minnesmodeller, Attraktordynamik, Episodiskt minne, Arbetsminne
National Category
Neurosciences Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-363243 (URN)978-91-8106-282-3 (ISBN)
Public defence
2025-06-09, F2, Lindstedtsvägen 26 & 28, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council
Note

QC 20250509

Available from: 2025-05-09 Created: 2025-05-09 Last updated: 2025-05-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Chrysanthidis, NikolaosFiebig, FlorianLansner, AndersHerman, Pawel

Search in DiVA

By author/editor
Chrysanthidis, NikolaosFiebig, FlorianLansner, AndersHerman, Pawel
By organisation
Computational Science and Technology (CST)
Neurosciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 218 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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