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Traces of Semantization, from Episodic to Semantic Memory in a Spiking Cortical 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). Department of Mathematics, Stockholm University, Stockholm, 10691, Sweden.ORCID iD: 0000-0002-2358-7815
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Digital futures.ORCID iD: 0000-0001-6553-823X
2022 (English)In: eNeuro, E-ISSN 2373-2822, Vol. 9, no 4, article id ENEURO.0062-22.2022Article in journal (Refereed) Published
Abstract [en]

Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or “semantization,” which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know (R/K) behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian–Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian–Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing-dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints while also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes. 

Place, publisher, year, edition, pages
Society for Neuroscience , 2022. Vol. 9, no 4, article id ENEURO.0062-22.2022
Keywords [en]
Bayesian–Hebbian plasticity, BCPNN, episodic memory, semantization, spiking cortical memory model, STDP, article, learning, memory, nerve cell plasticity, semantic memory, spike, spiking neural network
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:kth:diva-327297DOI: 10.1523/ENEURO.0062-22.2022PubMedID: 35803714Scopus ID: 2-s2.0-85138107120OAI: oai:DiVA.org:kth-327297DiVA, id: diva2:1758901
Note

QC 20250922

Available from: 2023-05-24 Created: 2023-05-24 Last updated: 2025-09-22Bibliographically 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

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Chrysanthidis, NikolaosFiebig, FlorianLansner, AndersHerman, Pawel

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