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Synergistic short-term synaptic plasticity mechanisms for working memory
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: 0000-0002-1290-0351
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]

Working memory (WM) is essential for almost every cognitive task and behavior. The neural and synaptic mechanisms supporting the rapid encoding and maintenance of memories in diverse tasks are the subject of an ongoing debate. The traditional view of WM as stationary persistent firing of selective neuronal populations has given room to newer ideas regarding mechanisms that support a more dynamic maintenance of multiple items, which may also tolerate more activity disruption. Various computational WM models based on different biologically plausible synaptic and neural plasticity mechanisms have been proposed. We show that these proposed short-term plasticity mechanisms may not necessarily be competing explanations, but instead yield interesting functional interactions on a wide set of WM tasks and enhance the biological plausibility of spiking neural network models, in particular of the underlying synaptic plasticity. While monolithic models (WM function explained by one particular mechanism) are theoretically appealing and have increased our understanding of specific mechanisms, they are narrow explanations. WM models need to become more capable, robust and flexible to account for new experimental evidence of bursty and activity-silent multi-item maintenance in more challenging WM tasks, and generally solve more than one particular task. More detailed models also allow for electrophysiological constraints from recordings.

In this study we evaluate the interactions between three commonly used classes of plasticity, namely intrinsic excitability, synaptic facilitation/augmentation and Hebbian plasticity. Combinations of these are systematically tested in a spiking neural network model on a broad suite of tasks or functional motifs deemed principally important for WM operation, such as one-shot encoding, free and cued recall, delay maintenance and updating. In our evaluation we focus on the operational task performance and biological plausibility. Our results indicate that a composite model, combining several commonly proposed plasticity mechanisms for WM function, is superior to more reductionist variants. Importantly, we attribute the observable differences to the principle nature of specific types of plasticity. 

National Category
Neurosciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-362579OAI: oai:DiVA.org:kth-362579DiVA, id: diva2:1953298
Note

QC 20250422

Available from: 2025-04-19 Created: 2025-04-19 Last updated: 2025-05-09Bibliographically 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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Fiebig, FlorianChrysanthidis, NikolaosLansner, AndersHerman, Pawel

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