Prediction error as the Basis of the Testing Effect: Empirical and Modelling Evidence DOI Open Access
Haopeng Chen, Cathy Hauspie, Kate Ergo

et al.

Published: Oct. 9, 2023

A robust finding in declarative memory is the testing effect, meaning that enhances retention more than mere studying. Emergent frameworks propose fundamental (Hebbian and predictive) learning principles as its basis. Predictive posits occurs based on contrast (or error) between prediction feedback (i.e., error). Here, we (but not studying) scenarios, participants predict potential answers, this subsequent yields a error. To investigate this, developed neural network incorporating Hebbian and/or predictive learning, together with an experimental design where human studied or tested English-Swahili word pairs followed by recognition. Two behavioral experiments revealed strong effects. Model fitting suggested only models can account for breadth of data associated effect. Our model suggest underlies

Language: Английский

Predictive learning as the basis of the testing effect DOI Creative Commons

Haopeng Chen,

Cathy Hauspie, Kate Ergo

et al.

Communications Psychology, Journal Year: 2025, Volume and Issue: 3(1)

Published: Feb. 1, 2025

A prominent learning phenomenon is the testing effect, meaning that enhances retention more than studying. Emergent frameworks propose fundamental (Hebbian and predictive) principles as its basis. Predictive posits occurs based on contrast (error) between a prediction feedback (prediction error). Here, we in (but not studying) scenarios, participants predict potential answers, with subsequent yields error, which facilitates testing-based learning. To investigate this, developed an associative memory network incorporating Hebbian and/or predictive learning, together experimental design where human studied or tested English-Swahili word pairs followed by recognition. Three behavioral experiments (N = 80, 81, 62) showed robust effects when was provided. Model fitting (of 10 different models) suggested only models can account for breadth of data associated effect. Our model suggest underlies

Language: Английский

Citations

0
Effect of Stromal Stem Cells’ Intrathecal Transplantation on the Course of Experimental Peripheral Nerve Injury DOI
Ziia K. Melikov, Oksana Rybachuk, Volodymyr V. Medvediev

et al.

Cytology and Genetics, Journal Year: 2025, Volume and Issue: 59(1), P. 36 - 46

Published: Feb. 1, 2025

Language: Английский

Citations

0
Adaptive learning via BG-thalamo-cortical circuitry DOI Creative Commons
Qin He, Daniel N. Scott, Michael J. Frank

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

People adjust their use of feedback over time through a process referred to as adaptive learning. We have recently proposed that the underlying mechanisms learning are rooted in how brain organizes into similarly credited units, which we refer latent states. Here develop BG-thalamo-cortical circuit model this and show it captures both commonalities heterogeneity human behavior. Our learns incrementally synaptic plasticity PFC-BG connections, but upon observing discordant information, produces thalamocortical reset signals alter PFC connectivity, driving attractor state transitions facilitate rapid updating behavioral policy. demonstrate mechanism can give rise optimized dynamics context either changepoints or reversals, under reasonable biological assumptions is able generalize efficiently across these conditions, adjusting behavior context-appropriate manner. Taken together, our results provide biologically plausible mechanistic for explains existing data makes testable predictions about computational roles different regions complex behaviors.

Language: Английский

Citations

0
Statistical Machine-Learning Methods to Model Brain Plasticity DOI
Pablo Robles-Granda, Aron K. Barbey, Oluwasanmi Koyejo

et al.

Oxford University Press eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Abstract This chapter discusses statistical machine-learning (ML) approaches to model brain plasticity, which involves complex changes in the due natural or induced causes. The highlights various advantages that ML models have compared with traditional of plasticity. Since plasticity can be analyzed at levels granularity, this several starting some examples most traditionally studied, is, visual and motor control systems synaptic for memory throughout mammalian neocortex. Then are discussed contexts scales, including main aspects considered multiscale modeling, specific information about neuron level, cortical column, as a result development. Following this, modeling plasticity’s effect on higher-level cognitive functions, specifically those related behavior, cognition, learning, decision making, intelligence, memory. Plasticity when it results from trauma damage is then reviewed. concludes by reviewing open research questions future directions

Language: Английский

Citations

0
Stability and Adaptability in Balance: A Dual Mechanism for Metaplasticity in Cortical Networks DOI Creative Commons

Tea Tompos,

Fleur Zeldenrust, Tansu Celikel

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

1. Abstract Metaplasticity dynamically adjusts how synaptic efficacy and connectivity change, helping neural circuits adapt to experience. However, the interaction between changes in weight (W) connection probability (P) remains poorly understood. We explored their using a biologically-inspired, multi-layer spiking network. found that while W controls network excitability, P exerts layer-specific time-dependent control, crucial for stability. Simultaneous P, i.e. metaplasticity, revealed complex, non-additive interactions, shaping response timing recruitment, resulting emergence of functionally distinct neuronal subtypes: input-invariant neurons maintaining responsiveness variant enabling adaptation, based on differential E-I dynamics. This allows achieve functional homeostasis input layer preserving flexibility superficial layers. provide novel framework understanding metaplasticity balances competing demands stability adaptability cortical circuits, with significant implications learning, memory, coding.

Language: Английский

Citations

0
Lethal Interactions of neuronal networks in epilepsy mediated by both synaptic and volume transmission indicate approaches to prevention DOI
Carl L. Faingold

Progress in Neurobiology, Journal Year: 2025, Volume and Issue: 249, P. 102770 - 102770

Published: April 19, 2025

Language: Английский

Citations

0
Multi‐scale brain attributes contribute to the distribution of diffuse glioma subtypes DOI
Peng Ren, Hongbo Bao, Shuai Wang

et al.

International Journal of Cancer, Journal Year: 2024, Volume and Issue: 155(9), P. 1670 - 1683

Published: July 1, 2024

Gliomas are primary brain tumors and among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, mechanisms underlying distributions subtypes not entirely clear despite partial explanations such cell origin. To investigate impact multi-scale attributes distribution, we constructed cumulative frequency maps for T1w structural images evaluated spatial correlation between tumor diverse attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, neurotransmitter signaling. Regression models were to evaluate contribution these factors anatomic different subtypes. Our findings revealed three had distinct patterns, showing preferences toward environmental attributes. Glioblastomas especially likely occur in regions enriched with synapse-related pathways receptors. Astrocytomas oligodendrogliomas preferentially occurred areas genes associated neutrophil-mediated immune responses. The network characteristics also contributed oligodendroglioma distribution. results suggest transcriptomic, neurotransmitter, connectomic determine These highlight importance bridging scales biological organization when studying neurological dysfunction.

Language: Английский

Citations

3
A Perspective on Prosthetic Hands Control: From the Brain to the Hand DOI Open Access
Cosimo Gentile, Emanuele Gruppioni

Prosthesis, Journal Year: 2023, Volume and Issue: 5(4), P. 1184 - 1205

Published: Nov. 16, 2023

The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, use tools. control of by brain crucial aspect cognition behaviour, but also challenging problem for both neuroscience engineering. aim this study review current state art in grasp from neuroscientific perspective, focusing on mechanisms underlie sensory integration engineering implications developing artificial hands can mimic interface brain. controls processing integrating information vision, proprioception, touch, using different neural pathways. user’s intention be obtained interfaces, such as electromyography, electroneurography, electroencephalography. This other exploited learning help user adapt changes inputs or outputs, reinforcement learning, motor adaptation, internal models. work summarizes main findings challenges each research highlights gaps limitations approaches. In last part, some open questions future directions are suggested emphasizing need approach bridge gap between hand.

Language: Английский

Citations

7
Lack of effects of eight-week left dorsolateral prefrontal theta burst stimulation on white matter macro/microstructure and connection in autism DOI
Chun‐Hung Yeh,

Po-Chun Lin,

Rung-Yu Tseng

et al.

Brain Imaging and Behavior, Journal Year: 2024, Volume and Issue: 18(4), P. 794 - 807

Published: March 16, 2024

Language: Английский

Citations

2
The evolution of sensitive periods beyond early ontogeny: Bridging theory and data DOI Open Access
Nicole Walasek, Karthik Panchanathan, Willem E. Frankenhuis

et al.

Published: April 1, 2024

Sensitive periods, during which experiences have a large impact on phenotypic development, are most common early in ontogeny, yet they also occur later ontogenetic stages, including adolescence. At present, however, we know little about why natural selection favors sensitive periods for some traits ontogeny and others ontogeny. This article synthesizes recent mathematical models empirical studies that explore beyond Across formal models, observe two general patterns. First, emerge when an organism’s uncertainty the environment-phenotype fit increases at developmental stages. Second, cues stages reduce this more than earlier do. In literature, showing tend to be social traits, particularly among mammals. Connecting theory data, hypothesize mammals evolved expect highly reliable information from peers adolescence current future environment (e.g. dominance, mate value). Finally, highlight gaps our understanding, describe how different ways of quantifying influenced observed patterns, suggest directions strengthening bridges between theoretical periods. Ultimately, hope synthesis will contribute towards integrative science across biological sciences.

Language: Английский

Citations

0