Can Human Brain Connectivity explain Verbal Working Memory? DOI Creative Commons

Maxime Carriere,

Rosario Tomasello, Friedemann Pulvermüller

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 19, 2023

Abstract Introduction: Humans are able to learn and use a broad range of words other symbols, whereas Monkeys limited acquiring small vocabularies signs, including sounds gestures. Although evolutionary changes on network architecture connectivity features within the left-perisylvian regions has been reported, their functional contribution symbol formation verbal working memory poorly understood.Methods: Here, we used brain-constrained neural frontotemporal occipital cortices mimicking key neuroanatomical distinctions between human non-human primates.Results: Our comparative analysis models shows that model, characterized by denser inter-area connectivity, gives rise larger cell assemblies with distinct semantic-specific topography compared less densely connected monkey models. Additionally, simulating auditory word recognition, observed emergence longer reverberation activity in those monkeys. Interestingly, these observations consistent across different model types, basic meanfield spiking model.Conclusions: These findings shed light structural underpinnings human-specific memory, crucial feature for acquisition an expansive vocabulary.

Language: Английский

Analysis of argument structure constructions in the large language model BERT DOI Creative Commons

Pegah Ramezani,

Achim Schilling, Patrick Krauß

et al.

Frontiers in Artificial Intelligence, Journal Year: 2025, Volume and Issue: 8

Published: Jan. 31, 2025

Understanding how language and linguistic constructions are processed in the brain is a fundamental question cognitive computational neuroscience. In this study, we investigate processing representation of Argument Structure Constructions (ASCs) BERT model, extending previous analyses conducted with Long Short-Term Memory (LSTM) networks. We utilized custom GPT-4 generated dataset comprising 2000 sentences, evenly distributed among four ASC types: transitive, ditransitive, caused-motion, resultative constructions. was assessed using various token embeddings across its 12 layers. Our involved visualizing Multidimensional Scaling (MDS) t-Distributed Stochastic Neighbor Embedding (t-SNE), calculating Generalized Discrimination Value (GDV) to quantify degree clustering. also trained feedforward classifiers (probes) predict construction categories from these embeddings. Results reveal that CLS cluster best according types layers 2, 3, 4, diminished clustering intermediate slight increase final Token for DET SUBJ showed consistent intermediate-level layers, while VERB demonstrated systematic layer 1 12. OBJ exhibited minimal initially, which increased substantially, peaking 10. Probe accuracies indicated initial contained no specific information, as seen low chance-level 1. From 2 onward, probe surpassed 90 percent, highlighting latent category information not evident GDV alone. Additionally, Fisher Discriminant Ratio (FDR) analysis attention weights revealed tokens had highest FDR scores, indicating they play crucial role differentiating ASCs, followed by tokens. SUBJ, CLS, SEP did show significant scores. study underscores complex, layered BERT, revealing both similarities differences compared recurrent models like LSTMs. Future research will compare findings neuroimaging data during continuous speech perception better understand neural correlates processing. This demonstrates potential transformer-based mirror human brain, offering valuable insights into mechanisms underlying understanding.

Language: Английский

Citations

2
Theoretical strategies for an embodied cognitive neuroscience: Mechanistic explanations of brain-body-environment systems DOI

Davy Mougenot,

Heath E. Matheson

Cognitive Neuroscience, Journal Year: 2024, Volume and Issue: 15(3-4), P. 85 - 97

Published: May 12, 2024

Cognitive neuroscience seeks to explain mind, brain, and behavior. But how do we generate explanations? In this integrative theoretical paper, review the commitments of 'New Mechanist' movement within philosophy science, focusing specifically on role mechanistic models in scientific explanation. We highlight approach differs from other explanatory approaches field, showing its unique contributions efforts then argue that Embodied Cognition framework converge with New Mechanist a way provides necessary strategy available cognitive neuroscience. discuss number consequences convergence, including issues related inadequacy statistical prediction, neuroscientific reduction, autonomy psychology neuroscience, psychological ontology. hope our thesis researchers for an embodied

Language: Английский

Citations

9
Advances and Challenges in Closed Loop Therapeutics: From Signal Selection to Optogenetic Techniques DOI Creative Commons
Francisco Pedro

Journal of Biomedical and Sustainable Healthcare Applications, Journal Year: 2024, Volume and Issue: unknown, P. 73 - 83

Published: Jan. 5, 2024

The main objective of this paper is to develop closed-loop therapeutic systems by reviewing various neurological disorders. We propose a system that incorporates biosensor, controller, and infusion pump provide feedback management medicine delivery. To address the specific requirements medication called Dox, they made precise adjustments system's functioning. device biosensor capable real-time assessment levels in bloodstream. method utilizes aptamer probes have been labeled with an electrochemical tag. When these connect drug target, undergo reversible change shape, leading modification redox current. A little quantity blood consistently extracted from animal's circulatory inside microfluidic device, which used for measurement. examines challenges seizure detection use advanced learning algorithms classification methods enhance real- time systems. Following successful optogenetic techniques epilepsy models, authors discuss potential technologies controlling brain activity.

Language: Английский

Citations

4
Visualization of functional and effective connectivity underlying auditory descriptive naming DOI
Yu Kitazawa, Kazuki Sakakura, Hiroshi Uda

et al.

Clinical Neurophysiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
The impact of early and late blindness on language and verbal working memory: A brain-constrained neural model DOI Creative Commons
Rosario Tomasello,

Maxime Carriere,

Friedemann Pulvermüller

et al.

Neuropsychologia, Journal Year: 2024, Volume and Issue: 196, P. 108816 - 108816

Published: Feb. 6, 2024

Neural circuits related to language exhibit a remarkable ability reorganize and adapt in response visual deprivation. Particularly, early late blindness induce distinct neuroplastic changes the cortex, repurposing it for semantic processing. Interestingly, these functional provoke unique cognitive advantage – enhanced verbal working memory, particularly blindness. Yet, underlying neuromechanisms impact on memory-related remain not fully understood. Here, we applied brain-constrained neural network mimicking structural features of frontotemporal-occipital cortices, model conceptual acquisition The results revealed differential expansion conceptual-related into deprived areas depending timing loss, which is most prominent This recruitment fundamentally governed by biological principles circuit absence uncorrelated sensory input. Critically, degree constrained availability matter previously allocated experiences, as case Moreover, here shed light implication deprivation underpinnings revealing longer reverberatory activity 'blind models' compared sighted ones. These findings provide better understanding interplay between deprivations, neuroplasticity, processing memory.

Language: Английский

Citations

3
Verbal Symbols Support Concrete but Enable Abstract Concept Formation: Evidence From Brain‐Constrained Deep Neural Networks DOI Creative Commons
Fynn Raphael Dobler, Malte R. Henningsen‐Schomers, Friedemann Pulvermüller

et al.

Language Learning, Journal Year: 2024, Volume and Issue: 74(S1), P. 258 - 295

Published: May 19, 2024

Abstract Concrete symbols (e.g., sun , run ) can be learned in the context of objects and actions, thereby grounding their meaning world. However, it is controversial whether a comparable avenue to semantic learning exists for abstract democracy ). When we simulated putative brain mechanisms conceptual/semantic using brain‐constrained deep neural networks, instances concrete concepts outside language contexts led robust circuits generating substantial prolonged activations. In contrast, yielded much reduced only short‐lived activity. Crucially, when conceptual were wordforms, circuit activations became long‐lasting both meanings. These results indicate that, although correlates representations built from experiences alone, concept formation at neurobiological level enabled by requires correlated presence linguistic forms.

Language: Английский

Citations

3
Learning, Diagrams, and AI DOI

Marcel Danesi

Mathematics in mind, Journal Year: 2025, Volume and Issue: unknown, P. 73 - 93

Published: Jan. 1, 2025

Language: Английский

Citations

0
On the embodied nature of knowledge: From neurons to numbers DOI Creative Commons
Martin H. Fischer

Annals of the New York Academy of Sciences, Journal Year: 2024, Volume and Issue: 1537(1), P. 5 - 12

Published: June 29, 2024

Abstract Interdisciplinary investigations of the human mind through cognitive sciences have identified a key role body in representing knowledge. After characterizing knowledge at grounded, embodied, and situated levels, number is analyzed from this hierarchical perspective. Lateralized cortical processing coarse versus fine detail as grounding substrate for population stereotype few/left, many/right, which then contributes to number‐related sensory motor biases embodied levels. Implications perspective education rehabilitation are discussed.

Language: Английский

Citations

2
GEPAF: A non-monotonic generalized activation function in neural network for improving prediction with diverse data distributions characteristics DOI
Khush Attarde, Javed Sayyad

Neural Networks, Journal Year: 2024, Volume and Issue: 180, P. 106738 - 106738

Published: Sept. 17, 2024

Language: Английский

Citations

2
Regionally specific cortical lateralization of abstract and concrete verb processing: Magnetic mismatch negativity study DOI
Maxim Ulanov, Grigory Kopytin, Beatriz Bermúdez‐Margaretto

et al.

Neuropsychologia, Journal Year: 2024, Volume and Issue: 195, P. 108800 - 108800

Published: Jan. 21, 2024

Language: Английский

Citations

1