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System-level brain modeling

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Title: System-level brain modeling
Authors: Johansson, Birger, Tjøstheim, Trond A., Balkenius, Christian
Contributors: Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Joint Faculties of Humanities and Theology, Departments, Department of Philosophy, Cognitive modeling, Lunds universitet, Humanistiska och teologiska fakulteterna, Institutioner, Filosofiska institutionen, Kognitiv modellering, Originator, Lund University, Joint Faculties of Humanities and Theology, Departments, Department of Philosophy, Cognitive Science, Lunds universitet, Humanistiska och teologiska fakulteterna, Institutioner, Filosofiska institutionen, Kognitionsvetenskap, Originator
Source: Frontiers in Computational Neuroscience.
Subject Terms: Natural Sciences, Computer and Information Sciences, Human Computer Interaction, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Människa-datorinteraktion (Interaktionsdesign), Humanities and the Arts, Other Humanities, Other Humanities not elsewhere specified, Humaniora och konst, Annan humaniora och konst, Övrig annan humaniora
Description: System-level brain modeling is a powerful method for building computational models of the brain and allows biologically motivated models to produce measurable behavior that can be tested against empirical data. System-level brain models occupy an intermediate position between detailed neuronal circuit models and abstract cognitive models. They are distinguished by their structural and functional resemblance to the brain, while also allowing for thorough testing and evaluation. In designing system-level brain models, several questions need to be addressed. What are the components of the system? At what level should these components be modeled? How are the components connected— that is, what is the structure of the system? What is the function of each component? What kind of information flows between the components, and how is that information coded? We mainly address models of cognitive abilities or subsystems that produce measurable behavior rather than models that to reproduce internal states, signals or activation patterns. In this method paper, we argue that system-level modeling is an excellent method for addressing complex cognitive and behavioral phenomena.
Access URL: https://doi.org/10.3389/fncom.2025.1607239
Database: SwePub
Description
Abstract:System-level brain modeling is a powerful method for building computational models of the brain and allows biologically motivated models to produce measurable behavior that can be tested against empirical data. System-level brain models occupy an intermediate position between detailed neuronal circuit models and abstract cognitive models. They are distinguished by their structural and functional resemblance to the brain, while also allowing for thorough testing and evaluation. In designing system-level brain models, several questions need to be addressed. What are the components of the system? At what level should these components be modeled? How are the components connected— that is, what is the structure of the system? What is the function of each component? What kind of information flows between the components, and how is that information coded? We mainly address models of cognitive abilities or subsystems that produce measurable behavior rather than models that to reproduce internal states, signals or activation patterns. In this method paper, we argue that system-level modeling is an excellent method for addressing complex cognitive and behavioral phenomena.
ISSN:16625188
DOI:10.3389/fncom.2025.1607239