Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition.
Explaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. We introduce neuro-cognitive multilevel causal modeling (NC-MCM), a framework that bridges the explanatory gap between neuronal activity and cognition by construing cog...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012674 |
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| author | Moritz Grosse-Wentrup Akshey Kumar Anja Meunier Manuel Zimmer |
| author_facet | Moritz Grosse-Wentrup Akshey Kumar Anja Meunier Manuel Zimmer |
| author_sort | Moritz Grosse-Wentrup |
| collection | DOAJ |
| description | Explaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. We introduce neuro-cognitive multilevel causal modeling (NC-MCM), a framework that bridges the explanatory gap between neuronal activity and cognition by construing cognitive states as (behaviorally and dynamically) causally consistent abstractions of neuronal states. Multilevel causal modeling allows us to interchangeably reason about the neuronal- and cognitive causes of behavior while maintaining a physicalist (in contrast to a strong dualist) position. We introduce an algorithm for learning cognitive-level causal models from neuronal activation patterns and demonstrate its ability to learn cognitive states of the nematode C. elegans from calcium imaging data. We show that the cognitive-level model of the NC-MCM framework provides a concise representation of the neuronal manifold of C. elegans and its relation to behavior as a graph, which, in contrast to other neuronal manifold learning algorithms, supports causal reasoning. We conclude the article by arguing that the ability of the NC-MCM framework to learn causally interpretable abstractions of neuronal dynamics and their relation to behavior in a purely data-driven fashion is essential for understanding biological systems whose complexity prohibits the development of hand-crafted computational models. |
| format | Article |
| id | doaj-art-b69ca9a8792545c0ad3a035bdb306ef6 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-b69ca9a8792545c0ad3a035bdb306ef62025-08-20T02:57:52ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101267410.1371/journal.pcbi.1012674Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition.Moritz Grosse-WentrupAkshey KumarAnja MeunierManuel ZimmerExplaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. We introduce neuro-cognitive multilevel causal modeling (NC-MCM), a framework that bridges the explanatory gap between neuronal activity and cognition by construing cognitive states as (behaviorally and dynamically) causally consistent abstractions of neuronal states. Multilevel causal modeling allows us to interchangeably reason about the neuronal- and cognitive causes of behavior while maintaining a physicalist (in contrast to a strong dualist) position. We introduce an algorithm for learning cognitive-level causal models from neuronal activation patterns and demonstrate its ability to learn cognitive states of the nematode C. elegans from calcium imaging data. We show that the cognitive-level model of the NC-MCM framework provides a concise representation of the neuronal manifold of C. elegans and its relation to behavior as a graph, which, in contrast to other neuronal manifold learning algorithms, supports causal reasoning. We conclude the article by arguing that the ability of the NC-MCM framework to learn causally interpretable abstractions of neuronal dynamics and their relation to behavior in a purely data-driven fashion is essential for understanding biological systems whose complexity prohibits the development of hand-crafted computational models.https://doi.org/10.1371/journal.pcbi.1012674 |
| spellingShingle | Moritz Grosse-Wentrup Akshey Kumar Anja Meunier Manuel Zimmer Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition. PLoS Computational Biology |
| title | Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition. |
| title_full | Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition. |
| title_fullStr | Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition. |
| title_full_unstemmed | Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition. |
| title_short | Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition. |
| title_sort | neuro cognitive multilevel causal modeling a framework that bridges the explanatory gap between neuronal activity and cognition |
| url | https://doi.org/10.1371/journal.pcbi.1012674 |
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