Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain.
Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach has gained new prominence through the arrival of modern perturbation techniques with unprecedented levels of spatiotemporal precision. While inferences...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010250 |
| _version_ | 1811294973102915584 |
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| author | Kayson Fakhar Claus C Hilgetag |
| author_facet | Kayson Fakhar Claus C Hilgetag |
| author_sort | Kayson Fakhar |
| collection | DOAJ |
| description | Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach has gained new prominence through the arrival of modern perturbation techniques with unprecedented levels of spatiotemporal precision. While inferences drawn from brain perturbations are conceptually powerful, they face methodological difficulties. Particularly, they are challenged to disentangle the true causal contributions of the involved elements, since often functions arise from coalitions of distributed, interacting elements, and localized perturbations have unknown global consequences. To elucidate these limitations, we systematically and exhaustively lesioned a small artificial neural network (ANN) playing a classic arcade game. We determined the functional contributions of all nodes and links, contrasting results from sequential single-element perturbations with simultaneous perturbations of multiple elements. We found that lesioning individual elements, one at a time, produced biased results. By contrast, multi-site lesion analysis captured crucial details that were missed by single-site lesions. We conclude that even small and seemingly simple ANNs show surprising complexity that needs to be addressed by multi-lesioning for a coherent causal characterization. |
| first_indexed | 2024-04-13T05:25:26Z |
| format | Article |
| id | doaj.art-21996cb9081e41a9b2fcf72cfc67da26 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T05:25:26Z |
| publishDate | 2022-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-21996cb9081e41a9b2fcf72cfc67da262022-12-22T03:00:36ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-06-01186e101025010.1371/journal.pcbi.1010250Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain.Kayson FakharClaus C HilgetagLesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach has gained new prominence through the arrival of modern perturbation techniques with unprecedented levels of spatiotemporal precision. While inferences drawn from brain perturbations are conceptually powerful, they face methodological difficulties. Particularly, they are challenged to disentangle the true causal contributions of the involved elements, since often functions arise from coalitions of distributed, interacting elements, and localized perturbations have unknown global consequences. To elucidate these limitations, we systematically and exhaustively lesioned a small artificial neural network (ANN) playing a classic arcade game. We determined the functional contributions of all nodes and links, contrasting results from sequential single-element perturbations with simultaneous perturbations of multiple elements. We found that lesioning individual elements, one at a time, produced biased results. By contrast, multi-site lesion analysis captured crucial details that were missed by single-site lesions. We conclude that even small and seemingly simple ANNs show surprising complexity that needs to be addressed by multi-lesioning for a coherent causal characterization.https://doi.org/10.1371/journal.pcbi.1010250 |
| spellingShingle | Kayson Fakhar Claus C Hilgetag Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. PLoS Computational Biology |
| title | Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. |
| title_full | Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. |
| title_fullStr | Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. |
| title_full_unstemmed | Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. |
| title_short | Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain. |
| title_sort | systematic perturbation of an artificial neural network a step towards quantifying causal contributions in the brain |
| url | https://doi.org/10.1371/journal.pcbi.1010250 |
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