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• W4283068005 abstract "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." @default.
• W4283068005 created "2022-06-19" @default.
• W4283068005 creator A5005391007 @default.
• W4283068005 creator A5045659246 @default.
• W4283068005 date "2022-06-17" @default.
• W4283068005 modified "2023-09-26" @default.
• W4283068005 title "Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain" @default.
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• W4283068005 doi "https://doi.org/10.1371/journal.pcbi.1010250" @default.
• W4283068005 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35714139" @default.
• W4283068005 hasPublicationYear "2022" @default.
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