ArticlePLoS computational biology2022
Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Human cortical networks trade communication efficiency for computational reliability.Science advances · 2026Article
- Simultaneously determining regional heterogeneity and connection directionality from neural activity and symmetric connection.PLoS computational biology · 2025Article
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- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Dynamics of specialization in neural modules under resource constraints.Nature communications · 2025Article
- General relationship of local topologies, global dynamics, and bifurcation in cellular networks.NPJ systems biology and applications · 2024Article
- Nonlinear fusion is optimal for a wide class of multisensory tasks.PLoS computational biology · 2024Article
- Downstream network transformations dissociate neural activity from causal functional contributions.Scientific reports · 2024Article
- When Neural Activity Fails to Reveal Causal Contributions.bioRxiv : the preprint server for biology · 2023Article
- The Entangled Brain.Journal of cognitive neuroscience · 2023Article
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2 authors.
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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.
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