aiqbench analysis
This theoretical work establishes a mathematical link between biological constraints like energy efficiency and the emergence of disentangled representations in both neural networks and brain cell types. By proving that these simple physical limits naturally enforce selectivity for single task factors, the study offers new insights into how variational autoencoders can mimic biological efficiency without explicit architectural engineering. It bridges fundamental neuroscience with machine learning theory, providing a rigorous framework for understanding why specific neuron populations encode distinct interpretable variables.
Disentanglement with Biological Constraints: A Theory of Functional Cell Types
arXiv:2210.01768v3 Announce Type: replace-cross Abstract: Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representations by enforcing neurons to become selective for single factors of task variation. We demonstrate these constraints lead to disentanglement in a variety of tasks and architectures, including variational autoencoders. We also use this theory to explain why the brain partitions its cells into distinct cell types such as grid and object-vector cells, and also explain when the brain instead entangles representations in response to entangled task factors. Overall, this work provides a mathematical understanding of why single neurons in the brain often represent single human-interpretable factors, and steps towards an understanding task structure shapes the structure of brain representation.
Abstract:Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representations by enforcing neurons to become selective for single factors of task variation. We demonstrate these constraints lead to disentanglement in a variety of tasks and architectures, including variational autoencoders. We also use this theory to explain why the brain partitions its cells into distinct cell types such as grid and object-vector cells, and also explain when the brain instead entangles representations in response to entangled task factors. Overall, this work provides a mathematical understanding of why single neurons in the brain often represent single human-interpretable factors, and steps towards an understanding task structure shapes the structure of brain representation.
Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
arXiv:2210.01768 [q-bio.NC]
(or arXiv:2210.01768v3 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2210.01768 Focus to learn more arXiv-issued DOI via DataCite
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