Work

How much neuroscience does a neuroscientist need to know?

Year: 2026

Type: review

Abstract: How much of the brain's learned algorithms depend on the fact it is a brain? We argue: a lot, but surprisingly few details matter. We point to simple biological details-e.g. nonnegative firing and energetic/space budgets in connectionist architectures-which, when mixed with the requirements of solving a task, produce models that predict brain responses down to single-neuron tuning. We understand this as details constraining the set of plausible algorithms and their implementations, such that only 'brain-like' algorithms are learned. In particular, each biological detail breaks a symmetry in connectionist models (scale, rotation and permutation) leading to interpretable single-neuron responses that are meaningfully characteristic of particular algorithms. This view helps us not only understand the brain's choice of algorithm but also infer algorithm from measured neural responses. Further, this perspective aligns computational neuroscience with mechanistic interpretability in artificial intelligence (AI), suggesting a more unified approach to studying the mechanisms of intelligence, both natural and artificial.

Source: Current Opinion in Neurobiology

DOI: 10.1016/j.conb.2026.103287

Authors: James C.R. Whittington, William Dorrell

Institutions: University of Oxford, Gatsby Computational Neuroscience Unit, University College London

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,320

Funders: Wellcome Trust, European Research Council