Learning produces an orthogonalized state machine in the hippocampus

Weinan Sun (), Johan Winnubst, Maanasa Natrajan, Chongxi Lai, Koichiro Kajikawa, Arco Bast, Michalis Michaelos, Rachel Gattoni, Carsen Stringer, Daniel Flickinger, James E. Fitzgerald and Nelson Spruston ()
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Weinan Sun: Howard Hughes Medical Institute
Johan Winnubst: Howard Hughes Medical Institute
Maanasa Natrajan: Howard Hughes Medical Institute
Chongxi Lai: Howard Hughes Medical Institute
Koichiro Kajikawa: Howard Hughes Medical Institute
Arco Bast: Howard Hughes Medical Institute
Michalis Michaelos: Howard Hughes Medical Institute
Rachel Gattoni: Howard Hughes Medical Institute
Carsen Stringer: Howard Hughes Medical Institute
Daniel Flickinger: Howard Hughes Medical Institute
James E. Fitzgerald: Howard Hughes Medical Institute
Nelson Spruston: Howard Hughes Medical Institute

Nature, 2025, vol. 640, issue 8057, 165-175

Abstract: Abstract Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, ‘state cells’). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

Date: 2025
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DOI: 10.1038/s41586-024-08548-w

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