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Peer-reviewedCC BYNature Neuroscience

Learning shapes neural geometry in the primate prefrontal cortex

  • Michał J. Wójcik1,2
  • Jake Patrick Stroud3,4
  • Dante Wasmuht5
  • Makoto Kusunoki A5,6
  • Mikiko Kadohisa5,6
  • Mark J. Buckley5
  • Rui Ponte Costa7
  • Nicholas E. Myers8,5
  • Laurence Tudor Hunt5,9
  • John Duncan5,6
  • Mark G. Stokes5
9 affiliations
  1. 1Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. [email protected]
  2. 2Department of Experimental Psychology, University of Oxford, Oxford, UK. [email protected]
  3. 3Coherence Neuro Global, San Francisco, CA, USA
  4. 4Department of Engineering, University of Cambridge, Cambridge, UK
  5. 5Department of Experimental Psychology, University of Oxford, Oxford, UK
  6. 6MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  7. 7Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
  8. 8Centre for Neurotechnology, Neuromodulation and Neurotherapeutics, University of Nottingham, Nottingham, UK
  9. 9Department of Psychiatry, University of Oxford, Oxford, UK
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Abstract

The relationship between the geometry of neural representations and the task being performed is a central question in neuroscience. The primate prefrontal cortex (PFC) is a primary focus of inquiry, as it can encode information with geometries that either rely on past experience or are experience agnostic. One hypothesis is that PFC representations should evolve with learning, from a format that supports exploration of all possible task rules to a format that minimizes the encoding of task-irrelevant features and supports generalization. Here we test this idea by recording neural activity from the macaque PFC when learning a new rule ('XOR rule') from scratch. We show that PFC representations progress from being high dimensional, nonlinear and randomly mixed to low dimensional and rule selective. Upon generalizing the rule to new stimuli, these representations further evolve into an abstract, stimulus-invariant geometry. These findings reconcile previously conflicting accounts of PFC function by demonstrating how neural representations adapt across distinct stages of learning.

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Cite

BibTeXRIS
BibTeX
@article{wojcik2026learning,
  title = {{Learning shapes neural geometry in the primate prefrontal cortex}},
  author = {Michał J. Wójcik and Jake Patrick Stroud and Dante Wasmuht and Makoto Kusunoki A and Mikiko Kadohisa and Mark J. Buckley and Rui Ponte Costa and Nicholas E. Myers and Laurence Tudor Hunt and John Duncan and Mark G. Stokes},
  journal = {Nature Neuroscience},
  year = {2026},
  month = jun,
  doi = {10.1038/s41593-026-02333-w},
  url = {https://doi.org/10.1038/s41593-026-02333-w},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Learning shapes neural geometry in the primate prefrontal cortex
AU  - Michał J. Wójcik
AU  - Jake Patrick Stroud
AU  - Dante Wasmuht
AU  - Makoto Kusunoki A
AU  - Mikiko Kadohisa
AU  - Mark J. Buckley
AU  - Rui Ponte Costa
AU  - Nicholas E. Myers
AU  - Laurence Tudor Hunt
AU  - John Duncan
AU  - Mark G. Stokes
PY  - 2026
DA  - 2026/06/25
JO  - Nature Neuroscience
DO  - 10.1038/s41593-026-02333-w
UR  - https://doi.org/10.1038/s41593-026-02333-w
AB  - The relationship between the geometry of neural representations and the task being performed is a central question in neuroscience. The primate prefrontal cortex (PFC) is a primary focus of inquiry, as it can encode information with geometries that either rely on past experience or are experience agnostic. One hypothesis is that PFC representations should evolve with learning, from a format that supports exploration of all possible task rules to a format that minimizes the encoding of task-irrelevant features and supports generalization. Here we test this idea by recording neural activity from the macaque PFC when learning a new rule ('XOR rule') from scratch. We show that PFC representations progress from being high dimensional, nonlinear and randomly mixed to low dimensional and rule selective. Upon generalizing the rule to new stimuli, these representations further evolve into an abstract, stimulus-invariant geometry. These findings reconcile previously conflicting accounts of PFC function by demonstrating how neural representations adapt across distinct stages of learning.
SN  - 1097-6256
SN  - 1546-1726
ER  -