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Peer-reviewedCC BYNature Communications

Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training

  • Lynn K. A. Sörensen1,2,3,4
  • James J. DiCarlo5,6,7
  • Kohitij Kar8,9
9 affiliations
  1. 1Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, USA. [email protected]
  2. 2Centre for Integrative and Applied Neuroscience, York University, Toronto, Canada. [email protected]
  3. 3MIT Quest for Intelligence, Cambridge, USA. [email protected]
  4. 4McGovern Institute for Brain Research, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, USA. [email protected]
  5. 5Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, USA
  6. 6MIT Quest for Intelligence, Cambridge, USA
  7. 7McGovern Institute for Brain Research, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, USA
  8. 8Centre for Integrative and Applied Neuroscience, York University, Toronto, Canada. [email protected]
  9. 9Department of Biology, Centre for Vision Research, York University, Toronto, Canada. [email protected]
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Abstract

How does the primate brain coordinate plasticity when learning to discriminate new objects? We measured consequences of object learning on inferior temporal (IT) cortex, a key waypoint supporting object recognition in the ventral visual stream, in male macaques. Neural activity in task-trained monkeys' IT showed increased object selectivity, enhanced linear separability across objects, and more object-invariant representations compared to task-naïve monkeys. To model these differences, we developed a computational framework using anatomically-mapped artificial neural network (ANN) models of the ventral stream with various learning algorithms. Simulations revealed that gradient-based, performance-optimizing updates of ANN internal representations accurately approximated observed IT cortex changes. These models predict novel training-induced phenomena in the IT cortex, including changes independent of object identity and IT's alignment with behavior. This convergence between empirical measurements and model predictions suggests ventral stream plasticity follows task optimization principles well-approximated by gradient descent, enabling accurate predictions about visual plasticity and generalization to test images.

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Cite

BibTeXRIS
BibTeX
@article{sorensen2026hierarchical,
  title = {{Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training}},
  author = {Lynn K. A. Sörensen and James J. DiCarlo and Kohitij Kar},
  journal = {Nature Communications},
  year = {2026},
  month = jul,
  doi = {10.1038/s41467-026-74816-0},
  url = {https://doi.org/10.1038/s41467-026-74816-0},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training
AU  - Lynn K. A. Sörensen
AU  - James J. DiCarlo
AU  - Kohitij Kar
PY  - 2026
DA  - 2026/07/08
JO  - Nature Communications
DO  - 10.1038/s41467-026-74816-0
UR  - https://doi.org/10.1038/s41467-026-74816-0
AB  - How does the primate brain coordinate plasticity when learning to discriminate new objects? We measured consequences of object learning on inferior temporal (IT) cortex, a key waypoint supporting object recognition in the ventral visual stream, in male macaques. Neural activity in task-trained monkeys' IT showed increased object selectivity, enhanced linear separability across objects, and more object-invariant representations compared to task-naïve monkeys. To model these differences, we developed a computational framework using anatomically-mapped artificial neural network (ANN) models of the ventral stream with various learning algorithms. Simulations revealed that gradient-based, performance-optimizing updates of ANN internal representations accurately approximated observed IT cortex changes. These models predict novel training-induced phenomena in the IT cortex, including changes independent of object identity and IT's alignment with behavior. This convergence between empirical measurements and model predictions suggests ventral stream plasticity follows task optimization principles well-approximated by gradient descent, enabling accurate predictions about visual plasticity and generalization to test images.
SN  - 2041-1723
ER  -