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Peer-reviewedCC BYNature Neuroscience

Reciprocal connections dynamically build consensus between neocortical areas

  • Mitra Javadzadeh1,2,3
  • Marine Schimel4,5
  • Sonja B. Hofer6
  • Yashar Ahmadian7
  • Guillaume Hennequin7
7 affiliations
  1. 1Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. [email protected]
  2. 2Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK. [email protected]
  3. 3Sainsbury Wellcome Centre, University College London, London, UK. [email protected]
  4. 4Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK. [email protected]
  5. 5Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. [email protected]
  6. 6Sainsbury Wellcome Centre, University College London, London, UK
  7. 7Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK
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Abstract

The neocortex is organized into specialized areas. Although computations within individual areas have been well studied, it is unclear how these regions function collectively and reconcile potential conflicts to form coherent percepts and decisions. We investigated the joint dynamics of primary (V1) and higher-order lateromedial (LM) visual areas in mice using simultaneous multi-area electrophysiological recordings along with focal optogenetic perturbations to causally manipulate neural activity. We used data-driven nonlinear system identification to construct biologically constrained latent circuit models of both areas. This approach revealed that reciprocal excitatory connections between V1 and LM implement an approximate line attractor in their joint dynamics: this selectively slows the decay of congruent activity patterns while accelerating the decay of inconsistent ones, thereby dynamically achieving inter-area consensus. This mechanism predicts different timescales for consistent versus inconsistent activity patterns across areas, which we verified in our data. These findings, together with our mechanistic theory, identify dynamic consensus building as a general principle of distributed cortical computation.

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Cite

BibTeXRIS
BibTeX
@article{javadzadeh2026reciprocal,
  title = {{Reciprocal connections dynamically build consensus between neocortical areas}},
  author = {Mitra Javadzadeh and Marine Schimel and Sonja B. Hofer and Yashar Ahmadian and Guillaume Hennequin},
  journal = {Nature Neuroscience},
  year = {2026},
  month = sep,
  doi = {10.1038/s41593-026-02437-3},
  url = {https://doi.org/10.1038/s41593-026-02437-3},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Reciprocal connections dynamically build consensus between neocortical areas
AU  - Mitra Javadzadeh
AU  - Marine Schimel
AU  - Sonja B. Hofer
AU  - Yashar Ahmadian
AU  - Guillaume Hennequin
PY  - 2026
DA  - 2026/09/18
JO  - Nature Neuroscience
DO  - 10.1038/s41593-026-02437-3
UR  - https://doi.org/10.1038/s41593-026-02437-3
AB  - The neocortex is organized into specialized areas. Although computations within individual areas have been well studied, it is unclear how these regions function collectively and reconcile potential conflicts to form coherent percepts and decisions. We investigated the joint dynamics of primary (V1) and higher-order lateromedial (LM) visual areas in mice using simultaneous multi-area electrophysiological recordings along with focal optogenetic perturbations to causally manipulate neural activity. We used data-driven nonlinear system identification to construct biologically constrained latent circuit models of both areas. This approach revealed that reciprocal excitatory connections between V1 and LM implement an approximate line attractor in their joint dynamics: this selectively slows the decay of congruent activity patterns while accelerating the decay of inconsistent ones, thereby dynamically achieving inter-area consensus. This mechanism predicts different timescales for consistent versus inconsistent activity patterns across areas, which we verified in our data. These findings, together with our mechanistic theory, identify dynamic consensus building as a general principle of distributed cortical computation.
SN  - 1097-6256
SN  - 1546-1726
ER  -