Specific connectivity optimizes learning in thalamocortical loops

Kaushik J. Lakshminarasimhan, Marjorie Xie,Jeremy D. Cohen, Britton A. Sauerbrei,Adam W. Hantman, Ashok Litwin-Kumar,Sean Escola

Cell Reports(2024)

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摘要
Thalamocortical loops have a central role in cognition and motor control, but precisely how they contribute to these processes is unclear. Recent studies showing evidence of plasticity in thalamocortical synapses indicate a role for the thalamus in shaping cortical dynamics through learning. Since signals undergo a compression from the cortex to the thalamus, we hypothesized that the computational role of the thalamus depends critically on the structure of corticothalamic connectivity. To test this, we identified the optimal corticothalamic structure that promotes biologically plausible learning in thalamocortical synapses. We found that corticothalamic projections specialized to communicate an efference copy of the cortical output benefit motor control, while communicating the modes of highest variance is optimal for working memory tasks. We analyzed neural recordings from mice performing grasping and delayed discrimination tasks and found corticothalamic communication consistent with these predictions. These results suggest that the thalamus orchestrates cortical dynamics in a functionally precise manner through structured connectivity.
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关键词
thalamocortical loop,corticothalamic feedback,thalamus,biologically plausible learning,recurrent neural network,meta-learning,random feedback,motor learning,working memory
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