논문명 |
Parametric control of flexible timing through low-dimensional neural manifolds |
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저자 |
Manuel Beiran, Nicolas Meirhaeghe, Hansem Sohn, Mehrdad Jazayeri and Srdjan Ostojic |
저널 |
Neuron 111(5), e8-(2023) |
Abstract |
www.sciencedirect.com/science/article/pii/S0896627322010893?via%3Dihub |
Biological brains possess an unparalleled ability to adapt behavioral responses to changing stimuli and environments. How neural processes enable this capacity is a fundamental open question. Previous works have identified two candidate mechanisms: a low-dimensional organization of neural activity and a modulation by contextual inputs. We hypothesized that combining the two might facilitate generalization and adaptation in complex tasks. We tested this hypothesis in flexible timing tasks where dynamics play a key role. Examining trained recurrent neural networks, we found that confining the dynamics to a low-dimensional subspace allowed tonic inputs to parametrically control the overall input-output transform, enabling generalization to novel inputs and adaptation to changing conditions. Reverse-engineering and theoretical analyses demonstrated that this parametric control relies on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds while preserving their geometry. Comparisons with data from behaving monkeys confirmed the behavioral and neural signatures of this mechanism. |