Parametric control of flexible timing through low-dimensional neural manifolds

Abstract

AbstractBiological brains possess an unparalleled ability to generalize adaptive behavioral responses from only a few examples. How neural processes enable this capacity to extrapolate is a fundamental open question. A prominent but underexplored hypothesis suggests that generalization is facilitated by a low-dimensional organization of collective neural activity. Here we tested this hypothesis in the framework of 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 and enabled smooth extrapolation to inputs well beyond the training range. Reverse-engineering and theoretical analyses demonstrated that this parametric control of extrapolation relies on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds in activity space while preserving their geometry. Comparisons with neural data from behaving monkeys confirmed the geometric and dynamical signatures of this mechanism.