Topic: contextual decision making

Recent studies have proposed that the activity of both recurrent and biological neural networks is constrained to a task manifold. This manifold is often intrinsically lower dimensional than the full neural state space, and its geometry is directly related to task computations. In parallel, there has been a surge of interest in training RNNs to solve behavioural tasks in order to generate testable hypotheses regarding the computations employed by neural circuits. However, means of using task-trained RNNs to test hypotheses regarding the geometry of neural data manifolds are currently lacking.