A theory of multi-task computation and task selection
Neural activity during the performance of a stereotyped behavioral task is often described as low-dimensional, occupying only a limited region in the space of all firing-rate patterns. This region has been referred to as the "neural manifold" associated with a task. More recently, recordings of neural activity in animals challenged to perform multiple tasks have suggested that each task is associated with a different low-dimensional manifold. What connectivity structures underlie this flexibility in neural dynamics, and how is interference between the dynamics associated with different tasks avoided? We develop a theoretical model for multi-task computation in nonlinear recurrent neural networks whose connectivity is constructed as a weighted sum of many low-rank components, each encoding the dynamics associated with a different task. The model demonstrates that interference between different tasks dynamics limits flexible multi-tasking and can lead to chaotic fluctuations. However, small modulations of a networks effective connectivity overcome this interference. We derive the conditions that enable such task selection and characterize both single-neuron and population statistics in task-selected and unselected states. The model reveals the requirements for a single network to produce distinct dynamics confined to distinct neural manifolds and suggests circuit mechanisms that support this capability. Using the model, we propose different hypotheses for explaining the origin of high-dimensional neural activity in large-scale recordings.