Equivalent volitional learning emerges through circuit-specific population dynamics in motor cortex and hippocampus

Abstract

Learning operates across different brain circuits to associate population activity patterns with desired outcomes, and to enable volitional reactivation of those patterns to control behavior. These circuits differ profoundly in their architecture and dynamical regimes, yet which features of learning are shared across them and which arise from circuit-specific implementations remains unknown. Here, we use a brain-computer interface (BCI) to train mice to modulate the activity of selected neuronal ensembles toward configurations that trigger reward delivery. By making reward delivery contingent directly on population activity, we impose an identical associative learning problem on two circuits with distinct dynamical regimes: the primary motor cortex (M1) and the hippocampal area CA3. Mice acquired robust volitional control in both regions, and learning produced a set of shared signatures across circuits, including modulation of reward-controlling neurons, network-level sparsification, and greater exploration of reward-related activity patterns. These signatures were underpinned by distinct population dynamics: M1 activity flowed continuously through reward-associated states, whereas CA3 activity traced approach-and-return dynamics around them. Recurrent network models endowed with distinct minimal connectivity constraints chosen to reflect the dominant dynamical regime associated with each region captured key features of these shared signatures and region-specific dynamics, indicating that local architectural constraints are sufficient to account for the distinct implementations of learning. These findings indicate that equivalent learning outcomes arise from divergent dynamical implementations across architecturally distinct circuits. This principled degeneracy reveals that learning is not a single canonical solution, but is implemented through multiple circuit-specific mechanisms shaped by local network architecture.