Constrained Modulatory Reservoirs for Context-Dependent Computation

Biological recurrent circuits can reuse a common substrate by changing its operating regime rather than replacing the circuit itself. This principle is difficult to isolate in fully adaptive neural models, where recurrence, controllers, readouts, and internal representations can all absorb task structure. As a result, successful multi-context behavior does not reveal which route made the computation possible. We introduce Constrained Modulatory Reservoirs (CMR), a fixed-substrate framework for studying such reconfiguration with the main bypasses removed. The primary dynamics and low-rank perturbation basis are fixed, and the readout is denied direct access to the cue, forcing input-dependent adaptation to act through a limited set of recurrent connectivity changes. In transient-cue channel-selection tasks, this restriction makes a capacity transition visible. Low-loss behavior appears only when the available perturbation directions are sufficient, the selected change is retained across the cue-to-signal gap, and the primary dynamics express it during the response window. Controls show that this effect is not explained by low-dimensional cue representation alone. Allowing the substrate to learn hides the bottleneck, while activity- and gain-level alternatives fail to reproduce the same regime. CMR therefore reframes flexible recurrent computation as a question of where adaptive influence is allowed to enter a fixed dynamical system, making capacity, memory, and expression experimentally separable.