Intrinsic timescale placement shapes temporal computation and learning in recurrent networks

Neural systems comprise units with varying intrinsic timescales, but how this heterogeneity contributes to temporal computation and interacts with recurrent dynamics is not fully resolved. We show that intrinsic heterogeneity provides a richer temporal basis for a readout; however, in our temporal tasks, recurrence can largely reduce this advantage as diverse effective dynamics emerge. By fixing the recurrent graph, its weights, and the set of intrinsic timescales while varying only their assignment across nodes, we find that placement systematically changes the effective temporal repertoire. While degree and strength effects are topology dependent, alignment with slow recurrent modes shows more consistent effects across the tested topology families, indicating slow recurrent structure as a more general organizing coordinate. These dynamical effects also influence function: under fixed recurrent connectivity, slow mode alignment improves temporal computation in the tested tasks and topology families, with generally larger benefits at greater temporal demand, and serves as an inductive bias when recurrence is trainable. Finally, we test whether this principle is reflected in human cortical organization. Empirical magnetoencephalography (MEG) neural timescales are positively associated with slow modes of the structural connectome, and their observed spatial ordering produces a broader effective temporal repertoire than spatially rotated alternatives. Taken together, these results suggest that placement relative to slow recurrent structure is an organizing factor in how intrinsic timescale heterogeneity contributes to network dynamics, computation, and learning.