Reliable but Compressed: Longer Timescales Lower Dimensionality with Readout-Dependent Task Costs

When a neural representation becomes more reliable and lower-dimensional, does it necessarily become less useful downstream? We test this question in a fixed auditory spiking network by increasing membrane and synaptic timescales while matching mean firing rates. Longer timescales increase reliability to input perturbations but reduce effective dimensionality, producing a reliable-but-compressed representation. Crucially, the apparent downstream cost of this compression is not stable across readout procedures: on the same frozen features, an expanded regularized readout substantially attenuates the long-timescale penalty while improving absolute predictive performance. The geometric change therefore persists, whereas its inferred task cost changes with the evaluation procedure. Mechanistic controls show that compression is much weaker in a separately activity-matched continuous implementation, while slower local state recovery closely follows passive membrane decay. Within this controlled system, these results separate three effects that are easily conflated—representation geometry, perturbation response, and fitted task accessibility—and show that lower dimensionality alone is insufficient to infer an intrinsic loss of downstream utility.