From Spectral Responses to Task Effects: Auditing Frequency Dependence in Spiking Neural Networks

Connecting a spectral response in a spiking neural network to task behavior requires measurements beyond band energy. We present a frequency-lineage protocol that separates operator responses, fixed-decoder responses, and predictive interventions. A MaxFormer trace locates the spread of an input-band perturbation at the first LIF, and a three-architecture, three-dataset panel records cross-frequency decoder responses. Matched operator experiments place this observation in context: ordinary ReLU and tanh also generate cross-frequency components, while integration and reset change task performance. In three frozen MaxFormer networks, equal-budget binary patches selected using same- or cross-band targets reduce classification loss, but the tested joint non-source target does not. These patches preserve binary states without strict band isolation; continuous Fourier patches give a different joint response at a larger dose. Separately, learned natural residuals show no consistent predictive advantage across the development panel, and beating a control need not mean beating the baseline. These findings motivate distinct tests for where a frequency perturbation acts, how a decoder responds, and whether an intervention benefits the task. They constrain explanations based on spectral attenuation without identifying information preservation or loss.