Thresholds, Not Normalization: A Homeostatic Spiking Layer at Parity Without the Overhead

Spiking neural networks processing long event streams frequently rely on normalization to match input currents to a fixed firing threshold. However, this reliance introduces batch reductions, floating-point operations, and per-timestep statistics that neuromorphic hardware struggles to accommodate efficiently. We propose adapting the threshold itself instead of the incoming current. We introduce HomeoLayer, a leaky integrate-and-fire layer featuring ${\ell }_2$-normalized weights, a per-feature affine transform, closed-form spike-probability gradients, and an end-to-end learned threshold controller. Pooling this controller over the batch or layer recovers the classic BatchNorm/LayerNorm dichotomy. At inference, HomeoLayer utilizes only additions and multiplications, stores no running statistics, and remains completely batch-independent. Across two architectures and four datasets (SHD, SSC, GSC, and N-MNIST), HomeoLayer achieves parity with per-timestep batch normalization while maintaining a fraction of the per-neuron state. Furthermore, learning the firing rate target eliminates the need for manual threshold tuning. Transplanting the controller into existing adaptive models (C-SiLIF and RadLIF) successfully replaces native normalization with a marginal accuracy cost. This work is open-sourced at https://anonymous.4open.science/r/snn_homeolayer-E003.