Timing Is All You Need: SpikeCore, Learnable Delays, and Gain Control for Neuromorphic Classification

Spiking neural networks (SNNs) offer a compelling path to energy-efficient, biologically plausible computation. Yet, a persistent gap remains: fully spiking models are difficult to train and incompatible with standard deep learning frameworks, while ANN-to-SNN conversion and hybrid approaches sacrifice either biological plausibility or deployment efficiency. We address this gap by introducing SpikeCore, a recurrent spiking unit designed to be natively compatible with ANN training frameworks, differentiable, gradient-friendly, and composable within standard pipelines, while retaining event-driven, spike-based computation. SpikeCore achieves this through learnable membrane decay and spike-history-dependent threshold adaptation, enabling rich temporal dynamics without surrogate gradient instability. Building on SpikeCore, we present a full spatio-temporal pipeline comprising: a two-stage learnable temporal delay hierarchy motivated by differential retinal ganglion latency and axonal conduction delays; a Multi-Branch Squeeze-Excitation (MBSE) backbone that extracts spatial features at multiple scales with channel-wise recalibration, using $11.8\times$ fewer parameters than equivalent residual architectures; and a Parameter-Free Temporal Gain Control (PFTGC) module that modulates each channel's temporal response without added parameters. Evaluated across five diverse benchmarks, the proposed architecture converges $5$--$6\times$ faster and requires ${\approx}3\times$ fewer timesteps than competitive baselines while matching or surpassing state-of-the-art models on accuracy.