Reward-Modulated Local Learning in Spiking Encoders: Controlled Benchmarks with STDP and Hybrid Rate Readouts

Researcher Debjyoti Chakraborty has published a significant paper titled 'Reward-Modulated Local Learning in Spiking Encoders: Controlled Benchmarks with STDP and Hybrid Rate Readouts' on arXiv, presenting a controlled empirical study of biologically motivated learning for handwritten digit recognition. The research evaluates two approaches: an STDP-inspired competitive proxy model and a practical hybrid benchmark, both built on spiking population encoders. The study reveals that while classical pixel baselines achieve 98.06-98.22% accuracy on sklearn digits, the local spike-based models initially reached 86-87% accuracy, but through careful optimization of normalization and reward-shaping settings, the best hybrid ablation achieved 95.52% accuracy with only ±1.11% variance.

The technical breakthrough centers on the hybrid update mechanism that's local in pre- and post-synaptic firing rates but uses supervised labels without timing-based credit assignment. The 2x2 analysis shows reward-shaping effects can reverse sign across different stabilization regimes, indicating that future neuromorphic computing research must report reward-shaping conclusions jointly with normalization settings. This work provides crucial benchmarks for developing more energy-efficient AI hardware that mimics biological neural processing, potentially enabling edge devices to run complex recognition tasks with significantly lower power consumption than traditional deep learning approaches.