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

TL;DR

This study evaluates biologically inspired local learning rules in spiking neural networks for digit recognition, comparing STDP-inspired and hybrid approaches, revealing key factors like normalization and reward shaping that influence performance.

Contribution

It provides a controlled empirical comparison of local learning rules in spiking encoders, highlighting the impact of normalization and reward shaping on accuracy.

Findings

Hybrid models achieve up to 95.52% accuracy with ablations.

Spike-based models reach around 87% accuracy.

Reward shaping can reverse effects depending on stabilization regimes.

Abstract

This paper presents a controlled empirical study of biologically motivated local learning for handwritten digit recognition. We evaluate an STDP-inspired competitive proxy and a practical hybrid benchmark built on the same spiking population encoder. The proxy is motivated by leaky integrate-and-fire E/I circuit models with three-factor delayed reward modulation. The hybrid update is local in pre x post rates but uses supervised labels and no timing-based credit assignment. On sklearn digits, fixed-seed evaluation shows classical pixel baselines from 98.06 to 98.22% accuracy, while local spike-based models reach 86.39 +/- 4.75% (hybrid default) and 87.17 +/- 3.74% (STDP-style competitive proxy). Ablations identify normalization and reward-shaping settings as the strongest observed levers, with a best hybrid ablation of 95.52 +/- 1.11%. A network-free synthetic temporal benchmark supports the same timing-versus-rate interpretation under matched local-update training. A descriptive 2x2 analysis further shows reward-shaping effects can reverse sign across stabilization regimes, so reward-shaping conclusions should be reported jointly with normalization settings.