Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation

TL;DR

This paper introduces a biologically inspired, scalable learning framework for deep recurrent spiking neural networks that avoids backpropagation, using local plasticity, fixed long-range connections, and neuromodulation.

Contribution

It proposes a novel structured recurrent SNN architecture with local learning rules and fixed long-range connectivity, enabling scalable supervised training without backpropagation.

Findings

Achieves stable learning on benchmark classification tasks.

Supports deep recurrent computation with local synaptic updates.

Demonstrates hardware feasibility and competitive performance.

Abstract

Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however, scalable learning in deep recurrent architectures with sparse connectivity remains a major challenge. In this work, we propose a structured multi-layer recurrent SNN architecture composed of locally dense recurrent layers augmented with sparse small-world long-range projections to a readout population. The long-range connectivity is largely fixed, preserving routing efficiency and hardware scalability, while synaptic adaptation is performed using strictly local plasticity mechanisms. To enable supervised learning without backpropagation or surrogate gradients, we introduce a biologically motivated learning framework that combines: (i) population-based winner-take-all (WTA) teaching signals at the output layer, (ii) fixed random broadcast alignment feedback pathways, and (iii) low-dimensional modulatory neuron populations that gate synaptic updates through three-factor learning rules with eligibility traces. This design supports deep recurrent computation with sparse global communication and purely local synaptic updates. We analyze the algorithmic properties, computational complexity, and hardware feasibility of the proposed approach, and demonstrate stable learning and competitive performance on benchmark classification tasks. The results highlight the potential of structured recurrence and neuromodulatory learning to enable scalable, hardware-compatible SNN training beyond gradient-based methods.