The Neural Division of Labor: Biologically-Inspired Modular Architectures for Robust Neuromorphic Computing

2026-08-08

AI Summary

This work addresses the challenges of deploying conventional artificial neural networks on edge devices—namely, their dense global connectivity, catastrophic forgetting, and opaque decision-making—by introducing Decomposable Spiking Neural Networks (D-SNNs). Inspired by the modular organization of biological nervous systems, D-SNNs structurally isolate classification pathways into independent expert modules and employ a biologically inspired push-pull loss function for optimization. The proposed approach achieves accuracy comparable to dense networks on MNIST, Fashion-MNIST, and CIFAR-10/100 benchmarks while reducing model parameters by an order of magnitude and decreasing spike rates and synaptic operations by several orders of magnitude. Moreover, D-SNNs inherently resist catastrophic forgetting and enable auditable decision processes, offering a promising pathway toward efficient, interpretable, and robust neuromorphic computing on resource-constrained hardware.

Abstract

Biological neural systems achieve high efficiency and robustness through compartmentalized architectures. In contrast, modern artificial neural networks rely on globally entangled structures, which obscure decision logic and suffer from catastrophic forgetting. Here, we report a Decomposable Spiking Neural Network (D-SNN) that eliminates global synaptic entanglement by structurally isolating classification pathways into independent experts. Optimized via a bio-inspired push-pull loss function, the D-SNN achieves competitive accuracies on MNIST, Fashion-MNIST, and CIFAR-10/100 benchmarks. This modular approach matches the performance of fully dense networks while utilizing an order of magnitude fewer parameters. In addition, our networks operate with up to several orders of magnitude lower firing rates and fewer synaptic operations. Furthermore, physically severing connections between experts provides inherent protection against catastrophic forgetting during sequential learning. Crucially, these isolated pathways generate auditable neural signals, increasing decision transparency. This biomimetic, verifiable architecture establishes an efficient foundation for deploying deterministic neuromorphic intelligence in resource-constrained edge environments.

Problem

• catastrophic forgetting
• global entanglement
• decision transparency
• modular architecture
• neuromorphic computing

Innovation

• Decomposable Spiking Neural Network
• modular architecture
• catastrophic forgetting
• neuromorphic computing
• decision transparency