Biologically-Inspired Modular Neural Networks Enhance Neuromorphic Computing Robustness

Researchers have developed a novel Decomposable Spiking Neural Network (D-SNN) architecture, drawing inspiration from the compartmentalized and efficient nature of biological neural systems. Unlike traditional artificial neural networks that often have globally entangled structures, D-SNNs structurally separate classification pathways into distinct, independent "experts." This modular design, optimized using a bio-inspired push-pull loss function, achieves performance comparable to fully dense networks on standard benchmarks like MNIST and CIFAR-10/100. Crucially, D-SNNs accomplish this with an order of magnitude fewer parameters and significantly lower firing rates, leading to reduced synaptic operations. Beyond efficiency, the D-SNN architecture offers enhanced robustness. Its isolated pathways inherently protect against catastrophic forgetting during sequential learning, a common challenge in AI. Furthermore, these distinct pathways generate auditable neural signals, improving the transparency and interpretability of the network's decisions. This verifiable and efficient design holds promise for deploying neuromorphic intelligence in resource-constrained edge computing environments.