Efficient Training of Deep Spiking Neural Networks with Input-Driven Derivative-Free Updates

Spiking neural networks (SNNs) are a promising paradigm for efficient neuromorphic computing, yet their training remains challenging due to the non-differentiability nature of spike firing. Backpropagation through time (BPTT) with surrogate gradients (SG) has become a mainstream approach due to its superior performance. However, this method incurs high computational and memory demands during training, while its update rule depends on specific neuronal dynamics. Furthermore, when targeting neuromorphic hardware implementation, the temporal error-propagation process and the full membrane-potential access required by SG are difficult to realize; these can introduce additional readout overhead, measurement noise, or perturbations to the system dynamics. To address these issues, we propose the input-driven derivative-free training (IDDFT) method. Rather than relying on membrane-potential-based surrogate derivatives, IDDFT constructs derivative-free error signals by applying a relaxed nonlinearity to the inputs, thereby avoiding the temporal error-propagation process and reducing the dependence of the update rule on specific neuronal dynamics. By eliminating access to internal membrane-potential states, our method reduces computational and memory costs, enhances compatibility with neuromorphic hardware, and enables training of black-box SNN models. We validate the IDDFT method through theoretical analysis and systematic experiments, demonstrating its effectiveness as an input-driven, derivative-free mechanism for constructing update signals. When integrated with a biologically inspired training strategy, IDDFT maintains comparable performance while substantially enhancing robustness to the choice of relaxed nonlinearities. Based on the resulting bio-inspired combined framework, we conduct evaluations on CIFAR-10, CIFAR-100, CIFAR10-DVS, and Tiny-ImageNet. Experimental results demonstrate that our method achieves performance comparable to BPTT and state-of-the-art methods, while exhibiting remarkable robustness under adversarial attacks.