Learnable Membrane Time Constants for Structure Dynamics in Recurrent Spiking Neural Networks

In recurrent spiking neural networks (RSNNs), adapting connectivity structure represents an important means of RSNN optimization. However, their computational capabilities depend not only on connectivity structure but also on intrinsic neuronal dynamics. Previous studies have shown that heterogeneous neuronal timescales can improve learning in SNNs. Although existing approaches have made neuronal dynamics learnable, such adaptation is typically performed within a fixed connectivity structure. This raises the challenge of jointly adapting connectivity structure and heterogeneous neuronal dynamics without relying on backpropagation. To this end, we propose Directional Credit guided Forward Optimization (DCFO), a more biologically inspired, backpropagation-free learning framework that jointly adapts connectivity structure and neuron-specific membrane time constants. DCFO constructs candidate directions separately in the connectivity parameter space and the neuron-specific membrane-time-constant space, providing directional credit for each. The resulting directional credit guides forward-gradient updates in the two parameter spaces, enabling structural–dynamical co-adaptation. We evaluate DCFO on three Brax continuous-control tasks, including the 17-DoF Humanoid. The proposed method outperforms the state-of-the-art Evolving Connectivity method across a range of tasks and achieves performance competitive with deep recurrent neural network baselines. The learned neuronal dynamics further exhibit task-dependent heterogeneous neuronal timescales. These results demonstrate that DCFO enables effective structural–dynamical co-adaptation in RSNNs without relying on backpropagation.