ConDenFormer: A Conductance-Inspired Spiking Transformer with Dendritic Memory and Adaptive Neuronal Dynamics

Spiking neural networks (SNNs) process information through discrete spikes, offering a promising approach to energy-efficient, brain-inspired computation. However, conventional current-based leaky integrate-and-fire (LIF) neurons face two limitations. First, weak inputs may fail to elicit spikes within a finite simulation window, causing subthreshold evidence to be lost from the emitted spike sequence. Second, their responses depend strongly on the temporal synchrony of excitatory inputs: dispersing a fixed number of spikes over time can substantially reduce the output firing rate compared with synchronized inputs. We address these limitations with ConDenFormer, a spiking Transformer inspired by conductance-dependent integration and dendritic–somatic computation. ConDenFormer integrates adaptive conductance exchange with persistent, source-specific dendritic memory. Learnable nonnegative conductances, modulated by pre-cancellation input activity and dendritic context, control how retained evidence contributes to the somatic membrane potential and its effective response timescale. Dendritic states persist across somatic spikes, preserving input history that would otherwise be discarded. We establish a dissipative exchange law, bounded driven dynamics, and a constructive separation of input sequences with identical source and temporal marginals. Across multiple benchmarks, ConDenFormer achieves 97.21% accuracy on CIFAR-10, 83.42% on CIFAR-100, 85.4% on CIFAR10-DVS, 99.0% on DVS128 Gesture, and 79.45% on ImageNet-1K with only 16.29M parameters. These results demonstrate that source-specific memory and adaptive conductance can improve the preservation and utilization of temporal evidence in spiking visual computation.