MTA-SNN: Revitalizing Convolutional Spiking Neural Networks via Multi-Threshold Parallel Neurons and Global-Local Aggregation

Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing via event-driven paradigms, but a significant trade-off exists between the hardware-friendly nature of convolutional architectures and the superior representational capacity of Transformer-based models, whose reliance on complex attention mechanisms can complicate asynchronous deployment. To bridge this gap, we propose MTA-SNN, which revitalizes convolutional SNNs by integrating attention-like capabilities using fully deployable primitives. Our approach introduces three core innovations: (1) Multi-Threshold Parallel Neurons (MTPN), which employ heterogeneous firing thresholds to expand information capacity in the channel dimension; (2) Global Local Aggregation (GLA) that captures long-range dependencies; and (3) Multi-Scale Position Encoding (MSPE) via dilated convolutions to explicitly enrich spatial context. Extensive experiments demonstrate that MTA-SNN establishes new state-of-the-art results among low-latency SNNs, achieving 83.07% on CIFAR-100 and 78.37% on ImageNet with a single timestep. This performance significantly outperforms existing convolutional SNNs and matches transformer-based counterparts requiring multiple timesteps, proving that high-performance vision SNNs can be realized within a strictly neuromorphic-friendly convolutional framework.