The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing. One prevalent challenge encountered by SNNs is the trade-off between inference speed and accuracy, which requires sufficient time to achieve the desired level of performance. Drawing inspiration from animal behavior experiments that demonstrate a connection between decision-making reaction times, task complexity, and confidence levels, this study seeks to apply these insights to SNNs. The focus is on understanding how SNNs make inferences, with a particular emphasis on untangling the interplay between signal and noise in decision-making processes. The proposed theoretical framework introduces a new optimization objective for SNN training, highlighting the importance of not only the accuracy of decisions but also the development of predictive confidence through learning from past experiences. Experimental results demonstrate that SNNs trained according to this framework exhibit improved confidence expression, leading to better decision-making outcomes. In addition, a strategy is introduced for efficient decision-making during inference, which allows SNNs to complete tasks more quickly and can use stopping times as indicators of decision confidence. By integrating neuroscience insights with neuromorphic computing, this study opens up new possibilities to explore the capabilities of SNNs and advance their application in complex decision-making scenarios.

翻译：脉冲神经网络(SNNs)的节能与类脑信息处理能力备受关注，已成为类脑计算的关键组成部分。SNNs面临的一个普遍挑战是推理速度与精度间的权衡，需要充足时间才能达到理想性能水平。受动物行为实验中决策反应时间、任务复杂度与置信度水平间关联的启发，本研究旨在将这些洞见应用于SNNs。研究聚焦于理解SNNs的推理机制，特别强调厘清决策过程中信号与噪声的相互作用。所提出的理论框架为SNN训练引入了新的优化目标，不仅强调决策精度的重要性，更凸显了通过经验学习形成预测置信度的价值。实验结果表明，依据该框架训练的SNNs展现出更优的置信度表达能力，从而获得更佳的决策效果。此外，本研究还提出了一种推理过程中的高效决策策略，使SNNs能够更快完成任务，并可将停止时间作为决策置信度的指标。通过整合神经科学洞见与神经形态计算，本研究为探索SNNs能力、推进其在复杂决策场景中的应用开辟了新途径。