对比信号依赖可塑性：脉冲神经回路中的自监督学习
Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits
AI总结：
本文提出对比信号依赖可塑性，将自监督思想用于并行事件驱动脉冲层的局部突触调整，并在循环脉冲网络训练中展现优于其他生物可信方法的稳定效果，且无需反馈突触。
摘要：
受脑启发的机器智能研究旨在开发计算模型，以模拟生物神经元系统所特有的信息处理与适应能力。这推动了脉冲神经网络的发展；这类模型有望解决现代深度神经网络固有的生物不可信性以及能效不足问题。在本研究中，我们应对的挑战是设计具有神经生物学依据的方案，用于调整脉冲网络的突触，并提出对比信号依赖可塑性。该过程推广了自监督学习背后的思想，以在并行运行的基于事件的神经元层架构中促进局部适应。我们的实验仿真表明，在训练循环脉冲网络时，该方法相对其他具有生物可信性的方法具有稳定优势，并且关键在于避开了对反馈突触等额外结构的需求。
英文摘要：
Brain-inspired machine intelligence research seeks to develop computational models that emulate the information processing and adaptability that distinguishes biological systems of neurons. This has led to the development of spiking neural networks, a class of models that promisingly addresses the biological implausibility and {the lack of energy efficiency} inherent to modern-day deep neural networks. In this work, we address the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks and propose contrastive-signal-dependent plasticity, a process which generalizes ideas behind self-supervised learning to facilitate local adaptation in architectures of event-based neuronal layers that operate in parallel. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks, crucially side-stepping the need for extra structure such as feedback synapses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
