Many learning algorithms used as normative models in neuroscience or as candidate approaches for learning on neuromorphic chips learn by contrasting one set of network states with another. These Contrastive Learning (CL) algorithms are traditionally implemented with rigid, temporally non-local, and periodic learning dynamics that could limit the range of physical systems capable of harnessing CL. In this study, we build on recent work exploring how CL might be implemented by biological or neurmorphic systems and show that this form of learning can be made temporally local, and can still function even if many of the dynamical requirements of standard training procedures are relaxed. Thanks to a set of general theorems corroborated by numerical experiments across several CL models, our results provide theoretical foundations for the study and development of CL methods for biological and neuromorphic neural networks.

翻译：许多用于神经科学中的规范模型或作为神经形态芯片学习方法候选的算法，通过对比一组网络状态与另一组网络状态来进行学习。这些传统的对比学习（CL）算法通常采用刚性、时间非局部且周期性的学习动力学，这可能限制了能够利用CL的物理系统范围。在本研究中，我们基于最近探索生物或神经形态系统如何实现CL的工作，表明这种学习形式可以实现时间局部性，并且即使放宽标准训练过程中的许多动力学要求，仍然能够有效运行。得益于一套通用定理以及多个CL模型上的数值实验验证，我们的结果为生物和神经形态神经网络中CL方法的研究与开发提供了理论基础。