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：许多在神经科学中作为规范模型或在神经形态芯片上作为候选学习方法的学习算法，通过对比一组网络状态与另一组状态来进行学习。这些对比学习算法传统上采用刚性、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对比学习的物理系统的范围。在本研究中，我们基于探索对比学习如何由生物或神经形态系统实现的近期工作，证明了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的许多动力学要求，它仍然能够有效运作。得益于一系列一般性定理以及跨多个对比学习模型的数值实验的佐证，我们的结果为生物和神经形态神经网络的对比学习方法的研究与开发提供了理论基础。