The study of plasticity in spiking neural networks is an active area of research. However, simulations that involve complex plasticity rules, dense connectivity/high synapse counts, complex neuron morphologies, or extended simulation times can be computationally demanding. The BrainScaleS-2 neuromorphic architecture has been designed to address this challenge by supporting "hybrid" plasticity, which combines the concepts of programmability and inherently parallel emulation. In particular, observables that are expensive in numerical simulation, such as per-synapse correlation measurements, are implemented directly in the synapse circuits. The evaluation of the observables, the decision to perform an update, and the magnitude of an update, are all conducted in a conventional program that runs simultaneously with the analog neural network. Consequently, these systems can offer a scalable and flexible solution in such cases. While previous work on the platform has already reported on the use of different kinds of plasticity, the descriptions for the spiking neural network experiment topology and protocol, and the plasticity algorithm have not been connected. In this work, we introduce an integrated framework for describing spiking neural network experiments and plasticity rules in a unified high-level experiment description language for the BrainScaleS-2 platform and demonstrate its use.

翻译：脉冲神经网络可塑性研究是一个活跃的研究领域。然而，涉及复杂可塑性规则、密集连接/高突触数量、复杂神经元形态或长时间模拟的仿真计算量可能非常巨大。BrainScaleS-2神经形态架构旨在通过支持"混合"可塑性来应对这一挑战，该架构结合了可编程性和固有并行仿真的概念。具体而言，在数值仿真中计算成本高昂的观测量（例如每个突触的相关性测量）直接在突触电路中实现。这些观测量的评估、是否执行更新的决策以及更新幅度，均由一个与模拟神经网络同时运行的传统程序执行。因此，这些系统在此类情况下可以提供可扩展且灵活的解决方案。尽管该平台先前的工作已经报道了不同类型可塑性的应用，但脉冲神经网络实验拓扑与协议的描述和可塑性算法尚未实现关联。在本工作中，我们为BrainScaleS-2平台引入了一个集成框架，使用统一的高级实验描述语言来描述脉冲神经网络实验和可塑性规则，并演示了其使用方法。