In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depend on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency.

在计算神经科学以及机器学习中，神经形态设备有望成为神经网络仿真的加速且可扩展的替代方案。它们的神经连通性和突触能力取决于其特定的设计选择，但始终受到内在的限制。在这里，我们提出一种策略，通过不断地重新布线突触前和突触后伙伴，同时保持神经元扇入恒定和连接组稀疏，来在这些约束条件下优化资源分配。特别是，我们在模拟神经形态系统BrainScaleS-2上实现了该算法。它在芯片上的定制嵌入式数字处理器上执行，伴随着尖峰神经元和突触电路的混合信号基质。我们在一个简单的有监督的学习场景中评估了我们的实施情况，