Efficient Balanced Networks (EBNs) are networks of spiking neurons in which excitatory and inhibitory synaptic currents are balanced on a short timescale, leading to desirable coding properties such as high encoding precision, low firing rates, and distributed information representation. It is for these benefits that it would be desirable to implement such networks in low-power neuromorphic processors. However, the degree of device mismatch in analog mixed-signal neuromorphic circuits renders the use of pre-trained EBNs challenging, if not impossible. To overcome this issue, we developed a novel local learning rule suitable for on-chip implementation that drives a randomly connected network of spiking neurons into a tightly balanced regime. Here we present the integrated circuits that implement this rule and demonstrate their expected behaviour in low-level circuit simulations. Our proposed method paves the way towards a system-level implementation of tightly balanced networks on analog mixed-signal neuromorphic hardware. Thanks to their coding properties and sparse activity, neuromorphic electronic EBNs will be ideally suited for extreme-edge computing applications that require low-latency, ultra-low power consumption and which cannot rely on cloud computing for data processing.

翻译：高效平衡网络(EBNs)是神经神经元的网络,在这种网络中,刺激性和抑制性神经元合成电流在短时间范围内得到平衡,从而产生高编码精度、低发率和分布式信息代表等可取的编码特性。为了这些好处,最好在低功率神经畸形处理器中实施这种网络。但是,模拟混合信号神经形态变异电路的装置不匹配程度使得使用预先训练的EBN系统具有挑战性,即使不是不可能。为了克服这一问题,我们制定了适合在芯片上实施的新的地方学习规则,将随机连接的神经元弹射线网络推进到一个严格平衡的系统中。我们在这里介绍了执行这一规则的集成电路,并在低电路模拟中展示其预期行为。我们所提议的方法为系统一级实施对模拟混合信号神经形态硬件的严格平衡网络铺平铺平了道路。由于它们的编码特性和稀疏活动,神经形态电子内导电子导导母体将非常适合用于极近的计算机应用,而不能依赖低热能处理。