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.

中文翻译：

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使用基于混合信号尖峰的学习电路实现高效的平衡网络

高效平衡网络 (EBN) 是尖峰神经元网络，其中兴奋性和抑制性突触电流在短时间内平衡，从而产生理想的编码特性，例如高编码精度、低放电率和分布式信息表示。正是为了这些好处，在低功耗神经形态处理器中实现这样的网络是可取的。然而，模拟混合信号神经形态电路中设备不匹配的程度使得使用预先训练的 EBN 具有挑战性，如果不是不可能的话。为了克服这个问题，我们开发了一种适用于片上实现的新型局部学习规则，它将随机连接的尖峰神经元网络驱动到一个紧密平衡的状态。在这里，我们展示了实现此规则的集成电路，并在低级电路仿真中展示了它们的预期行为。我们提出的方法为在模拟混合信号神经形态硬件上实现紧密平衡网络的系统级实现铺平了道路。由于其编码特性和稀疏活动，神经形态电子 EBN 将非常适合需要低延迟、超低功耗且不能依赖云计算进行数据处理的极端边缘计算应用。