Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train spiking networks with various ratios of excitatory to inhibitory neurons on AI-relevant datasets. The results indicate networks with the biologically realistic 80:20 excitatory:inhibitory balance can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.

中文翻译：

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用于鲁棒尖峰神经网络训练的生物信息兴奋性和抑制性平衡

尖峰神经网络从大脑的生物限制中汲取灵感，有望为人工智能提供一种节能范例。然而，在确定以稳健的方式训练这些网络的指导原则方面存在挑战。此外，当结合兴奋性和抑制性连接的生物限制时，训练变得更加困难。在这项工作中，我们确定了几个关键因素，例如低初始放电率和多样化的抑制尖峰模式，这些因素决定了在人工智能相关数据集上训练具有不同兴奋性与抑制性神经元比例的尖峰网络的整体能力。结果表明，具有生物学上真实的 80:20 兴奋性：抑制性平衡的网络可以在低活动水平和嘈杂的环境中可靠地进行训练。此外，范罗森距离（尖峰序列同步性的度量）可以深入了解抑制神经元对于提高网络对噪声的鲁棒性的重要性。这项工作支持进一步的生物信息大规模网络和节能硬件的实现。