One of the main goals of neuromorphic computing is the implementation and design of systems capable of dynamic evolution with respect to their own experience. In biology, synaptic scaling is the homeostatic mechanism which controls the frequency of neural spikes within stable boundaries for improved learning activity. To introduce such control mechanism in a hardware spiking neural network (SNN), we present here a novel artificial neuron based on phase change memory (PCM) devices capable of internal regulation via homeostatic and plastic phenomena. We experimentally show that this mechanism increases the robustness of the system thus optimizing the multi-pattern learning under spike-timing-dependent plasticity (STDP). It also improves the continual learning capability of hybrid supervised-unsupervised convolutional neural networks (CNNs), in terms of both resilience and accuracy. Furthermore, the use of neurons capable of self-regulating their fire responsivity as a function of the PCM internal state enables the design of dynamic networks. In this scenario, we propose to use the PCM-based neurons to design bio-inspired recurrent networks for autonomous decision making in navigation tasks. The agent relies on neuronal spike-frequency adaptation (SFA) to explore the environment via penalties and rewards. Finally, we show that the conductance drift of the PCM devices, contrarily to the applications in neural network accelerators, can improve the overall energy efficiency of neuromorphic computing by implementing bio-plausible active forgetting.

中文翻译：

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基于相变存储器的受大脑启发的稳态神经元，用于高效的神经形态计算。

神经形态计算的主要目标之一是实现和设计能够根据自身经验动态进化的系统。在生物学中，突触缩放是一种稳态机制，它将神经尖峰的频率控制在稳定的范围内，以改善学习活动。为了在硬件尖峰神经网络（SNN）中引入这种控制机制，我们在这里提出了一种基于相变存储器（PCM）设备的新型人工神经元，能够通过稳态和塑性现象进行内部调节。我们通过实验证明，这种机制提高了系统的鲁棒性，从而优化了尖峰时序依赖性可塑性（STDP）下的多模式学习。它还在弹性和准确性方面提高了混合监督-无监督卷积神经网络（CNN）的持续学习能力。此外，使用能够根据 PCM 内部状态自我调节其火焰响应性的神经元可以设计动态网络。在这种情况下，我们建议使用基于 PCM 的神经元来设计仿生循环网络，以便在导航任务中进行自主决策。该智能体依靠神经元尖峰频率适应（SFA）通过惩罚和奖励来探索环境。最后，我们表明，与神经网络加速器中的应用相反，PCM 器件的电导漂移可以通过实现生物合理的主动遗忘来提高神经形态计算的整体能量效率。