Neurons are the primary building block of the nervous system. Exploring the mysteries of the brain in science or building a novel brain-inspired hardware substrate in engineering are inseparable from constructing an efficient biological neuron. Balancing the functional capability and the implementation cost of a neuron is a grand challenge in neuromorphic field. In this paper, we present a low-cost adaptive exponential integrate-and-fire neuron, called SC-AdEx, for large-scale neuromorphic systems using stochastic computing. In the proposed model, arithmetic operations are performed on stochastic bit-streams with small and low-power circuitry. To evaluate the proposed neuron, we perform biological behavior analysis, including various firing patterns. Furthermore, the model is synthesized and implemented physically on FPGA as a proof of concept. Experimental results show that our model can precisely reproduce wide range biological behaviors as the original model, with higher computational performance and lower hardware cost against state-of-the-art AdEx hardware neurons.

中文翻译：

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使用随机计算的低成本自适应指数积分和发射神经元。

神经元是神经系统的主要组成部分。在科学上探索大脑的奥秘，或者在工程上构建新型的仿脑硬件基板，都离不开构建高效的生物神经元。平衡神经元的功能能力和实现成本是神经形态领域的一大挑战。在本文中，我们为使用随机计算的大规模神经形态系统提出了一种低成本的自适应指数积分和激发神经元，称为 SC-AdEx。在所提出的模型中，算术运算是在具有小型和低功耗电路的随机比特流上执行的。为了评估提议的神经元，我们进行了生物行为分析，包括各种放电模式。此外，该模型在 FPGA 上进行了综合和物理实现，作为概念证明。