论文标题
硅中ADEX神经元动力学的精确而灵活的类似模仿
An accurate and flexible analog emulation of AdEx neuron dynamics in silicon
论文作者
论文摘要
模拟神经形态硬件一方面有望快速脑仿真，并有效地实现了新型的，脑启发的计算范式。桥接此频谱需要灵活的可配置电路，并通过准确实现目标神经元和突触模型来促进可靠且可重复的动力学。该手稿介绍了混合信号加速神经形态系统brainscales-2的模拟神经元电路。它们能够灵活，准确地模拟自适应指数泄漏的集成和射击模型方程，并结合基于电流和基于电导的突触，这是通过精确复制广泛的复杂神经元动力学和发射模式来证明的。
Analog neuromorphic hardware promises fast brain emulation on the one hand and an efficient implementation of novel, brain-inspired computing paradigms on the other. Bridging this spectrum requires flexibly configurable circuits with reliable and reproducible dynamics fostered by an accurate implementation of the targeted neuron and synapse models. This manuscript presents the analog neuron circuits of the mixed-signal accelerated neuromorphic system BrainScaleS-2. They are capable of flexibly and accurately emulating the adaptive exponential leaky integrate-and-fire model equations in combination with both current- and conductance-based synapses, as demonstrated by precisely replicating a wide range of complex neuronal dynamics and firing patterns.
