scientific report|磁性隧道结模拟的随机皮质脉冲神经元

受大脑启发的计算架构目标是模仿大脑神经元与突触的计算机制，以实现人脑的高学习效率与复杂任务处理能力。普渡大学的Abhronil Sengupta提出了热噪声存在时，可以用磁性隧道结的随机翻转行为模拟皮质神经元的概率脉冲特性，他们的结果说明了在这种基于磁性隧道结的人工神经网络系统中存在的侧抑制以及内部稳态，这种基于磁性隧道结的神经网络也实现了一种Belife网络中概率计算的直接映射。

Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic spiking nature of pyramidal neurons in the cortex to the stochastic switching behavior of a Magnetic Tunnel Junction in presence of thermal noise. We present results to illustrate the efficiency of neuromorphic systems based on such probabilistic neurons for pattern recognition tasks in presence of lateral inhibition and homeostasis. Such stochastic MTJ neurons can also potentially provide a direct mapping to the probabilistic computing elements in Belief Networks for performing regenerative tasks.