生物启发神经形态感觉系统

Tong Wang, Xiao-Xue Wang, Juan Wen, Zhenya Shao, He-Ming Huang, Xin Guo
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引用次数: 11

摘要

智能社会的到来导致了信息的指数级增长,迫切需要一种时间和能源效率的方式来处理产生数据的信息。这个问题可以通过受生物感觉系统启发的计算神经形态范式来解决,生物感觉系统在外部刺激和生物体的实时反应之间建立了联系;在该范式中,神经形态系统与传感器相结合,形成人工感觉系统。本文展示了一种具有气体传感、数据存储和处理集成能力的神经形态感觉系统。漏失积分-火(LIF)神经元是系统的基本计算单元,由易失性记忆器件Pt/Ag/TaOx/Pt实现;感觉神经元,即与一系列气体传感器相连的LIF神经元,检测气体并将气体的化学信息转化为神经脉冲;基于非易失性记忆器件Pt/Ta/TaOx/Pt的突触根据突触权重将感觉神经元的信号传递给中继神经元,这些神经元通过监督的峰值速率依赖可塑性进行训练;然后,中继神经元处理来自突触的信号,并对气体进行分类。这项工作的方法也可以应用于通过与不同传感器的集成来模拟其他生物感知。
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A Bio‐Inspired Neuromorphic Sensory System
The advent of the intelligent society leads to the exponential growth of information, imposing urgent requirements in a time‐ and energy‐efficient way to process information where data are generated. This issue can be addressed by the neuromorphic paradigm of computing inspired by biological sensory systems that build up the association between external stimuli and the response of an organism in real‐time; in the paradigm, a neuromorphic system is integrated with sensors to form an artificial sensory system. Herein, a neuromorphic sensory system with integrated capabilities of gas sensing, data storage, and processing is demonstrated. Leaky integrate‐and‐fire (LIF) neurons, the basic computing units in the system, are realized with volatile memristive device Pt/Ag/TaOx/Pt; sensory neurons, i.e., the LIF neurons connected with an array of gas sensors, detect gases and convert the chemical information of gases into neural spikes; synapses based on nonvolatile memristive device Pt/Ta/TaOx/Pt transmit the signals from sensory neurons to relay neurons according to synaptic weights, which are trained by the supervised spike‐rate dependent plasticity; relay neurons then process the signals from the synapses and classify gases. The approach of this work can also be applied to emulate other biological perceptions through the integration with different sensors.
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