基于记忆装置的人工感觉系统

Ju Young Kwon, Ji Eun Kim, Jong Sung Kim, Suk Yeop Chun, Keunho Soh, Jung Ho Yoon
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引用次数: 0

摘要

在生物神经系统中,受体、神经元和突触并行系统的整合与合作,使其能够高效地检测和处理错综复杂、杂乱无章的外部信息。这些系统能实时获取和处理环境数据,以最小的能耗高效地处理复杂的任务。Memristors 可模拟典型的生物受体、神经元和突触,实现神经元信号处理功能的关键特征,如受体的选择性适应、神经元的漏整合-发射和突触的突触可塑性。外部刺激由 "人造感受器 "敏感地检测和过滤,通过 "人造神经元 "编码为尖峰信号,并通过 "人造突触 "整合和存储。忆阻器具有运行速度快、功耗低、可扩展性强等特点,因此将其与高性能传感器集成在一起,是一种很有前景的创建集成人工感觉系统的方法。这些集成系统可以从大量原始数据中提取有用数据,促进环境信息的实时检测和处理。本综述探讨了基于忆阻器的人工感觉系统的最新进展。作者首先介绍了人工感觉元件的要求,然后深入评述了忆阻器件所展示的人工感觉元件。最后,还讨论了开发基于忆阻器的人工感觉系统所面临的主要挑战和机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Artificial sensory system based on memristive devices

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by “artificial receptors,” encoded into spike signals via “artificial neurons,” and integrated and stored through “artificial synapses.” The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.

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