Memristor‐based reservoir computing systems represent an attractive approach in processing the time‐series information with a low training cost, in a range of fields from finance to engineering. Previous investigations have identified the charming potential of organic devices for next‐generation memory devices. However, the structural inhomogeneity and wide energy bandgap of most organic polymers usually lead to low‐yield and high operation power microelectronic devices, that permit their further application in neuromorphic computing. Herein, an organic‐inorganic hybrid memristor that can be conveniently processed into crossbar devices with tolerable yield via spin‐coating is shown. The doped inorganic polyoxometalate (POM) clusters via supramolecular assembly strategy not only act as the charge trapping modules but also assist the formation of conductive filaments due to their delocalized electrostatic adsorption property. With the dynamic short‐term memory property, the designed memristor devices can be used as a reservoir framework to process temporal information directly. A smaller reservoir with 100 memristors can be used for the recognition of emotion patterns efficiently. This strategy demonstrates the unique role of POM in developing low‐power and repeated memristors, which provides a new material platform to design advanced function memristors for neuromorphic computing.

Neumann system is a conventional computing architecture with divided processor and memory unit which executes computational tasks sequentially which has served as a pillar of contemporary computing since 1945. However, the frequent data shuffling between the separated processor and memory unit induce massive power consumption and latency which is so-called von Neumann bottleneck. [7,8] The human brain is capable of concurrently executing several complicated tasks with enormous parallelism with extremely low power consumption, outstanding fault tolerance, and remarkable durability owing to its extensive connection, functional organizational hierarchy, advanced learning rules, and neuronal plasticity. Inspiring from it, Mead first pioneered the notion of "neuromorphic computing" in the late 1980s and early 1990s. [9,10] Accordingly, to eliminate inherent constraint of the von Neumann systems, substantial effort has been focused on investigating neuromorphic computing systems. [11][12][13] Biological neuromorphic systems consisting 10 11 neurons interconnected with each other via 10 15 synapses [14,15] is capable to respond to environment and history at different levels from simple molecular (nucleic acids could displays adaptive behaviors including self-repair and replication, under stimuli from the local environment), the elementary information-processing blocks in biological systems (neurons could exhibit more than 20 different dynamic behaviors triggered by historically and environmentally electrochemical stimulation), to whole functional systems with more hierarchical complexity (extremely low or high relative humidity (RH), has a substantial impact on the accuracy of human visual system). [16][17][18] Recently, inspired by human sensory processing and perceptual learning, neuromorphic sensing and computing systems incorporating sensors and machine learning algorithms have been demonstrated to perceive, process, and integrate diverse sensory information where the adaptation and learning can be obtained through dynamically updating the weights of neural network according to the different training algorithms. [19,20] However, on contrary to the distributed processing in biological hierarchical architectures which is more adaptable and cognitive for the optimum analysis of complicated information, the modern computing systems adopting centralized processing are still based on static elements with zeroth-order complexity (e.g., transistor). The essential step for developing neuromorphic systems is to The essential step for developing neuromorphic systems is to construct more biorealistic elementary devices with rich spatiotemporal dynamics to exhibit highly separable responses in dynamic environmental circumstances. Unlike transistor-based devices and circuits with zeroth-order complexity, memristors intrinsically express some simple biomimetic functions. However, with only two-terminal structure, precise control of operation principles to ensure large dynamic space, improved linearity and symmetry, multimodal oper...

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain‐inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high‐dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor‐implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor‐implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on‐demand application scenarios. Furthermore, recent advances in the application of memristor‐based physical RC systems are surveyed. In the end, the further prospects of memristor‐implemented RC system in a material view are envisaged.

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal‐oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insight into the function of every part of the visual pathway for visual perception could bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits which are able to mimic the function of each part of visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from retina to the primate visual cortex within reach (chapter 2). Based on the extraction of biological principles, the recent hardware implemented visual neurons located in different parts of the visual pathway are detailed discussed (chapter 3 and chapter 4). Furthermore, we attempt to provide valuable applications of inspired artificial vision in different scenarios (chapter 5). The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next‐generation artificial visual perception systems.This article is protected by copyright. All rights reserved

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