Memristor-based Hopfield network circuit for recognition and sequencing application

Sun, Junwei; Xiao, Xiao; Yang, Qinfei; Liu, Peng; Wang, Yanfeng

doi:10.1016/j.aeue.2021.153698

Cited by 31 publications

(18 citation statements)

References 44 publications

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“…Hence, the neural network can recognize and sequence four characters simultaneously. [227] In image processing, Sheridan et al implemented a sparse-coding system constructed by a memristor crossbar. Based on pattern matching and lateral neuron inhibition, they demonstrated image construction successfully, and the performance can be further improved with image pre-processing techniques (Figure 20a).…”

Section: Brain-inspired Computing Systemsmentioning

confidence: 99%

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Memristor‐Based Intelligent Human‐Like Neural Computing

Wang

Song

Chen

et al. 2022

Adv Elect Materials

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In the field of a relatively dangerous working environment, NASA developed Valkyrie, a 44-degree-of-freedom, series elastic actuator-based robot. In addition, Valkyrie is designed to respond to disasters like nuclear disasters and advance human spaceflight in extraterrestrial planetary settings. [6] By implementing safety features and allowing remote intervention, Atkeson et al. enabled an Atlas humanoid robot to meet the standard in performing disaster response-related tasks involving physical contact with the environment. [7] Currently, to allow humanoid robots to feel and process the environmental information as human beings for perfectly implementing tasks, perceptual comprehension and computation efficiency are two key indexes. Nevertheless, it is challenging to achieve them. The former requires the installation of massive, diverse sensors in humanoids, which will inevitably slow down the processing speed under the current sensing-storage-process separated framework, i.e., von Neumann architecture. In other words, the current computer architecture builds up a barrier between sensing performance and computation efficiency. In this context, neuromorphic devices which can emulate the perceptual and computation functions of the biological nervous system have illustrated their potential to break the von Neumann barrier, attracting researchers' interests (Figure 1). Humanoid robots, intelligent machines resembling the human body in shape and functions, cannot only replace humans to complete services and dangerous tasks but also deepen the own understanding of the human body in the mimicking process. Nowadays, attaching a large number of sensors to obtain more sensory information and efficient computation is the development trend for humanoid robots. Nevertheless, due to the constraints of von Neumann-based structures, humanoid robots are facing multiple challenges, including tremendous energy consumption, latency bottlenecks, and the lack of bionic properties. Memristors, featured with high similarity to the biological elements, play an important role in mimicking the biological nervous system. The memristor-based nervous system allows humanoid robots to obtain high energy efficiency and bionic sensing properties, which are similar properties to the biological nervous system. Herein, this article first reviews the biological nervous system and memristor-based nervous system thoroughly, including the structures and also the functions. The applications of memristor-based nervous systems are introduced, the difficulties that need to be overcome are put forward, and future development prospects are also discussed. This review can hopefully provide an evolutionary perspective on humanoid robots and memristor-based nervous systems.

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Section: Brain-inspired Computing Systemsmentioning

confidence: 99%

“…Hence, the neural network can recognize and sequence four characters simultaneously. [ 227 ] In image processing, Sheridan et al. implemented a sparse‐coding system constructed by a memristor crossbar.…”

Section: Applicationmentioning

confidence: 99%

Memristor‐Based Intelligent Human‐Like Neural Computing

Wang

Song

Chen

et al. 2022

Adv Elect Materials

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“…By using this scheme, the Hopfield network can be trained directly by physical inputs with different external patterns as they occur, i.e., the network is trained online. This approach enables us to autonomously program new memristance values and thereby assemble and update the connectivity matrix based on physical events, unlike previously implemented physical Hopfield networks [48][49][50] in which memristance values in the weight matrix were iteratively adjusted offline using applied voltage pulses to achieve a desired connectivity matrix.…”

Section: Spatiotemporal Memorymentioning

confidence: 99%

Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning

Riley

Koner

Osorio-Pinzon

et al. 2022

Advanced Intelligent Systems

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Physical systems exhibiting neuromechanical functions promise to enable structures with directly encoded autonomy and intelligence. A neuromorphic metamaterials class embodying bioinspired mechanosensing, memory, and learning functionalities obtained by leveraging mechanical instabilities integrated with memristive materials is reported. The prototype system comprises a multistable metamaterial whose bistable dome‐shaped units collectively filter, amplify, and transduce external mechanical inputs over large areas into simple electrical signals using embedded piezoresistive sensors. Dome deformations in nonvolatile memristors triggered by the transduced signals, providing a means to store loading events in measurable material states are recorded. Sequentially applied mechanical inputs result in accumulated memristance changes that allow us to physically encode a Hopfield network into the neuromorphic metamaterials. This physical network learns the history of spatially distributed input patterns. Crucially, the neuromorphic metamaterials can retrieve the learned patterns from the memristors’ final accumulated state. Therefore, the system exhibits the ability to learn without supervised training and retain spatially distributed inputs with minimal external overhead. The system's embodied mechanosensing, memory, and learning capabilities establish an avenue for synthetic neuromorphic metamaterials that learn via tactile interactions. This capability suggests new types of large‐area smart surfaces for robotics, autonomous systems, wearables, and morphing structures subjected to spatiotemporal mechanical loading.

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“…Also, synapse can be seen as a two-port memristive device to connect two systems so as to realize the complex memory transmission characteristic [8]. Thus, memristor-based neurons or neural networks are now playing a vital effect in neuromorphic computation and brain-like applications [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Memristive electromagnetic induction effects on Hopfield neural network

et al. 2021

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Due to the existence of membrane potential differences, the electromagnetic induction flows can be induced in the interconnected neurons of Hopfield neural network (HNN). To express the induction flows, this paper presents a unified memristive HNN model using hyperbolic-type memristors to link neurons. By employing theoretical analysis along with multiple numerical methods, we explore the electromagnetic induction effects on the memristive HNN with three neurons. Three cases are classified and discussed. When using one memristor to link two neurons bidirectionally, the coexisting bifurcation behaviors are disclosed with respect to the memristor coupling strength. When using two memristors to link three neurons, the antimonotonicity phenomena of periodic and chaotic bubbles are yielded, and initial-related multistable patterns are emerged. When using three memristors to link three neurons end to end, the extreme event owning riddled basin of attraction is demonstrated. In addition, we develop the printed circuit board (PCB)-based hardware experiments by synthesizing the memristive HNN and the experimental results well confirm the memristive electromagnetic induction effects.

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Memristor-based Hopfield network circuit for recognition and sequencing application

Cited by 31 publications

References 44 publications

Memristor‐Based Intelligent Human‐Like Neural Computing

Memristor‐Based Intelligent Human‐Like Neural Computing

Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning

Memristive electromagnetic induction effects on Hopfield neural network

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