Neurorobotic approaches to emulate human motor control with the integration of artificial synapse

Kim, Seonkwon; Kim, Seongchan; Ho, Dong Hae; Roe, Dong Gue; Choi, Young Jin; Kim, Min Je; Kim, Ui Jin; Le, Manh Linh; Kim, Ju‐Young; Kim, Se Hyun; Cho, Jeong Ho

doi:10.1126/sciadv.abo3326

Cited by 12 publications

(12 citation statements)

References 54 publications

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“…Recent studies have focused on integrating artificial synaptic devices and actuators or robots to replicate the function of an efferent nerve. Most of the research has centered on mimicking motor neurons with an excitatory synapse, with a few works exploring the function of inhibitory synapses. − …”

Section: Artificial Nervous Systemsmentioning

confidence: 99%

“…Artificial neuron devices can revolutionize the field of soft robotics by providing advanced control and intelligence to robotic devices. ,,,,, These devices and systems enable robots to exhibit autonomous and adaptive behavior, resembling the flexibility and responsiveness of living organisms. This opens possibilities for applications in areas such as human–robot collaboration, industrial automation, and healthcare.…”

Section: Application Scope For Artificial Neuron Devicementioning

confidence: 99%

“…By applying positive and negative pulses to the two synapses, the graphdiyne-based artificial synapse allows for the regulation of neural response, leading to potentially more precise and controlled nerve signal transmission. In addition, Kim et al developed an artificial reciprocal inhibition system that includes excitatory and inhibitory synapse circuits, voltage and current amplifiers, and a soft actuator capable of producing artificial muscle movement . The actuator is composed of a Ni–Ti shape memory alloy fiber.…”

Section: Artificial Nervous Systemsmentioning

confidence: 99%

“…In addition, Kim et al developed an artificial reciprocal inhibition system that includes excitatory and inhibitory synapse circuits, voltage and current amplifiers, and a soft actuator capable of producing artificial muscle movement. 525 The actuator is composed of a Ni−Ti shape memory alloy fiber. The output voltages of the excitatory and inhibitory synapse circuits were utilized to emulate reciprocal inhibition, allowing for relaxation on one side to accommodate contraction on the other side of the actuator.…”

Section: Artificial Efferent Nervesmentioning

confidence: 99%

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Artificial Neuron Devices

He,

Wang,

et al. 2023

Chem. Rev.

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Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue’s mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

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Section: Artificial Nervous Systemsmentioning

confidence: 99%

Section: Application Scope For Artificial Neuron Devicementioning

confidence: 99%

Section: Artificial Nervous Systemsmentioning

confidence: 99%

Section: Artificial Efferent Nervesmentioning

confidence: 99%

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Artificial Neuron Devices

He,

Wang,

et al. 2023

Chem. Rev.

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“…Neuromorphic devices that electronically simulate the functions of biological synapses in human neural networks have been reported (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39). These artificial synapses, characterized by their low power consumption, small volume, and optimized analog signal processing capabilities, have excellent potential to process overflowing biometric data.…”

Section: Introductionmentioning

confidence: 99%

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks

Choi

Kim

et al. 2023

Sci. Adv.

Self Cite

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Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.

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Weight‐Reconfigurable Neuromorphic Computing Systems for Analog Signal Integration

Choi,

Roe,

et al. 2024

Adv Funct Materials

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Owing to the necessity of high computation amounts has emerged, interest in a neuromorphic computing system has significantly increased as a compelling alternative to conventional CMOS technology. This paper presents a neuromorphic hardware algorithm to finely reconfigure multi‐input signal processing, which can be implemented as an advanced processor for diverse external information, and the hydrogen explosion risk assessment system is demonstrated as a proof of concept. Hydrogen concentration and temperature are used as sensory inputs for the signal integration and the precise values of them are determined by offsetting the effect of temperature on the electrical signal from the hydrogen sensor through a sensor circuit. Each signal is then updated by the weight control circuit and converted into a postsynaptic current to represent the hydrogen explosion risk using a multi‐input artificial synapse. This simplicity of the circuitry renders the fabrication of all components and circuits compatible with simple inkjet printing methods, enabling cost‐effective and high‐throughput manufacturing. Additionally, the real‐time demonstration of the neuromorphic computing system is successfully conducted, offering insights into the practical application of neuromorphic computing.

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Neurorobotic approaches to emulate human motor control with the integration of artificial synapse

Cited by 12 publications

References 54 publications

Artificial Neuron Devices

Artificial Neuron Devices

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks

Weight‐Reconfigurable Neuromorphic Computing Systems for Analog Signal Integration

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