Bio-plausible memristive neural components towards hardware implementation of brain-like intelligence

Sung, Sang Hyun; Jeong, Yu‐Jin; Oh, Jung Won; Shin, Hyeon‐Jin; Lee, Jae Hee; Lee, Keon Jae

doi:10.1016/j.mattod.2022.11.022

Cited by 16 publications

(9 citation statements)

References 248 publications

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“…Breakthroughs in powerful hardware and algorithms would bring a revolution of brain-like chips. 86,87 For brain-like chips, some automatic design toold, simulator platforms, and integration technology should be developed to support the system-level design of memristor-based computing chips. 88−94 The hardware−software codesign flow and platforms from device to algorithm is a foundation to design efficient computing chips.…”

Section: Discussionmentioning

confidence: 99%

“…Breakthroughs in powerful hardware and algorithms would bring a revolution of brain-like chips. , For brain-like chips, some automatic design toold, simulator platforms, and integration technology should be developed to support the system-level design of memristor-based computing chips. − The hardware–software codesign flow and platforms from device to algorithm is a foundation to design efficient computing chips. For the nanodevices and nanotechnology, the memristor-based nanodevice primitives should be optimized to meet the AI application requirements of computing chips, and developing integration technology is beneficial to the design of future large-scale computing chips.…”

Section: Discussionmentioning

confidence: 99%

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Memristor-Based Artificial Chips

Sun,

Chen,

Zhou

et al. 2023

ACS Nano

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Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Memristor-Based Artificial Chips

Sun,

Chen,

Zhou

et al. 2023

ACS Nano

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“…In the evolution of artificial neuron devices, the progress and performance of key aspects have been compared to biological systems (Figure c). ,,,,,,,,,,,,− The response speed of biological receptors varies depending on the sensory modality. For instance, in the visual system, the photoreceptor cells (rods and cones) exhibit a relatively slow response time of approximately 100–200 ms to stimuli.…”

Section: Outlook For Artificial Neuron Devicesmentioning

confidence: 99%

“…These neurons communicate with each other through specialized structures called synapses, which allow for the transmission of electrical and chemical signals. The plasticity of these synapses, meaning their ability to change their strength and connectivity over time, is crucial for learning and memory. − Neuromorphic electronic systems, proposed by Carver Mead in the late 1980s to early 1990s, aim to design electronic systems that mimic the structure, function, and plasticity of biological neural networks (Figure ). While neuromorphic circuits based on silicon complementary metal-oxide-semiconductor (CMOS) technology have been developed to replicate synaptic functionalities, classical computing systems traditionally relied on the von Neumann computing architecture and suffered from limitations due to the separation of memory from processing, leading to issues such as speed latency, high energy consumption, and limited communication bandwidth. ,,, To overcome these drawbacks, researchers have developed novel artificial synapses based on a variety of materials and structures, typically implemented with 2-terminal memristors or 3-terminal transistors. ,,, These devices are capable of achieving neuromorphic functions, such as short-term and long-term plasticity (STP and LTP), similar to the synapses in biological systems. ,, In recent years, there has been growing interest in using flexible electronics for the development of artificial neuron devices. ,, Flexible electronics refer to electronic devices and systems that can bend, stretch, and conform to their surroundings without breaking or losing their functionality. ,− Flexible electronics possess mechanical properties similar to human organs and tissues, showing great advantages for the development of artificial neuron devices. ,,− They can be easily integrated with biological tissues and structures, allowing for seamless interaction with the nervous system and the development of biointerfaces and biohybrid systems. − …”

Section: Introductionmentioning

confidence: 99%

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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“…AI design and development emerge from a reciprocal interdependence and interaction between the natural world and human culture. AI is heavily influenced by our understanding of the way the human brain works and the principles of natural intelligence: many AI algorithms are designed to mimic the structure and function of neural networks in the brain (Shanmuganathan, 2016;Sung et al, 2023;Strukov et al, 2019;Alaloul and Qureshi, 2020;Sung et al, 2023). On the other hand, AI is also shaped by the values, goals, and aspirations of human culture.…”

Section: Preprint -Please Cite the Originalmentioning

confidence: 99%

Augmented Cognition: Life as we don't know it

Hipólito¹

2023

Preprint

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This paper proposes a framework for comprehending the integration of Artificial Intelligence (AI) as Augmented Cognition (AugCog). AugCog is viewed as an emergent bio-cultural process, reflecting AI's design, implementation, and usage. Section 1 establishes smart societies as a complex system. Section 2 defends that the development of AI is analogous to the biological process of niche construction. Section 3 defines AI as a socioculturally embodied expansion by which AI is shaped by and shapes human experience, resulting in various forms of AugCog. Section 4 highlights AugCog in mixed realities such as social media, neurotechnology, and smart environments, illustrating its emergence from multiscale and interdependent sociocultural perspectives. AugCog's perspective situates the human species in the state space of evolution, signifying our existence as a species and as embodied individuals.

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Bio-plausible memristive neural components towards hardware implementation of brain-like intelligence

Cited by 16 publications

References 248 publications

Memristor-Based Artificial Chips

Memristor-Based Artificial Chips

Artificial Neuron Devices

Augmented Cognition: Life as we don't know it

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