Energy scavenging artificial nervous system for detecting rotational movement

Choi, Daehwan; Song, Moon Hee; Sung, Taehoon; Jang, Sukjin; Kwon, Jang Yeon

doi:10.1016/j.nanoen.2020.104912

Cited by 32 publications

(27 citation statements)

References 45 publications

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“…The stronger the stimulus applied, the stronger the response behaved. 49,53 In contrast, under the same pulses test for the TFTs without K + doping, the EPSCs quickly returned to the initial states after the removal of the pulses without an obvious rising trend (Fig. S6, ESI †).…”

Section: Resultsmentioning

confidence: 96%

“…48 Due to the memory effect, if two presynaptic peaks are close enough, the EPSC peak resulting from the first presynaptic peak cannot be eliminated entirely, and the peak generated by the second presynaptic peak will be enhanced accordingly. 48,49 A pair of presynaptic spikes (4 V, 30 ms) with a pulse interval (Dt) of 20 ms was continuously applied to the gate, as shown in Fig. 2b.…”

Section: Resultsmentioning

confidence: 99%

“…3c, with the increase of the number of learning pulses, the drop of EPSC as a function of time gradually slows down, indicating the transition from STM to LTM. 3,49 In addition, due to the presence of electrochemical doping, the charge can be preserved for a long time. Furthermore, the learning process based on positive pulses is reversible, which means that the negative pulse input could accelerate the EPSC decrease process.…”

Section: Resultsmentioning

confidence: 99%

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Advanced artificial synaptic thin-film transistor based on doped potassium ions for neuromorphic computing via third-generation neural network

et al. 2022

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Advanced artificial synaptic thin-film transistor based on doped potassium ions for neuromorphic computing via third-generation neural network

et al. 2022

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“…[1][2][3][4][5][6] Somatosensory nerves transfer signals through synaptic connections, to achieve a variety of perceptions, memories and motion outputs with different depths. [7][8][9][10] Therefore, the imitation of synapses as building blocks for neural processing are of crucial importance for constructing an e cient arti cial neuromorphic system. [11][12][13] For emulating synaptic behaviors, a variety of structures such as metal/insulator/metal (MIM)-stack switches, [14][15][16][17][18] electrolyte/semiconductor hetero-junctions, 19,20 and multi-terminal transistors [21][22][23][24][25] have been employed to realize the signal transmission.…”

Section: Introductionmentioning

confidence: 99%

Graphdiyne-Based Ultra-Responsive Artificial Synapse with Multi-Ion Diffusive Dynamics for Mimicking Efferent Nerves

Wei¹,

Shi²,

Sun³

et al. 2020

Preprint

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Somatosensory nerves require synapses to respond efficiently and in parallel for receving and transmiting biological signals. The gap between biological systems and conventional electronics needs ionotronics to bridge. However, the exploration of new materials and the systematic construction of ionotronics still pose challenges. Graphdiyne, a highly π-extended two-dimensional (2D) carbon allotrope, has demonstrated potential applications in ionic peripheral systems for its inherent network holes that can be used for rapid and selective transmission of diverse ions. Here, a graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic the biological signal transmission behaviors. An record-breaking impulse responsiveness (±5 mV) that is an order of magnitude exceeding biological level has been realized for ultra-sensitive and power-efficient brain-inspired applications, with the lowest femtowatt-level consumption (~16.7 fW). Most importantly, GAS is capable of parallelly processing signals transmitted from multiple preneurons and therefore realizing dynamic logics and spatiotemporal rules. In a proof-of-concept demonstration, our artificial efferent nerve, connecting GAS with artificial muscles, completes the information integration of preneurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks (e.g., visual and tactile) and reacting to these events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics and neurorobotics.

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“…omplex human nervous systems, which have the advantages of being highly compact, parallel, and reliable, are gaining increasing attention in various fields, such as neuromorphic computing, bioinspired sensorimotor systems, brain-machine interfaces, and prosthetics [1][2][3][4][5][6][7][8][9][10] . Somatosensory nerves transfer signals through synaptic connections to achieve a variety of perceptions, memories, and motion outputs with different depths [11][12][13][14] . Therefore, the imitation of synapses as building blocks for neural processing is of crucial importance for constructing efficient artificial sensorimotor systems, biohybrid systems, and neuromorphic chips [15][16][17][18][19][20][21][22][23][24] .…”

mentioning

confidence: 99%

Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

et al. 2021

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A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain–computer interfaces.

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Energy scavenging artificial nervous system for detecting rotational movement

Cited by 32 publications

References 45 publications

Advanced artificial synaptic thin-film transistor based on doped potassium ions for neuromorphic computing via third-generation neural network

Advanced artificial synaptic thin-film transistor based on doped potassium ions for neuromorphic computing via third-generation neural network

Graphdiyne-Based Ultra-Responsive Artificial Synapse with Multi-Ion Diffusive Dynamics for Mimicking Efferent Nerves

Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

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