Mimicking Neuroplasticity in a Hybrid Biopolymer Transistor by Dual Modes Modulation

Lv, Ziyu; Chen, Meng; Qian, Fangsheng; Roy, Vellaisamy A. L.; Fraser, Michael; She, Donghong; Wang, Yan; Xu, Zong‐Xiang; Zhou, Ye; Han, Su‐Ting

doi:10.1002/adfm.201902374

Cited by 174 publications

(145 citation statements)

References 54 publications

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“…By employing semiconductors of varying band-gap and optical absorption, this concept can utilise wavelength division multiplexing schemes, enabling selective probing of artificial neural networks. In comparison to previous works that demonstrate optical programming and electrical erase [50][51][52] , a V gs that keeps the trap states empty, a V ds that ensures the leakage currents do not interfere with the weight readouts, a low initial background carrier concentration to prevent screening effects and a light intensity sufficient to fill the traps ensures the better linear update of weights in our devices (Supplementary Note 7, Supplementary Figs. 12-16 and Fig.…”

Section: Discussionmentioning

confidence: 99%

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

et al. 2020

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Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise-not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-electronic transfer of weights with a two-shot write scheme, while electrical in-memory inference provides energy efficiency. This method enables implementing a memristive deep recurrent neural network with twelve trainable layers with more than a million parameters to recognize spoken commands with >90% accuracy.

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Section: Discussionmentioning

confidence: 99%

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

et al. 2020

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“…The combination of synaptic and sensing capabilities in a single device has the advantage of high compactness without the need for additional sensing elements. [195] Artificial synapses developed so far can detect light, [134,135,158,[196][197][198] pH, [142] and chemicals ( Table 3). [199,200] Light-sensitive artificial synapses have mostly used flexible 2-T devices.…”

Section: Sensory Synaptic Devicesmentioning

confidence: 99%

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

et al. 2019

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Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next‐generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high‐density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

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“…The synaptic weight can be controlled by both low‐consumption optical pulses and electric pulses. Lv et al exploited carbon dots combined with silk protein to build an optical synaptic transistor, which can be regulated by light not only in volatile memory mode but also in nonvolatile memory mode first . Carbon dots/silk protein is appointed as the charge trapping medium.…”

Section: Emerging Materials‐based Synaptic Devicesmentioning

confidence: 99%

“…The low‐energy‐consumption device can reach 82.7% recognition accuracy after 2000 trainings, which paves a significant way for promoting the learning and recognition capability in visualization systems and neuro‐inspired computing. Moreover, Lv et al made use of MNIST pattern to investigate learning and recognition ability in the single‐layer perceptron network consisting of 785 input neurons and 10 output neurons made of carbon dots/silk‐based synapses (Figure c) . After 15 000 trainings, the recognitions for the letters “A” and “I” reach about 73% and 65%, respectively.…”

Section: Innovative Applications Of Photonic Synapses For Neuromorphimentioning

confidence: 99%

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Recent Progress in Photonic Synapses for Neuromorphic Systems

Zhang

Dai

Zhao

et al. 2020

Advanced Intelligent Systems

165

118

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Implementing synaptic functions with electronic devices is critical to achieve neuromorphic systems on the hardware platform, as synapses play important roles in brain computing and memory. Synapses modulated by light signals, which are also referred as photonic synapses, can not only make effective use of the outstanding properties of light to provide devices with ultrahigh propagation speed, high bandwidth and low crosstalk but also provide a noncontact writing method, which can facilitate the evolution of optical wireless communication and operation. More importantly, real‐time image processing can also be performed by photonic synapses which possess temporary memory. Thus far, tremendous efforts have been taken to design and fabricate photonic synapses. Herein, a summary of the development of different kinds of emerging materials utilized in photonic synaptic devices including memristors, field‐effect transistors, and phase change memory is presented, followed by the innovative applications of photonic synapses for neuromorphic systems. Finally, some current challenges and future study directions are discussed.

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Mimicking Neuroplasticity in a Hybrid Biopolymer Transistor by Dual Modes Modulation

Cited by 174 publications

References 54 publications

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Recent Progress in Photonic Synapses for Neuromorphic Systems

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