Near infrared neuromorphic computing via upconversion-mediated optogenetics

Zhai, Yongbiao; Zhou, Ye; Yang, Xueqing; Wang, Feng; Fraser, Michael; Zhu, Xiaojian; She, Donghong; Lü, Wei; Han, Su‐Ting

doi:10.1016/j.nanoen.2019.104262

Cited by 61 publications

(49 citation statements)

References 46 publications

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“…Long‐term synaptic plasticity, which consists of long‐term potentiation (LTP) and long‐term depression (LTD) of synaptic weights, is an essential component to control memory in hippocampal neurons. [ 48 ] Unlike recently reported artificial synaptic devices, where LTP and LTD characteristics are generated by a compulsory combination of optical and electrical pulses, [ 10,49–53 ] long‐term synaptic plasticity in our BP devices is imitated merely by optical pulses, as shown in Figure . It should be noted that our BP devices are optoelectronic in nature where optical illumination is converted into the corresponding conductance of the device.…”

Section: Figurementioning

confidence: 99%

“…First, we classify 28 × 28 pixel images of handwritten digits adopted from a Modified National Institute of Standards and Technology (MNIST) dataset by designing a single‐layer perceptron model, performing supervised learning with back‐propagation algorithm (see Note S7, Supporting Information). [ 50,54,55 ] As schematically illustrated in Figure a, the network consists of 784 input neurons ( X 0 to X 783 ) corresponding to 28 × 28 pixels of the input image and 10 output neurons ( Y 0 to Y 9 ) with connections through 784 × 10 synaptic weights. During simulation, each neuron in the input layer receives a value corresponding to a pixel in the image and is assigned to an input vector ( X m ) which is then transformed to 10 output values (Σ n ) through a weigh matrix ( W m,n ) to feed output neurons.…”

Section: Figurementioning

confidence: 99%

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Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

et al. 2020

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Imprinting vision as memory is a core attribute of human cognitive learning. Fundamental to artificial intelligence systems are bioinspired neuromorphic vision components for the visible and invisible segments of the electromagnetic spectrum. Realization of a single imaging unit with a combination of in‐built memory and signal processing capability is imperative to deploy efficient brain‐like vision systems. However, the lack of a platform that can be fully controlled by light without the need to apply alternating polarity electric signals has hampered this technological advance. Here, a neuromorphic imaging element based on a fully light‐modulated 2D semiconductor in a simple reconfigurable phototransistor structure is presented. This standalone device exhibits inherent characteristics that enable neuromorphic image pre‐processing and recognition. Fundamentally, the unique photoresponse induced by oxidation‐related defects in 2D black phosphorus (BP) is exploited to achieve visual memory, wavelength‐selective multibit programming, and erasing functions, which allow in‐pixel image pre‐processing. Furthermore, all‐optically driven neuromorphic computation is demonstrated by machine learning to classify numbers and recognize images with an accuracy of over 90%. The devices provide a promising approach toward neurorobotics, human–machine interaction technologies, and scalable bionic systems with visual data storage/buffering and processing.

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Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

et al. 2020

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“…One of the leading directions is utilizing the braininspired parallel computing systems based on artificial synapses, i.e., artificial neural systems. [1,[6][7][8][9][10][11] Just like the information transmission between the two neurons in the biological neural network, the artificial synapse provides an effective approach for data-processing depends on biomimetic synaptic processes, which could realize a specific logic function through one individual component. [1,[12][13][14] Through transforming presynaptic stimuli into postsynaptic responses, an artificial synaptic device could mimic the perception, learning, and memory functions of synapses in the human brain.…”

mentioning

confidence: 99%

Bio‐Inspired Photoelectric Artificial Synapse based on Two‐Dimensional Ti₃C₂T_x MXenes Floating Gate

Zhao

et al. 2021

Adv Funct Materials

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The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.

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“…used a near‐infrared synapse to simulate a single‐layer perceptron for supervised learning. [ 334 ] Recently, Mennel et al. demonstrated that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images.…”

Section: Applicationmentioning

confidence: 99%

“…[331] In the same year, Zhai et al used a near-infrared synapse to simulate a single-layer perceptron for supervised learning. [334] Recently, Mennel et al demonstrated that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images. Figure 28b shows an image sensor array that can identify 3 × 3 pixel images, where each pixel consists of three WSe 2 photodiodes/subpixels.…”

Section: Application Of Bionicsmentioning

confidence: 99%

The Future of Memristors: Materials Engineering and Neural Networks

Sun

Chen

Yan

2020

Adv Funct Materials

272

173

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From Deep Blue to AlphaGo, artificial intelligence and machine learning are booming, and neural networks have become the hot research direction. However, due to the size limit of complementary metal–oxide–semiconductor (CMOS) transistors, von Neumann‐based computing systems are facing multiple challenges (such as memory walls). As the number of transistors required by the neural network increases, the development of neural networks based on the von Neumann computer is limited by volume and energy consumption. As the fourth basic circuit element, memristor shines in the field of neuromorphic computing. The new computer architecture based on memristor is widely considered as a substitute for the von Neumann architecture and has great potential to deal with the neural network and big data era challenge. This article reviews existing materials and structures of memristors, neurophysiological simulations based on memristors, and applications of memristor‐based neural networks. The feasibility and advancement of implementing neural networks using memristors are discussed, the difficulties that need to be overcome at this stage are put forward, and their development prospects and challenges faced are also discussed.

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Near infrared neuromorphic computing via upconversion-mediated optogenetics

Cited by 61 publications

References 46 publications

Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

Bio‐Inspired Photoelectric Artificial Synapse based on Two‐Dimensional Ti₃C₂T_x MXenes Floating Gate

The Future of Memristors: Materials Engineering and Neural Networks

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Near infrared neuromorphic computing via upconversion-mediated optogenetics

Cited by 61 publications

References 46 publications

Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

Fully Light‐Controlled Memory and Neuromorphic Computation in Layered Black Phosphorus

Bio‐Inspired Photoelectric Artificial Synapse based on Two‐Dimensional Ti3C2Tx MXenes Floating Gate

The Future of Memristors: Materials Engineering and Neural Networks

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Product

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Bio‐Inspired Photoelectric Artificial Synapse based on Two‐Dimensional Ti₃C₂T_x MXenes Floating Gate