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Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks

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The article was published on 2021-06-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Neuromorphic engineering & Artificial neural network.

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Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons

TL;DR: This work proposes a purely photonic operation of an Integrate-and-Fire Spiking neuron, based on the phase change dynamics of Ge2Sb2Te5 (GST) embedded on top of a microring resonator, which alleviates the energy constraints of PCMs in electrical domain.
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Exploiting Ultralow Loss Multimode Waveguides for Broadband Frequency Combs.

TL;DR: In this article, the authors show ultralow propagation loss by shaping the mode using a highly multimode structure to reduce its overlap with the waveguide interfaces, thus relaxing the fabrication processing requirements.
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On-Chip Integrated Photonic Devices Based on Phase Change Materials

TL;DR: This review seeks to highlight the progress thus far made in on-chip devices derived from phase change materials including memory devices, neuromorphic computing, switches, and modulators.
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All-optical silicon microring spiking neuron

- 11 Mar 2022 - 
TL;DR: In this article , the authors systematically characterize the spiking dynamics of a passive silicon microring neuron and demonstrate that it can function as an all-optical class II resonate-and-fire neuron.
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Deep learning in light–matter interactions

TL;DR: The emerging opportunities and challenges of deep learning in photonics are discussed, shining light on how deep learning advances photonics.
References
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Journal ArticleDOI

Long short-term memory

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book

Deep Learning

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Learning representations by back-propagating errors

TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Mastering the game of Go with deep neural networks and tree search

TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
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