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      A biomimetic neural encoder for spiking neural network

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          Abstract

          Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of SNNs in future neuromorphic hardware requires hardware encoders analogous to the sensory neurons, which convert external/internal stimulus into spike trains based on specific neural algorithm along with inherent stochasticity. Unfortunately, conventional solid-state transducers are inadequate for this purpose necessitating the development of neural encoders to serve the growing need of neuromorphic computing. Here, we demonstrate a biomimetic device based on a dual gated MoS 2 field effect transistor (FET) capable of encoding analog signals into stochastic spike trains following various neural encoding algorithms such as rate-based encoding, spike timing-based encoding, and spike count-based encoding. Two important aspects of neural encoding, namely, dynamic range and encoding precision are also captured in our demonstration. Furthermore, the encoding energy was found to be as frugal as ≈1–5 pJ/spike. Finally, we show fast (≈200 timesteps) encoding of the MNIST data set using our biomimetic device followed by more than 91% accurate inference using a trained SNN.

          Abstract

          The implementation of spiking neural network in future neuromorphic hardware requires hardware encoder analogous to the sensory neurons. The authors show a biomimetic dual-gated MoS 2 field effect transistor capable of encoding analog signals into stochastic spike trains at energy cost of 1–5 pJ/spike.

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          Deep learning in neural networks: An overview

          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            Progress, challenges, and opportunities in two-dimensional materials beyond graphene.

            Graphene's success has shown that it is possible to create stable, single and few-atom-thick layers of van der Waals materials, and also that these materials can exhibit fascinating and technologically useful properties. Here we review the state-of-the-art of 2D materials beyond graphene. Initially, we will outline the different chemical classes of 2D materials and discuss the various strategies to prepare single-layer, few-layer, and multilayer assembly materials in solution, on substrates, and on the wafer scale. Additionally, we present an experimental guide for identifying and characterizing single-layer-thick materials, as well as outlining emerging techniques that yield both local and global information. We describe the differences that occur in the electronic structure between the bulk and the single layer and discuss various methods of tuning their electronic properties by manipulating the surface. Finally, we highlight the properties and advantages of single-, few-, and many-layer 2D materials in field-effect transistors, spin- and valley-tronics, thermoelectrics, and topological insulators, among many other applications.
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              Neuromorphic electronic systems

              C Mead (1990)
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                Author and article information

                Contributors
                sud70@psu.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 April 2021
                9 April 2021
                2021
                : 12
                : 2143
                Affiliations
                [1 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Engineering Science and Mechanics, , Pennsylvania State University, ; University Park, PA USA
                [2 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Electrical Engineering, , Pennsylvania State University, ; University Park, PA USA
                [3 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Materials Science and Engineering, , Pennsylvania State University, ; University Park, PA USA
                [4 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Materials Research Institute, , Pennsylvania State University, ; University Park, PA USA
                Author information
                http://orcid.org/0000-0003-1136-7425
                http://orcid.org/0000-0003-4558-0013
                http://orcid.org/0000-0003-1001-2198
                http://orcid.org/0000-0003-4553-5693
                http://orcid.org/0000-0002-0188-945X
                Article
                22332
                10.1038/s41467-021-22332-8
                8035177
                33837210
                1b57390a-011b-47b7-99a8-6224a031412e
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 September 2020
                : 9 March 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000183, United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO);
                Award ID: W911NF1920338
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                electronic devices,sensors and biosensors,two-dimensional materials
                Uncategorized
                electronic devices, sensors and biosensors, two-dimensional materials

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