Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Tang, Jianshi; Yuan, Fang; Shen, Xinke; Wang, Zhongrui; Rao, Mingyi; He, Yanlin; Sun, Yuhao; Li, Xinyi; Zhang, Wenbing; Li, Yijun; Gao, Bin; Qian, He; Bi, Guoqiang; Song, Sen; Yang, J. Joshua; Wu, Huaqiang

doi:10.1002/adma.201902761

Cited by 472 publications

(355 citation statements)

References 274 publications

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“…Similar power‐law scaling behavior was observed in spontaneous neural oscillations generated in the human brain . More importantly, network dynamics can be exploited for the emulation of short‐term synaptic plasticity (STP) that regulates the information exchange and processing within biological neural networks . By applying an over‐threshold constant voltage bias, the network conductance (synaptic weight) between two pads (neuron terminals) can be gradually increased (facilitation), as shown in Figure b (details in S7, Supporting Information).…”

mentioning

confidence: 58%

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

Milano

Pedretti

Fretto

et al. 2020

Advanced Intelligent Systems

104

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Acting as artificial synapses, two‐terminal memristive devices are considered fundamental building blocks for the realization of artificial neural networks. Current memristive crossbar architectures demonstrate the implementation of neuromorphic computing paradigms, although they are unable to emulate typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Herein, self‐organizing memristive random nanowire (NW) networks with functional connectivity able to display homo‐ and heterosynaptic plasticity is reported thanks to the mutual electrochemical interaction among memristive NWs and NW junctions. In particular, it is shown that rewiring and reweighting effects observed in single NWs and single NW junctions, respectively, are responsible for structural plasticity of the network under electrical stimulation. Such biologically inspired systems allow a low‐cost realization of neural networks that can learn and adapt when subjected to multiple external stimuli, emulating the experience‐dependent synaptic plasticity that shape the connectivity and functionalities of the nervous system that can be exploited for hardware implementation of unconventional computing paradigms.

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mentioning

confidence: 58%

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

Milano

Pedretti

Fretto

et al. 2020

Advanced Intelligent Systems

104

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“…Therefore, integrating the algorithmic power of deep SNNs with the compelling energy efficiency of NC hardware represents an intriguing solution for pervasive machine learning tasks and always-on applications. Furthermore, growing research efforts are devoted to developing novel non-volatile memory devices for ultra-low-power implementation of biological synapses and neurons (Tang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

Yılmaz

Zhang

et al. 2020

Front. Neurosci.

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Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of computation. The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation. Motivated by their unprecedented energyefficiency and rapid information processing capability, we explore the use of SNNs for speech recognition. In this work, we use SNNs for acoustic modeling and evaluate their performance on several large vocabulary recognition scenarios. The experimental results demonstrate competitive ASR accuracies to their ANN counterparts, while require significantly reduced computational cost and inference time. Integrating the algorithmic power of deep SNNs with energy-efficient neuromorphic hardware, therefore, offer an attractive solution for ASR applications running locally on mobile and embedded devices.

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“…Among them, remarkable progress has been made on memristors for the implementation of ANNs by taking advantage of their analog resistive switching properties. [24][25][26][27][28][29] Meanwhile, the inherent dynamic properties and nonlinear behaviour of memristors also make them very suitable for the implementation of RC systems. 30,31 In a RC system, the richness of the reservoir states is an important factor that largely determines the system performance.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Memristor-based Reservoir Computing for High-Efficiency Spatiotemporal Signal Processing

Zhong

Tang

Gao

et al. 2020

Preprint

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Reservoir computing (RC) is a highly efficient network for processing spatiotemporal signals due to its low training cost compared to standard recurrent neural networks. The design of different reservoir states plays a very important role in the hardware implementation of RC system. Recent studies have used the device-to-device variation to generate different reservoir states; however, this method is not well controllable and reproducible. To solve this problem, we report a dynamic memristor-based RC system. By applying a controllable mask process, we reveal that even a single dynamic memristor can generate rich reservoir states and realize the complete reservoir function. We further build a parallel RC system that can efficiently handle spatiotemporal tasks including spoken-digit and handwritten-digit recognitions, in which high classification accuracies of 99.6% and 97.6% have been achieved, respectively. The performance of dynamic memristor-based RC system is almost equivalent to the software-based one. Besides, our RC system does not require additional read operations, which can make full use of the device nonlinearity and further improve the system efficiency. Our work could pave the road towards high-efficiency memristor-based RC systems to handle more complex spatiotemporal tasks in the future.

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Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Cited by 472 publications

References 274 publications

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

Dynamic Memristor-based Reservoir Computing for High-Efficiency Spatiotemporal Signal Processing

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