Winner-takes-all mechanism realized by memristive neural network

Wang, Jun Jie; Yu, Qin; Hu, Shaoxiang; Liu, Yanchen; Guo, Rui; Chen, T. P.; Yin, You; Liu, Yang

doi:10.1063/1.5120973

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…[ 458–460 ] Inhibition as an efficient mean for computing has been widely used in neural network algorithms [ 461–465 ] or implemented in neuromorphic electronic hardware. [ 466–481 ]…”

Section: Algorithmic Levelmentioning

confidence: 99%

“…[458][459][460] Inhibition as an efficient mean for computing has been widely used in neural network algorithms [461][462][463][464][465] or implemented in neuromorphic electronic hardware. [466][467][468][469][470][471][472][473][474][475][476][477][478][479][480][481] Humans and other animals operate in a world of sensory uncertainty. Therefore, uncertainty or probability is another facet of our understanding of neural coding.…”

Section: Brain-like Neural Networkmentioning

confidence: 99%

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A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

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Section: Algorithmic Levelmentioning

confidence: 99%

Section: Brain-like Neural Networkmentioning

confidence: 99%

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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“…Compound memristive synapses with the STDP property have been employed in winner-take-all (WTA) ( Wang et al, 2019 ) networks to provide stochastic learning capability from a Bayesian perspective as an unsupervised model optimization with the expectation-maximization method ( Bill and Legenstein, 2014 ). As shown in Figure 13C , N spiking input neurons, y 1 ,…, y N , and K spiking network, Z 1 ,…, Z K , construct the WTA network.…”

Section: Hardware Implementation Of Probabilistic Spiking Neural Networkmentioning

confidence: 99%

Brain-Inspired Hardware Solutions for Inference in Bayesian Networks

Bagheriye

Kwisthout

2021

Front. Neurosci.

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The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating-point operations. A departure from conventional computing systems to make use of the high parallelism of Bayesian inference has attracted recent attention, particularly in the hardware implementation of Bayesian networks. These efforts lead to several implementations ranging from digital circuits, mixed-signal circuits, to analog circuits by leveraging new emerging nonvolatile devices. Several stochastic computing architectures using Bayesian stochastic variables have been proposed, from FPGA-like architectures to brain-inspired architectures such as crossbar arrays. This comprehensive review paper discusses different hardware implementations of Bayesian networks considering different devices, circuits, and architectures, as well as a more futuristic overview to solve existing hardware implementation problems.

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“…On the other hand, HfO 2 memristor devices, which have the capability of being used as synapses to store weights in artificial neural networks and the advantages of being non-volatile and can reduce system complexity, were used to implement a Binary Artificial Neural Network (BANN) for intelligent Braille recognition. The relevant information about the HfO 2 memristor devices used in this work was reported in our previous works 18 , 21 , 22 . The artificial neural network has two bias layers and three fully connected layers.…”

Section: Introductionmentioning

confidence: 99%

Braille recognition by E-skin system based on binary memristive neural network

Liu

Wang

et al. 2023

Sci Rep

Self Cite

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Braille system is widely used worldwide for communication by visually impaired people. However, there are still some visually impaired people who are unable to learn Braille system due to various factors, such as the age (too young or too old), brain damage, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics human touch sensing function for collecting Braille information. Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and, thus, the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility of realizing a wearable and low-cost Braille recognition system and a Braille learning-assistance system.

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Winner-takes-all mechanism realized by memristive neural network

Cited by 16 publications

References 20 publications

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Brain-Inspired Hardware Solutions for Inference in Bayesian Networks

Braille recognition by E-skin system based on binary memristive neural network

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