Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Shuangming Yang"'
Publikováno v:
Fermentation, Vol 9, Iss 11, p 954 (2023)
The purpose of this study was to investigate the effects of replacing a portion of whole-plant corn silage with straw on the rumen microbial community structure and carbohydrate-active enzyme activity. The experiment employed a single-factor randomiz
Externí odkaz:
https://doaj.org/article/8bf77c3e4fb8426e94215c67fad03b58
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 7308-7322 (2022)
Change detection is an important task of identifying changed information by comparing bitemporal images over the same geographical area. Currently, many existing methods based on U-Net and attention mechanism have greatly promoted the development of
Externí odkaz:
https://doaj.org/article/18c0a714e8df4174a057dbf381af9de8
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the major challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap
Externí odkaz:
https://doaj.org/article/4aa21fab71b04664972d169023f93cc3
Autor:
Shuangming Yang, Tian Gao, Jiang Wang, Bin Deng, Mostafa Rahimi Azghadi, Tao Lei, Bernabe Linares-Barranco
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Working memory is a fundamental feature of biological brains for perception, cognition, and learning. In addition, learning with working memory, which has been show in conventional artificial intelligence systems through recurrent neural networks, is
Externí odkaz:
https://doaj.org/article/0b56e8f46f104650b4bc830e67f5c91a
Publikováno v:
Frontiers in Neuroscience, Vol 15 (2021)
A critical challenge in neuromorphic computing is to present computationally efficient algorithms of learning. When implementing gradient-based learning, error information must be routed through the network, such that each neuron knows its contributi
Externí odkaz:
https://doaj.org/article/016be3cdf064473ead879f0b47a202b7
Publikováno v:
Entropy, Vol 24, Iss 4, p 455 (2022)
The spiking neural network (SNN) is regarded as a promising candidate to deal with the great challenges presented by current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a
Externí odkaz:
https://doaj.org/article/b6b33431e28547d89e3942b727429163
Publikováno v:
Frontiers in Neuroscience, Vol 13 (2019)
Purkinje cell is an important neuron for the cerebellar information processing. In this work, we present an efficient implementation of a cerebellar Purkinje model using the Coordinate Rotation Digital Computer (CORDIC) algorithm and implement it on
Externí odkaz:
https://doaj.org/article/ec62ecaea2894aaa97e41fed6526615b
Publikováno v:
Applied Sciences, Vol 10, Iss 8, p 2857 (2020)
In this paper, an expanded digital hippocampal spurt neural network (HSNN) is innovatively proposed to simulate the mammalian cognitive system and to perform the neuroregulatory dynamics that play a critical role in the cognitive processes of the bra
Externí odkaz:
https://doaj.org/article/2f67166fdb6c43f7ba52459b1fd4e8e3
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. :1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8174c77530cebbcb1ffb9cdeadb98ac9
https://doi.org/10.1109/tits.2022.3232231
https://doi.org/10.1109/tits.2022.3232231
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:2801-2815
The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the correspond
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7198b9be761fc2b62f60eeed1190404
https://doi.org/10.1109/tnnls.2020.3045492
https://doi.org/10.1109/tnnls.2020.3045492