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A machine learning model guided by physical principles for biofilter performance prediction. [PDF]

Sci Rep
Uzma, Cholet F, Quinn D, Smith CJ, You S, Sloan WT.   +5 more
europepmc   +1 more source

Predicting microbial community structure and temporal dynamics by using graph neural network models. [PDF]

Nat Commun
Andersen KS, Zhao K, Agerskov AL, Sørensen CB, Holmager TJ, Nierychlo M, Peces M, Guo C, Nielsen PH.   +8 more
europepmc   +1 more source

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Memristor-Based Neural Network Circuit of Operant Conditioning Accorded With Biological Feature

IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2022
Most memristor-based associative memory neural networks are focused on classical conditioning and ignored operant conditioning. In this paper, a memristor-based neural network of operant conditioning accorded with biological feature is designed.
Junwei Sun, Juntao Han, Yanfeng Wang, Peng Liu   +3 more
semanticscholar   +1 more source

Extreme Multistability in a Hopfield Neural Network Based on Two Biological Neuronal Systems

IEEE Transactions on Circuits and Systems - II - Express Briefs, 2022
Based on different biological neuronal systems, various memristive neuron and neuron network models are generated. In this brief, a three-neurons-based Hopfield neural network with two biological neural mechanisms is investigated, with one of three ...
Lilian Huang, Yue Zhang, Jianhong Xiang, Jin Liu   +3 more
semanticscholar   +1 more source

Graph Signal Processing, Graph Neural Network and Graph Learning on Biological Data: A Systematic Review

IEEE Reviews in Biomedical Engineering, 2021
Graph networks can model data observed across different levels of biological systems that span from population graphs (with patients as network nodes) to molecular graphs that involve omics data.
Rui Li, Xin Yuan, Mohsen Radfar, Peter Marendy, W. Ni, T. J. O’Brien, Pablo M. Casillas-Espinosa   +6 more
semanticscholar   +1 more source

Brain-Inspired Spiking Neural Network Using Superconducting Devices

IEEE Transactions on Emerging Topics in Computational Intelligence, 2023
Based on recent research in artificial neural networks, researchers have focused on topics from brain-like computing based on the Von Neumann architecture to brain-inspired computing based on the integration of storage and calculation due to the large ...
Huilin Zhang, Chen Gang, Chen Xu, Guoliang Gong, Huaxiang Lu   +4 more
semanticscholar   +1 more source

Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation

IEEE Transactions on Industrial Informatics, 2021
Retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain.
Xiang Li, Yuchen Jiang, Minglei Li, Shen Yin   +3 more
semanticscholar   +1 more source

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons

IEEE Transactions on Neural Networks and Learning Systems, 2020
Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons.
Shuangming Yang, Bin Deng, Jiang Wang, Huiyan Li, Meili Lu, Y. Che, Xile Wei, K. Loparo   +7 more
semanticscholar   +1 more source

Memristor-Based Design of Sparse Compact Convolutional Neural Network

IEEE Transactions on Network Science and Engineering, 2020
Memristor has been widely studied for hardware implementation of neural networks due to the advantages of nanometer size, low power consumption, fast switching speed and functional similarity to biological synapse.
Shiping Wen, Huaqiang Wei, Zheng Yan, Zhenyuan Guo, Yin Yang, Tingwen Huang, Yiran Chen   +6 more
semanticscholar   +1 more source

Review on chaotic dynamics of memristive neuron and neural network

Nonlinear dynamics, 2021
Hairong Lin, Chunhua Wang, Quanli Deng, Cong Xu, Zekun Deng, Chao Zhou   +5 more
semanticscholar   +1 more source