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Extreme Multistability in a Hopfield Neural Network Based on Two Biological Neuronal Systems
IEEE Transactions on Circuits and Systems - II - Express Briefs, 2022Based 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 +3 more
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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 +6 more
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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 +6 more
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Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction
Briefings Bioinform., 2023Drug-target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data.
Mei Li, Xiangrui Cai, Sihan Xu, Hua Ji
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Brain-Inspired Spiking Neural Network Using Superconducting Devices
IEEE Transactions on Emerging Topics in Computational Intelligence, 2023Based 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 +4 more
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Lightweight Attention Convolutional Neural Network for Retinal Vessel Image Segmentation
IEEE Transactions on Industrial Informatics, 2021Retinal 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 +3 more
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Synchrony measures for biological neural networks
Biological Cybernetics, 1995Synchronous firing of a population of neurons has been observed in many experimental preparations; in addition, various mathematical neural network models have been shown, analytically or numerically, to contain stable synchronous solutions. In order to assess the level of synchrony of a particular network over some time interval, quantitative measures
Pinsky, Paul F., Rinzel, John
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IEEE Transactions on Neural Networks and Learning Systems, 2018
This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced ...
Arash Gharehbaghi, M. Lindén
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This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced ...
Arash Gharehbaghi, M. Lindén
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Introduction to Neural Networks: Biological Neural Network
2023Chapter 1 introduces the functional organization of the biological brain. The first section opens with the description of neurons, fundamental units of the brain. These are structures capable of collecting signals, processing them and delivering them to subsequent units.
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Robustness in biological neural networks
Physica A: Statistical Mechanics and its Applications, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kalampokis, Alkiviadis +3 more
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CAJAL - 91: A Biological Neural Network Simulator
, 1992E. Blum +4 more
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