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Bio-Inspired Neural Network Dynamics-Aware Reinforcement Learning for Spiking Neural Network [PDF]
BiomimeticsArtificial Intelligence (AI) has seen rapid advancements in recent times, finding applications across various sectors and achieving notable successes. However, current AI models based on Deep Convolutional Neural Networks (DNNs) face numerous challenges,
Yu Zheng +3 more
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Explanations for Neural Networks by Neural Networks [PDF]
Applied Sciences, 2022 Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming ...
Sascha Marton +2 more
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Neural Networks With Motivation [PDF]
Frontiers in Systems Neuroscience, 2021 Animals rely on internal motivational states to make decisions. The role of motivational salience in decision making is in early stages of mathematical understanding. Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal ongoing behavior for dynamically changing motivation values. First, we show that neural
Sergey A. Shuvaev +5 more
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Unet-Astar: A Deep Learning-Based Fast Routing Algorithm for Unified PCB Routing
IEEE Access, 2023 In recent years, there has been extensive research on the routing problem of printed circuit boards (PCBs). Due to the increasing number of pins, high pin density, and unique physical constraints, manual PCB routing has become a time-consuming task to ...
Shiyuan Yin +5 more
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Reinforcement-learning-based parameter adaptation method for particle swarm optimization
Complex & Intelligent Systems, 2023 Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performances in solving different optimization problems. However, the PSO usually suffers from slow convergence.
Shiyuan Yin +6 more
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Neural network approximation [PDF]
Acta Numerica, 2021 Neural networks (NNs) are the method of choice for building learning algorithms. They are now being investigated for other numerical tasks such as solving high-dimensional partial differential equations. Their popularity stems from their empirical success on several challenging learning problems (computer chess/Go, autonomous navigation, face ...
Ronald A. DeVore +2 more
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International Journal of Advanced Robotic Systems, 2021 As a fundamental assumption in simultaneous localization and mapping, the static scenes hypothesis can be hardly fulfilled in applications of indoor/outdoor navigation or localization.
Yujia Zhai +4 more
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Prediction model of coal seam gas content based on ACSOA optimized BP neural network
Meikuang Anquan, 2022 For the problem of coal seam gas content prediction, the influencing factors of coal seam gas content were analyzed by taking No.2 coal seam of Chensilou Coal Mine as the research object.
Prediction model of coal seam gas content based on ACSOA optimized BP neural network
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Frontiers in Genetics, 2022 Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge. In this study, we selected Zn2+, Cu2+,
Sixi Hao +13 more
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Frontiers in Genetics, 2022 The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function.
Shuang Xu +13 more
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