Behavioral Classification of Sequential Neural Activity Using Time Varying Recurrent Neural Networks [PDF]
IEEE Transactions on Neural Systems and Rehabilitation EngineeringShifts in data distribution across time can strongly affect early classification of time-series data. When decoding behavior from neural activity, early detection of behavior may help in devising corrective neural stimulation before the onset of behavior.
Yongxu Zhang +5 more
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Fluctuation-learning relationship in recurrent neural networks [PDF]
Nature CommunicationsLearning speed depends on both task structure and neural dynamics prior to learning, yet a theory connecting them has been missing. Inspired by the fluctuation-response relation, we derive two formulae linking neural dynamics to learning.
Tomoki Kurikawa, Kunihiko Kaneko
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Survey on Evolutionary Recurrent Neural Networks [PDF]
Jisuanji kexue, 2023 Evolutionary computation utilizes natural selection mechanisms and genetic laws in the process of biological evolution to solve optimization problems.The accuracy and efficiency of the evolutionary recurrent neural network model depends on the ...
HU Zhongyuan, XUE Yu, ZHA Jiajie
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Research on recurrent neural network model based on weight activity evaluation [PDF]
ITM Web of Conferences, 2022 Given the complex structure and parameter redundancy of recurrent neural networks such as LSTM, related research and analysis on the structure of recurrent neural networks have been done.
Zhang Cheng +5 more
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Intelligent algorithms of construction of public transport routes [PDF]
E3S Web of Conferences, 2023 Today, in public transport planning systems, it is relevant to a search for a possible route with a minimum time. The aim of the work is the development of intelligent algorithms for constructing public transport routes, the development of programs, and ...
Ismailov Mirxalil +5 more
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A new approach to seasonal energy consumption forecasting using temporal convolutional networks
Results in Engineering, 2023 There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature.
Abdul Khalique Shaikh +4 more
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An attractor-based complexity measurement for Boolean recurrent neural networks. [PDF]
PLoS ONE, 2014 We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics.
Jérémie Cabessa, Alessandro E P Villa
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Reinforcement Learning for Central Pattern Generation in Dynamical Recurrent Neural Networks
Frontiers in Computational Neuroscience, 2022 Lifetime learning, or the change (or acquisition) of behaviors during a lifetime, based on experience, is a hallmark of living organisms. Multiple mechanisms may be involved, but biological neural circuits have repeatedly demonstrated a vital role in the
Jason A. Yoder +3 more
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Stability and Synchronization of Switched Multi-Rate Recurrent Neural Networks
IEEE Access, 2021 Several designs of recurrent neural networks have been proposed in the literature involving different clock times. However, the stability and synchronization of this kind of system have not been studied.
Victoria Ruiz +3 more
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Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks
Sensors, 2021 Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently.
Ke Zang, Wenqi Wu, Wei Luo
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