Efficient event-based delay learning in spiking neural networks
Nature CommunicationsSpiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks.
Balázs Mészáros +2 more
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An infrastructure for neural network construction [PDF]
, 2005 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.After many years of research the area of Artificial Intelligence is still searching for ways to construct a truly intelligent system.
Stewart, Richard
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Minimum Description Length Recurrent Neural Networks
Transactions of the Association for Computational Linguistics, 2022 Nur Lan +3 more
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Conductance-based reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance. [PDF]
Front Comput NeurosciRodrigues M, Gasco-Galvez C, Vinck M.
europepmc +1 more source
Mutual information minimization enhances noise robustness and induces fault containment in functionally differentiated recurrent neural networks. [PDF]
Front NeurosciTomoda Y, Yamaguti Y.
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Respiratory Disease Classification Using NMF-Enhanced Log-Mel Spectrograms and Convolutional Recurrent Neural Networks. [PDF]
Sensors (Basel)Han B, Quan W, Matuszewski B, Corbett D.
europepmc +1 more source
Neural Network Models for Inflation Forecasting: An Appraisal [PDF]
We assess the power of artificial neural network models as forecasting tools for monthly inflation rates for 28 OECD countries. For short out-of-sample forecasting horizons, we find that, on average, for 45% of the countries the ANN models were a ...
Adnan Haider, Ali Choudhary
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Neuromodulators Generate Multiple Context-Relevant Behaviors in Recurrent Neural Networks. [PDF]
Neural ComputTsuda B +4 more
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Predicting depressive symptoms among Chinese college students using recurrent neural networks with longitudinal data. [PDF]
Sci RepJiang T +6 more
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Feedback control guides credit assignment in recurrent neural networks
How do brain circuits learn to generate behaviour? While significant strides have been made in understanding learning in artificial neural networks, applying this knowledge to biological networks remains challenging.
Feulner, B +3 more
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