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Schema generation in recurrent neural nets for intercepting a moving target
Biological Cybernetics, 2010The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load.
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A Myocardial T1-Mapping Framework with Recurrent and U-Net Convolutional Neural Networks
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020Noise and aliasing artifacts arise in various accelerated cardiac magnetic resonance (CMR) imaging applications. In accelerated myocardial T1-mapping, the traditional three-parameter based nonlinear regression may not provide accurate estimates due to sensitivity to noise. A deep neural network-based framework is proposed to address this issue.
Haris Jeelani +5 more
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Energy-Time Tradeoff in Recurrent Neural Nets
2015In this chapter, we deal with the energy complexity of perceptron networks which has been inspired by the fact that the activity of neurons in the brain is quite sparse (with only about 1% of neurons firing). This complexity measure has recently been introduced for feedforward architectures (i.e., threshold circuits).
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GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation
Medical Image Analysis, 2019Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks.
Thomas Wollmann +5 more
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Computer Simulations of Recurrent Neural Nets for Temporal Recognition Problems
1992A number of approaches are presented to the use of neural nets with feedback to handle the recognition of temporal data and these are assessed with respect to resilience (ability to handle noisy or incomplete input) and performance in handling similar and overlapping patterns.
G. S. Cooper, T. M. Child
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The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1998Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs.
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Alpha-nets: A recurrent ‘neural’ network architecture with a hidden Markov model interpretation
Speech Communication, 1990Abstract A hidden Markov model isolated word recogniser using full likelihood scoring for each word model can be treated as a recurrent ‘neural’ network. The units in the recurrent loop are linear, but the observations enter the loop via a multiplication.
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Fast convergence algorithm for multilayer and recurrent neural nets
1998The presentation deals with a class of fast algoritms for training feedforward and recurrent neural networks, especially suited dor signal processing and telecommunications.
DI CLAUDIO, Elio +2 more
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Evolutionary Chromatographic Law Identification by Recurrent Neural Nets
1995Alessandro Fadda, Marc Schoenauer
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