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From chaos to clock in recurrent neural net. Case study
Biosystems, 2022What is the reason for complex dynamical patterns registered from real biological neuronal networks? Noise and dynamical reconfiguring of a network (functional/dynamic connectome) were proposed as possible answers. In this case study, we report a complex dynamical pattern observed in a simple deterministic network of 25 excitatory neurons with fixed ...
Alexander K. Vidybida, Olha Shchur
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On the identification of recurrent neural nets
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002Observational equivalence for so-called Jordan networks, which are a special class of recurrent networks, is analysed. We show this type of neural nets to belong to a wider class of mixed networks and use the description of observational equivalence available for the latter class for obtaining the respective results for the first class.
Dietmar Trummer, Manfred Deistler
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Unsupervised Feature Learning Using Recurrent Neural Nets for Segmenting Hyperspectral Images
IEEE Geoscience and Remote Sensing Letters, 2021Although deep learning is gaining more widespread use in hyperspectral image analysis, it is challenging to train high-capacity models in a supervised way--ground-truth sets are expensive to obtain, and they are practically always extremely imbalanced.
Lukasz Tulczyjew +2 more
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Asynchronous translations with recurrent neural nets
Proceedings of International Conference on Neural Networks (ICNN'97), 2002Many researchers have explored the relation between discrete-time recurrent neural networks (DTRNN) and finite-state machines (FSMs) either by showing their computational equivalence or by training them to perform as finite-state recognizers from examples.
Ramón P. Ñeco, Mikel L. Forcada
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Modelling Landsurface Time-Series with Recurrent Neural Nets
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Machine learning tools and semi-empirical models have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere state and function. Yet, little work has been carried to explicitly model dynamic features accounting for memory effects, where in some cases hand-designed ...
Markus Reichstein +6 more
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A Structure Theory for Identification of Recurrent Neural Nets - Part 1
IFAC Postprint Volumes IPPV / International Federation of Automatic Control, 1998Abstract This paper extends some results on identifiability of recurrent (dynamic) neural nets obtained by F.Albertini and E.Sontag. A topological and geometrical description of the set of identifiable paranleters is given. In addition it is shown that the classes of observationally equivalent non-nlinimal systems are essentially finite unions of ...
Monika Dörfler, Manfred Deistler
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Recurrent neural nets as dynamical Boolean systems with application to associative memory
IEEE Transactions on Neural Networks, 1997Discrete-time/discrete-state recurrent neural networks are analyzed from a dynamical Boolean systems point of view in order to devise new analytic and design methods for the class of both single and multilayer recurrent artificial neural networks. With the proposed dynamical Boolean systems analysis, we are able to formulate necessary and sufficient ...
M H Hassoun
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RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation
IEEE Journal of Biomedical and Health Informatics, 2019The level set based deformable models (LDM) are commonly used for medical image segmentation. However, they rely on a handcrafted curve evolution velocity that needs to be adapted for each segmentation task. The Convolutional Neural Networks (CNN) address this issue by learning robust features in a supervised end-to-end manner.
Arunava Chakravarty, Jayanthi Sivaswamy
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