6,123 research outputs found
Cerebellar output controls generalized spike-and-wave discharge occurence
© 2015 The Authors Annals of Neurology published by Wiley Periodicals, Inc. on behalf of American Neurological Association. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (CC BY-NC-ND 4.0) which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike-and-wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizuresPeer reviewedFinal Published versio
Oscillator models of the solar cycle: Towards the development of inversion methods
This article reviews some of the leading results obtained in solar dynamo
physics by using temporal oscillator models as a tool to interpret
observational data and dynamo model predictions. We discuss how solar
observational data such as the sunspot number is used to infer the leading
quantities responsible for the solar variability during the last few centuries.
Moreover, we discuss the advantages and difficulties of using inversion methods
(or backward methods) over forward methods to interpret the solar dynamo data.
We argue that this approach could help us to have a better insight about the
leading physical processes responsible for solar dynamo, in a similar manner as
helioseismology has helped to achieve a better insight on the thermodynamic
structure and flow dynamics in the Sun's interior.Comment: 28 pages; 16 figures, ISSI Workshop 11-15 November 2013 - The Solar
Cycle, http://www.issibern.ch/program/workshops.htm
ECGAN: Self-supervised generative adversarial network for electrocardiography
High-quality synthetic data can support the development of effective
predictive models for biomedical tasks, especially in rare diseases or when
subject to compelling privacy constraints. These limitations, for instance,
negatively impact open access to electrocardiography datasets about
arrhythmias. This work introduces a self-supervised approach to the generation
of synthetic electrocardiography time series which is shown to promote
morphological plausibility. Our model (ECGAN) allows conditioning the
generative process for specific rhythm abnormalities, enhancing synchronization
and diversity across samples with respect to literature models. A dedicated
sample quality assessment framework is also defined, leveraging arrhythmia
classifiers. The empirical results highlight a substantial improvement against
state-of-the-art generative models for sequences and audio synthesis
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