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Development and application of a clinical core data set for deep brain stimulation in Parkinson's disease, dystonia or tremor: from data collection to data exchange and data sharing.
Neurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks.
Nat Commun
January 2025
Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models.
View Article and Find Full Text PDFBeliefs About the Speaker's Reasoning Ability Influence Pragmatic Interpretation: Children and Adults as Speakers.
Open Mind (Camb)
January 2025
Department of Language Science and Technology, Saarland University, Saarbrücken, Germany.
The cooperative principle states that communicators expect each other to be cooperative and adhere to rational conversational principles. Do listeners keep track of the reasoning sophistication of the speaker and incorporate it into the inferences they derive? In two experiments, we asked participants to interpret ambiguous messages in the reference game paradigm, which they were told were sent either by another adult or by a 4-year-old child. We found an effect of speaker identity: if sent by an adult, an ambiguous message was much more likely to be interpreted as an implicature, while if sent by a child, it was a lot more likely to be interpreted literally.
View Article and Find Full Text PDFClassification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.
Brain Topogr
January 2025
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature.
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