Results 101 to 110 of about 228,260 (299)

Remote Assessment of Ataxia Severity in SCA3 Across Multiple Centers and Time Points

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Spinocerebellar ataxia type 3 (SCA3) is a genetically defined ataxia. The Scale for Assessment and Rating of Ataxia (SARA) is a clinician‐reported outcome that measures ataxia severity at a single time point. In its standard application, SARA fails to capture short‐term fluctuations, limiting its sensitivity in trials.
Marcus Grobe‐Einsler   +20 more
wiley   +1 more source

WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks

open access: yesIEEE Access, 2020
Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for ...
Jinli Zhang   +3 more
doaj   +1 more source

Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Progressive Supranuclear Palsy (PSP) is a neurodegenerative ‘tauopathy’ with predominating pathology in the basal ganglia and midbrain. Caudal tau spread frequently implicates the cerebellum; however, the pattern of atrophy remains equivocal.
Chloe Spiegel   +8 more
wiley   +1 more source

A constructivist neural network model of German verb inflection in agrammatic aphasia [PDF]

open access: yes, 1999
We present a constructivist neural network that closely models the performance of agrammatic aphasics on German participle inflection. The network constructs a modular architecture leading to a double dissociation between regular and irregular verbs, and
Willshaw, David, Westermann, G, Penke, M
core  

Addressing State Representation in Deep Reinforcement Learning: a critical analysis of state-of-the-art” methods

open access: yes, 2022
openDeep Reinforcement Learning models use a Deep Neural Network to compute the Q-function, avoiding some computational and memory issues related to the Q-table in classic Reinforcement Learning. However, DeepRL models suffer from high sample complexity;
CANNAVÒ, FIAMMETTA
core  

Value of MRI Outcomes for Preventive and Early‐Stage Trials in Spinocerebellar Ataxias 1 and 3

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To examine the value of MRI outcomes as endpoints for preventive and early‐stage trials of two polyglutamine spinocerebellar ataxias (SCAs). Methods A cohort of 100 participants (23 SCA1, 63 SCA3, median Scale for the Assessment and Rating of Ataxia (SARA) score = 5, 42% preataxic, and 14 gene‐negative controls) was scanned at 3T up ...
Thiago J. R. Rezende   +26 more
wiley   +1 more source

Beyond Supervised Representation Learning

open access: yes, 2019
The complexity of any information processing task is highly dependent on the space where data is represented. Unfortunately, pixel space is not appropriate for the computer vision tasks such as object classification.
Noroozi, Mehdi
core  

Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett   +8 more
wiley   +1 more source

Structural representation learning for network alignment with self-supervised anchor links

open access: yes, 2020
Network alignment, the problem of identifying similar nodes across networks, is an emerging research topic due to its ubiquitous applications in many data domains such as social-network reconciliation and protein-network analysis.
Huynh, Thanh Trung   +6 more
core   +1 more source

Unsupervised learning of overlapping image components using divisive input modulation [PDF]

open access: yes, 2009
This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive
De Meyer, Kris   +5 more
core   +1 more source

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