We reproduced a decision-making network model using the neural simulator software neural simulation tool (NEST), and we embedded the spiking neural network in a virtual robotic agent performing a simulated behavioral task. The present work builds upon the concept of replicability in neuroscience, preserving most of the computational properties in the initial model although employing a different software tool. The proposed implementation successfully obtains equivalent results from the original study, reproducing the salient features of the neural processes underlying a binary decision. Furthermore, the resulting network is able to control a robot performing an visual discrimination task, the implementation of which is openly available on the EBRAINS infrastructure through the neuro robotics platform (NRP).
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http://dx.doi.org/10.3389/fnint.2022.930326 | DOI Listing |
PLoS One
January 2025
School of Civil and Architectural Engineering, Harbin University, Harbin, China.
This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
There is a growing need to document sociodemographic factors in electronic medical records to produce representative cohorts for medical research and to perform focused research for potentially vulnerable populations. The objective of this work was to assess the content of family physicians' electronic medical records and characterize the quality of the documentation of sociodemographic characteristics. Descriptive statistics were reported for each sociodemographic characteristic.
View Article and Find Full Text PDFCureus
January 2025
General Practice, Wad Medani Hospital, Wad Medani, SDN.
To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology.
View Article and Find Full Text PDFEpilepsia
January 2025
Department of Neurology, Neurocritical Care, and Neurorehabilitation, Center for Cognitive Neuroscience, Member of European Reference Network EpiCARE, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria.
Objective: People with epilepsy (PWEs) often face difficulties in obtaining or keeping employment. To determine the views on this topic of the heads of human resources (HHRs) and occupational physicians (OCPs).
Method: Twelve HHRs and five OCPs underwent a telephone interview concerning the opportunities and limitations of job applications for PWEs.
Strahlenther Onkol
January 2025
Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine and Health, Technical, University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.
Purpose: General practitioners (GPs) play a crucial role in providing interdisciplinary care for radiation oncology patients. This study aims to understand the specific needs and challenges faced by general practitioners in Germany when treating oncology patients.
Methods: A comprehensive web-based questionnaire with 24 items was disseminated to GPs in Germany via email using survio.
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