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DL for Neuro-heuristic Brain Analysis 2024 : Workshop on Deep Learning for Neuro-heuristic Brain Analysis @ ICANN'24

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Link: https://sites.google.com/view/dl4nhba/
 
When Sep 17, 2024 - Sep 20, 2024
Where Lugano, Switzerland
Submission Deadline Mar 15, 2024
Notification Due May 15, 2024
Categories    deep learning   machine learning   brain analysis
 

Call For Papers

Deep learning algorithms, with their capacity to identify non-linear patterns and relationships within vast datasets, have revolutionized our ability to decipher the complexities of the human brain and are thus being increasingly used in contemporary neuroscience research. Indeed, the intricate nature of neural processes calls for the use of advanced analytical tools, and deep learning provides a novel framework for extracting meaningful insights from neuroscientific data. Moreover, this trend has recently encouraged the adoption of Graph Neural Networks (GNNs) to analyze and understand how the pattern of connectivity in biological neural systems might account for human brain function and behavior. Indeed, the brain graph is a natural fit for GNN models, since they can preserve graph topological properties while learning to perform a given task.
This workshop aims to explore the integration of deep learning techniques with neuro-heuristic approaches for the advanced analysis of brain data, with a particular focus on discussing how deep learning can be used to enhance our understanding of the brain as a complex self-organizing system, how its topological properties drive the interplay between sensory processing, sensorimotor integration, and cognition, and how these properties are affected by brain diseases.
The primary goal will be to encourage discussion about the potential and the limitations of novel application of deep learning techniques in the analysis of brain data and explore the transformative impact of deep learning for tackling challenging questions in neuroscience research.
All accepted papers will be published in the Springer LNCS series. Read all the details in the call for papers on the workshop website:

https://sites.google.com/view/dl4nhba/

Topics
This workshop focuses on the broad spectrum of application of deep learning techniques in the analysis of brain data. Theoretical and methodological papers are welcome from any of the following areas, including but not limited to:

• Deep learning for neuroimaging analysis
• Deep learning for Functional Magnetic Resonance Imaging (fMRI) data analysis
• Deep learning in functional Near-Infrared Apectroscopy (fNIRS)
• Deep learning models for brain signal analysis
• Electroencephalography (EEG) data analysis using deep learning
• Deep learning for brain connectivity and network analysis
• Functional brain connectivity analysis using deep Learning
• Structural brain connectivity analysis deep Learning
• Deep Learning for graph theoretical analysis of brain networks.
• Deep learning for early detection and diagnosis of neurological disorders
• Deep learning model for brain activity classification
• Deep learning models for predicting cognitive states based on brain data
• Brain age estimation using deep learning models

Location
The Workshop will be located within the 33rd International Conference on Artificial Neural Networks (ICANN 2024), which will be held in Lugano, Switzerland from 17 to 20 September 2024. For registration, venue, and other information, please see the ICANN 2024 conference website:
https://e-nns.org/icann2024/

Important dates
-Deadline for submissions: 15 March 2024
-Notification of acceptance or rebuttal: 15 May 2024
-Deadline for rebuttal: 31 May 2024
-Final notification of acceptance or rejection after rebuttal: 10 June 2024
-Conference dates: 17 to 20 September 2024

Sincerely,
the Organizing Committee:
-Alessandro Villa (University of Lausanne)
-Alessandra Lintas (University of Lausanne)
-Luca Pasa (University of Padua)
-Nicolò Navarin (University of Padua)
-Alberto Testolin (University of Padua)
-Marco Zorzi (University of Padua)
-Alessandro Sperduti (University of Padua)

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