Call for papers

Scope of the workshop

Recent advancements in artificial intelligence and computational modeling have sparked a new era in understanding complex systems, particularly in neuroscience. Foundation models, distinguished by their capability to integrate diverse data modalities, have emerged as powerful tools in decoding the complexities of the visual cortex in both human and animal subjects. This workshop aims to explore state-of-the-art techniques and methodologies for developing multimodal foundational models that comprehensively represent the visual cortex.

Topics

Expected submissions should cover, but are not limited to, the following topics:

• Theoretical Frameworks and Computational Approaches: Novel theoretical constructs and computational strategies for modeling the visual cortex using multimodal data.
• Integration of Diverse Data Sources: Techniques and challenges in integrating and harmonizing heterogeneous data modalities such as fMRI, EEG, in vivo two-photon calcium imaging, fNIRS, and others.
• Learning Paradigms for Noisy Data: Innovations in learning algorithms and paradigms to effectively handle noisy and incomplete data in modeling brain functions.
• Applications in Neuroscientific Research: Practical applications of multimodal foundational models in elucidating perception, cognition, and neurodevelopmental or neurodegenerative disorders.
• Contrastive Learning for Multimodal Brain Data Fusion: Techniques and advancements in leveraging contrastive learning methods to fuse multimodal brain data for enhanced representation and analysis.
• Self-Supervised Learning for Temporal Brain Dynamics: Approaches utilizing self-supervised learning to capture and model temporal dynamics in brain imaging and physiological data.
• Unsupervised Learning for Structural and Functional Brain Network Construction: Methods employing unsupervised learning to construct and analyze structural and functional brain networks from multimodal data.
• Weakly Supervised Learning for Brain Connectivity Analysis: Innovations in weakly supervised learning techniques for analyzing brain connectivity patterns and networks.
• Foundational Models for Classification and Predictive Modeling: Development and application of foundational models for classification and predictive modeling tasks in neuroscience.
• Multimodal Brain Image Visualization with Advanced Learning Techniques: Techniques for visualizing multimodal brain images using advanced learning and visualization methods to aid in data interpretation.
• Ethical Implications: Ethical considerations in the creation, use, and implications of foundational models in neuroscience research and applications.