Speaker
Description
In computational neuroscience, understanding the multifaceted dynamics within neural networks remains a pressing challenge, particularly in the context of the brain's staggering complexity, comprising approximately 100 billion neurons. While traditional models have focused on local coupling and function, there is a growing consensus that a network-centric approach is indispensable for a comprehensive understanding of brain function. Against this backdrop, this study introduces a groundbreaking approach that leverages the mathematical principles of quantum mechanics to scrutinize neuroimaging data.
Our quantum-inspired model offers a novel framework for investigating network dynamics, complementing, and extending existing network science methodologies. It provides a nuanced perspective on neural information flow, enabling a deeper understanding of brain network topology. A significant feature of this model is its ability to integrate neuro-energetics, thereby enriching our understanding of metabolic processes within these intricate networks. Particularly salient is the model's utility in examining the temporal dynamics of resting-state networks, which are key to understanding the brain's baseline functional connectivity. Using predefined neural networks as a template, our model dynamically tracks network behaviour, aligning with the focus on neural network inference from imaging data.
Significantly, this approach uses source-localized electroencephalography (EEG) data, allowing for a broader application in both clinical and research settings. A significant expansion of this research is the incorporation of Physics-Informed Neural Networks (PINNs) to extend the capabilities of the quantum-mechanical framework. PINNs serve as a computational bridge, facilitating the integration of physical laws into the modelling process, thus enhancing the model's predictive accuracy and interpretability.
| Keyword-1 | Medical Physics |
|---|---|
| Keyword-2 | EEG |
