Title : Ai-driven models of mind: Investigating cognition at the neural level
Abstract:
This research investigates how Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models can enhance the collection of brain data, feature extraction, and classification processes. Our innovative deep learning techniques generate high-quality synthetic neuroimages that accurately depict anatomical structures. The application of adversarial training, Wasserstein optimization, and visual loss functions mitigates training instabilities and enhances image quality. Image transformation tools facilitate the organization of latent spaces post-generation, thereby accelerating neuroimaging feature extraction and classification. Our findings demonstrate that the combined methodology achieves a 94.5% accuracy in fMRI data classification and yields more stable, realistic, and noise-resistant structures. The efficiency of latent space representation, vital for feature extraction, reaches 89.1%, substantially surpassing individual models. This approach expedites convergence, requiring 48.7 hours of training and 75,000 trials. The resultant data is more comprehensible and socially acceptable, with positive implications. This integrated platform is efficient and easy to extend, adheres to stringent privacy and ethical standards, and is well-suited for clinical applications. The technological advancements may improve neuroimaging diagnostics and open new pathways for research in generative AI within medical imaging.
Keywords- Adversarial networks, classification, diffusion models, ethical compliance, feature extraction, generative models, interpretability, latent space, neuroimaging, variational autoencoders.
