4 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
Brain-MGF: Multimodal Graph Fusion Network for EEG-fMRI Brain Connectivity Analysis Under Psilocybin
Sin-Yee Yap, Fuad Noman, Junn Yong Loo +6
Psychedelics, such as psilocybin, reorganise large-scale brain connectivity, yet how these changes are reflected across electrophysiological (electroencephalogram, EEG) and haemody…
Hi-DREAM: Brain-Inspired Hierarchical Diffusion for fMRI-to-Image Reconstruction via ROI Encoder and VisuAl Mapping
Guowei Zhang, Yun Zhao, Kai Sun +4
Reconstructing natural images from fMRI requires bridging neural activity with both the structural and semantic representations used by modern generative models. Existing diffusion…
A memory-based active inference model of DishBrain-like adaptive behaviour
Aswin Paul, Moein Khajehnejad, Forough Habibollahi +2
Recent and rapid advances in artificial intelligence (AI) make it increasingly important to understand the foundations of adaptive behaviour in autonomous agents, especially for bu…
BrainSymphony: A parameter-efficient multimodal foundation model for brain dynamics with limited data
Moein Khajehnejad, Forough Habibollahi, Devon Stoliker +1
Foundation models are transforming neuroscience but are often prohibitively large, data-hungry, and difficult to deploy. Here, we introduce BrainSymphony, a lightweight and paramet…
A Principled Bayesian Framework for Training Binary and Spiking Neural Networks
James A. Walker, Moein Khajehnejad, Adeel Razi
We propose a Bayesian framework for training binary and spiking neural networks that achieves state-of-the-art performance without normalisation layers. Unlike commonly used surrog…