26 citations · 34 across the 11 of their papers we have counts for
13 papers
PCDT: A Predictive Cognitive Digital Twin Framework for Intelligent and Autonomous 6G Network Ecosystems
John Sengendo, Andreas Kassler, Fabrizio Granelli
Future 6G networks are expected to operate as intelligent and autonomous ecosystems where monitoring, prediction, and control are integrated into continuous self-optimization loops…
From Neurons to Conversation: Speech Brain-Computer Interfaces
Moein Khajehnejad, Forough Habibollahi, Tommaso Boccato +4
Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative intent into external outputs such…
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…
Graph-Based Representation Learning of Neuronal Dynamics and Behavior
Moein Khajehnejad, Forough Habibollahi, Ahmad Khajehnejad +3
Understanding how neuronal networks reorganize in response to external stimuli and give rise to behavior is a central challenge in neuroscience and artificial intelligence. However…
Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld
Moein Khajehnejad, Forough Habibollahi, Aswin Paul +2
How do biological systems and machine learning algorithms compare in the number of samples required to show significant improvements in completing a task? We compared the learning…
CrossWalk: Fairness-enhanced Node Representation Learning
Ahmad Khajehnejad, Moein Khajehnejad, Mahmoudreza Babaei +3
The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. Much recent work has focused on devel…
Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
Moein Khajehnejad, Ahmad Asgharian Rezaei, Mahmoudreza Babaei +3
Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of ind…
Optimal Decision Making Under Strategic Behavior
Stratis Tsirtsis, Behzad Tabibian, Moein Khajehnejad +3
We are witnessing an increasing use of data-driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure o…
Navigation by anomalous random walks on complex networks
Tongfeng Weng, Jie Zhang, Moein Khajehnejad +3
Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, tradit…