4 citations · 8 across the 9 of their papers we have counts for
9 papers
A Quantifiable Information-Processing Hierarchy Provides a Necessary Condition for Detecting Agency
Brett J. Kagan, Valentina Baccetti, Brian D. Earp +3
As intelligent systems are developed across diverse substrates - from machine learning models and neuromorphic hardware to in vitro neural cultures - understanding what gives a sys…
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…
On Predictive planning and counterfactual learning in active inference
Aswin Paul, Takuya Isomura, Adeel Razi
Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general th…