3 citations · 5 across the 5 of their papers we have counts for
8 papers
If the brain were so simple: Information-based drivers of intelligence and their interaction through an n-body-inspired framework
Brett J. Kagan, Johnson Zhou, Forough Habibollahi +1
Theories of how the brain processes information and returns intelligent outputs are numerous and often difficult to conclusively test. Here, we consider an approach grounded in inf…
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…
Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
Johnson Zhou, Daniel Tanneberg, Forough Habibollahi +12
Biological neural networks (BNNs) have been established as a powerful and adaptive substrate that offer the potential for incredibly energy and data efficient information processin…
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…
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…