1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
Simulating Biological Intelligence: Active Inference with Experiment-Informed Generative Model
Aswin Paul, Moein Khajehnejad, Forough Habibollahi +2
With recent and rapid advancements in artificial intelligence (AI), understanding the foundation of purposeful behaviour in autonomous agents is crucial for developing safe and eff…
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…