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Cerebrum: Simulate and infer synaptic connectivity in large-scale brain networks
Advancements in computational neuroscience are continually reshaping our understanding of the brain’s intricate networks. A key challenge in this field is deciphering the dynamic connectivity of neural networks, which is essential for both fundamental neuroscience and the development of clinical applications.
To address this, we introduce Cerebrum, a novel framework that combines biologically inspired neuron models with cutting-edge machine learning techniques to simulate and infer synaptic connectivity in large-scale brain networks. Traditional approaches to studying brain networks often rely on graph theoretical methods that provide valuable insights into the static topological properties of neural connections. Cerebrum bridges this gap by integrating the Hodgkin-Huxley(HH) neuron model, known for its biological realism, with Graph Neural Networks (GNNs), which excel at learning complex patterns in graph-structured data.
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