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Researchers have developed a method using artificial intelligence to predict neuronal activity in living brains without direct measurements. This approach leverages the connectome, a comprehensive map of neural connections, to simulate and understand how information is processed in neuronal networks. The study, published in Nature, specifically focuses on the fruit fly's visual system, demonstrating a significant advancement in neuroscience research.
The new model predicts the activity of 64 different cell types involved in motion detection, optimizing unknown parameters through deep learning techniques. This breakthrough allows for the identification of previously unknown cells related to motion detection, providing a wealth of data for future studies. The findings could revolutionize how neuroscientists formulate and test hypotheses regarding neural circuit functions.
• AI predicts neuronal activity without direct measurements in living brains.
• Deep learning optimizes parameters for simulating neural activity in fruit flies.
The study utilizes the connectome to inform predictions about neuronal activity in the fruit fly's visual system.
This technique is employed to optimize unknown parameters in the neuronal activity predictions.
The research focuses on predicting this activity based on the connectome data.
It plays a crucial role in advancing biomedical research, including studies on neuronal activity.
Researchers from this institution are involved in the study of neuronal networks and their functions.
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