THE “HOLY GRAIL” OF NEUROSCIENCE? RESEARCHERS CREATE STUNNINGLY ACCURATE DIGITAL TWIN OF THE BRAIN
In a breakthrough that could revolutionize neuroscience, researchers have harnessed the power of artificial intelligence to create a highly accurate “digital twin” of the mouse brain. This advanced AI model can predict how neurons respond to entirely new visual stimuli—something no previous model has accomplished with such precision.
Recently published in Nature
, the study—led by scientists from Baylor College of Medicine, Stanford University, and the Allen Institute for Brain Science—introduces a sophisticated artificial neural network called a “foundation model.”
The model replicates brain activity and mirrors the intricate structural details of neural circuits, offering a powerful new tool for exploring the brain’s inner workings.
Much like how ChatGPT and other large language models transformed natural language processing, this brain model could reshape how we study perception, behavior, or even consciousness.
“If you build a model of the brain and it’s very accurate, that means you can do a lot more experiments,” Stanford professor of ophthalmology and senior author of the study, Dr. Andreas Tolias, explained in a press release. “We’re trying to open the black box, so to speak, to understand the brain at the level of individual neurons or populations of neurons and how they work together to encode information.”
Remarkably, the model didn’t stop at predicting how neurons would fire. It also inferred their physical characteristics—like where they sit in the brain and what kind of neuron they are.
In another major leap for neuroscience, researchers with the MICrONS project recently unveiled the most detailed functional map of the brain to date, offering unprecedented insight into how neurons connect, interact, and communicate. This feat was once considered “impossible.”
Using data from the MICrONS project, researchers applied their model to more than 70,000 neurons. The model accurately predicted anatomical cell types, dendritic structures, and even synaptic connectivity patterns despite never having been trained on anatomical data.
This suggests that the “functional barcodes” generated by the model—essentially, how a neuron processes visual information—can be used as fingerprints to reveal a cell’s type and structure.
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