Building AI simulations of the human brain | Dan Yamins

This week on the show: Are we ready to create digital models of the human brain? 

Last month, Stanford researcher Andreas Tolias and colleagues created a "digital twin" of the mouse visual cortex. The researchers used the same foundation model approach that powers ChatGPT, but instead of training the model on text, the team trained in on brain activity recorded while mice watched action movies. The result? A digital model that can predict how neurons would respond to entirely new visual inputs. 

This landmark study is a preview of the unprecedented research possibilities made possible by foundation models of the brain—models which replicate the fundamental algorithms of brain activity, but can be studied with complete control and replicated across hundreds of laboratories.

But it raises a profound question: Are we ready to create digital models of the human brain? 

This week we talk with Wu Tsai Neuro Faculty Scholar Dan Yamins, who has been exploring just this question with a broad range of Stanford colleagues and collaborators. We talk about what such human brain simulations might look like, how they would work, and what they might teach us about the fundamental algorithms of perception and cognition.

Learn more

AI models of the brain could serve as 'digital twins' in research (Stanford Medicine, 2025)

An Advance in Brain Research That Was Once Considered Impossible (New York Times, 2025)

The co-evolution of neuroscience and AI (Wu Tsai Neuro, 2024)

Neuroscientists use AI to simulate how the brain makes sense of the visual world (Wu Tsai Neuro, 2024)

How Artificial Neural Networks Help Us Understand Neural Networks in the Human Brain (Stanford Institute for Human-Centered AI (HAI), 2021)

Related research

A Task-Optimized Neural Network Replicates Human Auditory Behavior... (PNAS, 2014)

Vector-based navigation using grid-like representations in artificial agents (Nature, 2018)

The neural architecture of language: Integrative modeling converges on predictive processing (PNAS, 2021)

Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations... (Neuron, 2021) 

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