Mackenzie Weygandt Mathis
Across every scale at which we study the brain, from folded proteins and single neurons to cortical populations and the moving body, artificial intelligence (AI) has shifted from a bespoke tool into a driver of measurement and, increasingly, a generative engine for hypotheses. Here, I review recent progress (and open challenges) in applying AI for neuroscience along this scale axis: structure prediction for proteins, simulation-based inference for biophysical neurons, latent and dynamical models for neural populations, task-trained networks as minimal models of circuit computation, computer vision for animal behavior, neuromusculoskeletal modeling for biomechanics, and the multimodal, agentic systems now promising to automate discovery itself.