From Closed-Loop Vision to Creative Machines: Generative Models as Tools and Theories of Neural Representation and Creativity
Model-free and Model-based Methods to Control and Interpret Neural Representation
Generative and Predictive AI for Closed-loop Visual Neuroscience
Towards Generative and Predictive AI as Computational Interfaces to the Brain
Dissociation between Visual Neuron Prediction and Control: A Regression-Theoretic Perspective
Parametric neural control identifies the deep encoding models with causal alignment to biological feature tuning
From Renormalization to Generative Universality: A Random Matrix Theory of Diffusion Consistency
What We Learn about Diffusion Models from the Linear Case: An Analytical Lens into Sampling, Learning, Receptive Field and Consistency
Model-Optimized Stimuli for Comparing Brain-Alignment of Generative Models and Encoding Models