The objective of the Visual Inference Lab is to understand the brain information processing that enables visual perception, object recognition, and scene understanding. Vision is of interest in its own right, but also provides a model for understanding, more generally, how the brain computes and how it might perform probabilistic inference through parallel and recurrent computations.
Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. We seek to develop neural network models that can meet real-world computational challenges faced by biological visual systems and that can also explain detailed patterns of brain and behavioral responses.
Computational neuroscience is entering a new era, where big models meet big data. We develop statistical inference and visualization techniques that help us connect theory and experiment, enabling us, for example, to adjudicate among many candidate neural network models using brain-activity measurements acquired with functional imaging and electrophysiological recordings in animals and humans.
We develop interactive visual tasks that enable us to carve up visual cognition into measurable components. Tasks should lend themselves to experiments with humans, monkeys, and computational models. In humans, we perform psychophysical experiments, functional MRI, and magnetoencephalography. We also acquire and analyze neuronal array recordings in nonhuman primates. This data enables us to adjudicate among neural network models that implement candidate brain-computational theories.