BAI: Notes and slides

01/28/25

Are you ready to create suprahuman intelligence? Do you want to understand how intelligent computations emerge from the neuronal orchestra in the brain? How can we leverage millions of years of evolution to transform engineering systems that perform intelligent computations? How concerned should we be about the singularity? How is information represented and transformed in biological circuits? How can machines learn?

Slides

Suggested reading

02/04/25

We understand the biological and computational underpinnings of our prodigious ability to see better than any other aspect of our brain’s function. Beginning with the seminal work of Hubel and Wiesel, we have discovered many of the biological principles that operate in the primate visual system. And computational models inspired by these principles have enjoyed tremendous success in object recognition and other visual capacities, sometimes outperforming humans. Yet these models also fail in spectacular and occasionally embarrassing ways, leading to the overwhelming question: What are they missing? We will explore the history of biological and machine vision, learn how they productively interact in present-day vision science and discuss whether Mother Nature might still have a few tricks up her sleeve that could spur the development of improved machine vision systems in the future.

Slides

Suggested reading

02/11/25

This class will present a theory of manifold separation in high-dimensional spaces, few-shot learning in visual representations, and aspects of visual reasoning.

Slides

Suggested reading

02/18/205

Abstract coming soon

Slides

Suggested reading

03/25/25

Abstract coming soon

Slides

Suggested reading

04/01/25

Dr. Rajan will discuss the basic design elements, or “building blocks,” of neural network models, highlighting the role of recurrent connections, linear and non-linear activity, and the types of time-varying activity (“dynamics”) produced by RNNs, including input-driven versus spontaneous dynamics. The main goal of this lecture is to explore both the tractability and computational power of RNN models, to appreciate why they have become a crucial part of the neuroscientific arsenal, and to understand the insights that can be gained by “training RNNs to do something” in a manner consistent with experimental data from the biological brain. We will also review how RNNs have been applied in neuroscience to leverage existing experimental data, infer mechanisms inaccessible from measurements alone, and make predictions that guide experimental design.

Slides

Suggested reading

04/08/25

The aim of both biological and artificial intelligence is to find efficient solutions to hard problems. Ideal observer models use this parallel to formulate theories of how our nervous system achieves certain tasks based on how they have been approached by artificial intelligence research. A basic tenant of ideal observer models is that the world we inhabit is noisy and ambiguous. The ideal way to deal with the arising uncertainty is by Bayesian decision theory. I will introduce Bayesian decision theory and how it has been used to inquire about the cognitive and neural processes that underlie humans and animal behavior. Specific focus will be put on the study of perceptual decisions and the relation between the speed and accuracy of such decisions.

Slides

Suggested reading

04/15/25

TBD

Slides

Suggested reading