How can we understand the neural basis of thought?

Speaker :- Nick Watters
Moderator :- Ted Theodosopoulos

Title: How can we understand the neural basis of thought?

Abstract: Neuroscience is undergoing a technological revolution, a “Moore’s Law” for neural recording that is allowing us to measure the activity of the brain at ever-increasing resolution. However, simply recording neural activity does not tell us how the brain works. To understand how the brain works, we must construct models that connect neural activity to interpretable principles of thought. This modeling becomes increasingly important as we tackle more abstract, cognitive types of thought that arise from the coordinated activity of large populations of neurons. In this talk, I’ll discuss approaches to modeling such large-scale neural activity. I’ll focus primarily on one cognitive domain: Our ability to predict the kinematics of moving objects. We use this ability regularly in daily life, from catching a ball to crossing a busy street. I’ll present neural data recorded from subjects predicting the kinematics of moving objects, introduce a modeling paradigm for interpreting this data, and discuss the implications of the neural latent variables this modeling effort reveals. I’ll conclude by sharing an optimistic outlook on the future of systems neuroscience and speculation about potential implications for artificial intelligence.

Speaker bio: Nick Watters is a postdoctoral associate at MIT studying the neural basis of cognition and motor control in the Jazayeri lab, where he was a PhD student beforehand. Prior to joining MIT, he worked at Google DeepMind as a research engineer, studying unsupervised visual structure-learning and sample-efficient reinforcement learning. Prior to joining DeepMind, he was an undergraduate at Harvard studying math, computer science, and neurobiology.

Moderator: This talk is moderated by Ted Theodosopoulos. Ted is a mathematician who, after working for years in academia and industry, transitioned to teaching at the pre-college level sixteen years ago, the last eight at Nueva, where he teaches math and economics. Ted’s research background is in the area of interacting stochastic systems, with particular applications in biology and economics.