In this enlightening episode, I speak with Professor Konrad Kording, a leading computational neuroscientist, data scientist, and Penn Integrates Knowledge (PIK) Professor of Neuroscience at the University of Pennsylvania. His research bridges neuroscience, AI, machine learning, and causal inference, tackling the challenge of uncovering cause-and-effect relationships when controlled experiments aren't possible.

We explore why proving causality is uniquely difficult and how quasi-experiments help control confounding variables. Professor Kording breaks down scientific pitfalls like p-hacking, deep insights from his microprocessor paper, and how biological brains solve the credit assignment problem compared to AI. Finally, he shares the hurdles of simulating the C. elegans nervous system and how initiatives like Neuromatch and the Community for Rigor are helping train the next generation of scientists to promote more rigorous research.