New episode with Dr. Konrad Kording, professor of bioengineering and neuroscience at the University of Pennsylvania and co-director of CIFAR’s Learning in Machines & Brains program. Konrad works at the intersection of causality, machine learning, and neuroscience, building rigorous methods for causal reasoning when experiments aren’t possible — and challenging how researchers interpret neural data and build AI.
Konrad argues the most promising path to understanding how the brain works is to read the brain’s wiring directly, down to the molecular detail of each connection, and to build compilers and simulations to understand the brain’s computation directly.
In this episode we go deep into how neurons work, how neurons wire together, and how organic and artificial neural networks differ. We discuss why organic neurons are doing much more; how a model of a single organic neuron can solve MNIST — computing more like a 3-layer artificial neural network; how the brain might learn by solving credit assignment with only local signals; how to approximate backprop without a global algorithm; why AI and humans are intelligent along different dimensions; why Konrad isn’t very worried about AI replacing us; economic models of intelligence and physical work; and much more.
Konrad is a brilliant, contrarian thinker who explains complex concepts very intuitively. It is a solid computational neuroscience primer. I hope you enjoy this conversation as much as I did!
Other links to this episode and references below.
Topics covered:
00:00:00 Introduction
00:01:01 How organic neurons work
00:24:13 How the brain learns: circuits and credit assignment
00:45:29 Recording the brain
00:52:47 Why simulating brains is hard
01:05:00 A new approach: connectomes and compilers
01:21:00 Why simulate brains?
01:29:50 How AI and human intelligence differ
01:41:04 Evolution, intelligence and AI risk
01:52:42 Robotics, causality, and the roots of intelligence
02:05:53 AI for science and scientific rigor
02:13:05 The economics of intelligence
02:27:50 A hopeful future
Podcast Links
Links From the Podcast Episode
Guest + Organizations
KordingLab: kordinglab.com
KordingLab on GitHub: https://github.com/KordingLab
KordingLab on X: https://x.com/kordinglab
Papers directly from Konrad Kording’s lab:
Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees (2020)
(Artificial) Intelligence Saturation and the Future of Work (2025)
Compiling Molecular Ultrastructure into Neural Dynamics (2026)
Referenced external papers:
Millisecond-timescale, genetically targeted optical control of neural activity (2005)
Dopamine Reward Prediction Error Coding — Wolfram Schultz (2016)
Single Cortical Neurons as Deep Artificial Neural Networks — Beniaguev, Idan Segev & London (2021)
Neural Signal Propagation Atlas of C. elegans — Randi, Sharma, Dvali & Leifer (Andrew Leifer’s lab) (2023)
Books & Media:
Juan & Protocol Labs
