Konrad Kording

Challenges in traditional neuroscience methods, focus on reverse engineering C.elegans, parallels between transistors and neurons, pitfalls of statistical analysis in biology, mechanistic understanding in neuroscience, neural complexity of C.elegans, error recalibration in neural modeling, activation functions in machine learning, optimization challenges in bio-physical models, variability in neural networks

In this episode, we sit down with Konrad Kording, a neuroscientist and professor who delves into the intersection of brains, AI, and causality. We explore the evolution of machine learning and delve into a deep learning-based view of the brain. Konrad shares his experiences coding neural networks 20 years ago and how he eventually arrived at the idea of gradient descent. We also discuss deconstraints in both biology and machine learning and how these systems allow for faster progress in AI. Finally, we reflect on the current state of LLMs and explore the potential evolution of physical embodiment in the next few years. Tune in for an insightful conversation with one of the leading voices in neuroscience and AI. Don't forget to support the podcast on Patreon: https://www.patreon.com/rhyslindmark Full show notes and resources at: https://www.roote.co/episodes

Carson Stringer, Janelia group leader focused on large-scale neural data and dimensionality reduction. Chethan Pandarinath, professor developing latent dynamical models for neural decoding. Konrad Kording, UPenn professor in computational neuroscience and education. Vikash Gilja, UCSD professor working on neural prostheses and translational neuroengineering. They dive into dimensionality reduction, PCA, noise and reliability, random projections, LFADS, and scaling recordings.