About This Episode
Today we’re joined by Kanaka Rajan, an assistant professor at the Icahn School of Medicine at Mt Sinai. Kanaka, who is a recent recipient of the NSF Career Award, bridges the gap between the worlds of biology and artificial intelligence with her work in computer science. In our conversation we explore how she builds “lego models” of the brain that mimic biological brain functions, then reverse engineers those models to answer the question “do these follow the same operating principles that the biological brain uses?”
We also discuss the relationship between memory and dynamically evolving system states, how close we are to understanding how memory actually works, how she uses RNNs for modeling these processes, and what training and data collection looks like. Finally, we touch on her use of curriculum learning (where the task you want a system to learn increases in complexity slowly), and of course, we look ahead at future directions for Kanaka’s research.
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- Paper: Inferring brain-wide interactions using data-constrained recurrent neural network models
- Paper: Efficient and robust multi-task learning in the brain with modular task primitives
- Paper: Rethinking brain-wide interactions through multi-region “network of networks” models
- Paper: Neuronal dynamics regulating brain and behavioral state transitions
- Webinar: How Brain Circuits Function in Health and Disease: Understanding Brain-wide Current Flow
- Article: 566: Dr. Kanaka Rajan: Creating Computational Models to Determine How the Brain Accomplishes Complex Tasks
- Article: Tracking Information Across the Brain