Theoretical Neuroscience Podcast On the philosophy of simplification in computational neuroscience - with Mazviita Chirimuuta and Terrence Sejnowski - #29
Jun 21, 2025
Terrence Sejnowski, a pioneer in computational neuroscience, discusses simplification in modeling the brain with philosopher Mazviita Chirimuuta. They delve into the delicate balance between oversimplification and complexity, emphasizing the implications for neuroscience models. The conversation touches on how varied neural models reflect brain function, the challenges of predicting behavior, and the philosophical underpinnings of simplification. Their insights reveal a fascinating interplay between rigorous scientific approaches and the abstraction necessary for understanding brain dynamics.
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Perspectives And Simplification
- Neuroscience uses both third-person and first-person perspectives, but combining them in the same argument is a category error.
- Simplification is necessary for modeling neural systems, but the right level of simplification is unclear.
Brain Does Not Model Reality Fully
- The brain's visual system does not create a detailed internal model of the world as once thought.
- Visual perception is selective, integrating only necessary information at any moment through rapid eye movements.
Brain Complexity Enables Flexibility
- The brain's complexity stems from neuron heterogeneity, plasticity, and constant change, not just cell number.
- This complexity enables flexible behavior and intelligence in changing environments.
