Theoretical Neuroscience Podcast On reverse engineering of the roundworm C.elegans - with Konrad Kording - #8
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Mar 2, 2024 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
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Limitations of Current Neuroscience Methods
- Analyzing large-scale neural recordings with statistical methods like PCA may not reveal how the brain works.
- A study applying neuroscience techniques to a microprocessor showed this, raising concerns about the current approach.
Microprocessor Study
- Konrad Kording and Eric Jonas applied neuroscience techniques to a microprocessor, a system they fully understood.
- Their findings revealed that these techniques didn't offer true insights into the microprocessor’s function.
Mechanistic Understanding
- Neuroscientists should strive for a mechanistic understanding, including causal effects and underlying mechanisms.
- However, current recording techniques limit our ability to establish causality in complex systems like the human brain.