Theoretical Neuroscience Podcast On the population code in visual cortex - with Kenneth Harris - #26
Mar 29, 2025
Kenneth Harris, a Professor at University College London, discusses the groundbreaking study he co-authored on population codes in the visual cortex. He explores how modern techniques allow us to analyze thousands of neurons simultaneously, revealing unique ways groups of neurons encode information. The conversation dives into the significance of efficiency and sparsity in neural coding and the impact of AI tools on neuroscience data analysis. Harris also addresses the integration of mathematical methods in decoding complex neural responses and the role of distinct neuron types in visual processing.
AI Snips
Chapters
Transcript
Episode notes
Efficient Coding Paradox
- Efficient coding seeks maximum bits per spike, minimizing correlations between neurons.
- However, pure efficient coding is flawed in the cortex due to observed correlations, exemplified by a binary encoding analogy.
Neural Coding Dimensions
- Neural coding dimensionality can be defined by ambient, manifold, or planar dimensions.
- Planar dimension matters most as downstream neurons use weighted sums, impacting how information gets processed.
Low Dimensionality Assumption
- Harris's lab initially hypothesized a low planar dimension due to previous studies.
- However, Gowon Ganguly showed that low planar dimension was inevitable with simple stimuli, not revealing anything about the neural code itself.