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Theoretical Neuroscience Podcast

On the neural code - with Arvind Kumar - #3

Nov 4, 2023
Arvind Kumar, a guest who has thought about coding questions throughout his career, discusses the neural code and its interpretations. Topics include neural representation of visual features, decoding motor systems with spike count codes and tuning curves, population coding and correlation, neural code and connectivity, diversity in tuning curves and encoding information, comparing visual neurons and entorhinal cortex neurons, catastrophic errors in AI and grid cells, collaboration to solve a problem involving time and stimuli, adversarial attacks and grid cells' representation of navigation, and efficient coding and sparse coding in the brain.
01:25:46

Episode guests

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Quick takeaways

  • Rate coding uses the firing rate of neurons to convey information, assuming spikes are independent and the total count of spikes matters more than precise timing.
  • Temporal coding involves encoding information in the precise timing and sequence of spikes, including correlations between spike intervals and alignment to population oscillations.

Deep dives

Rate coding as a neuronal representation

Rate coding refers to the representation of information through the firing rate of neurons. In rate coding, the number of spikes or action potentials generated by neurons in a given time interval is used to convey information. This coding method assumes that spikes are independent and that the total count of spikes is what matters, rather than the precise timing of individual spikes. Rate coding is commonly observed in various brain regions, such as the visual cortex and hippocampus, where neurons exhibit single peak tuning curves that encode specific features or stimuli.

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