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🎤 Vocab
❗ Unit and Larger Context
Why does V1 have topographically organized edge detectors?
Neurons:
- ions, diffusion and electrical forces ->
- tug-of-war dynamic, detector model
Networks:
- tug-of-war dynamic, detector model
- feedforward and feedback excitation->
- categorization, attractor dynamics. inhibition ->
- competition/regulation
Learning:
- competition/regulation
- categorization, attractor dynamics. inhibition ->
- synaptic efficacy changes based on firing patterns:
- neurons that fire together wire together
- self-organizing and error-driven
Different neurons have different receptive fields:
- In the lab, blue is inhibitory red is excitatory
- Neurons have slightly different receptive fields so that they can pick up on different things
Why does V1 have topographically organized edge detectors?
- Self-organizing learning
- Regularities in world
- Lateral connectivity
✒️ -> Scratch Notes
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🧪-> Example
Why does V1 have topographically organized edge detectors? (It’s okay to not know! What are your initial guesses, thoughts, or questions?)
It has edge detectors in order to extract features from perceived patterns of lightness and darkness in the environment. These features are more useful than directly where things are on our retina, because objects can move around in our field of vision, shrink and grow, and change their pattern of lightness in different lightings. These higher level features can help our visual system to be more robust against these variances.
They could be topographic in order to allow neurons processing similar things to be closer in space, facilitating connections. For example, it could allow a left to right edge to communicate with a right to left edge and discriminate whether something is a thin bar or a surface edge.
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Resources
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Connections
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