5. The Midget and Parasol systems

MIT 9.04 Sensory Systems, Fall 2013
View the complete course: ocw.mit.edu/9-0...
Instructor: Peter H. Schiller
This lecture describes the Midget and Parasol channels. It includes a discussion of how lesions of the LGN affect contrast sensitivity, brightness perception, pattern and texture perception, depth, motion and flicker detection.
License: Creative Commons BY-NC-SA
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[@glassfabrikat]

Very good lecture

[@yanag3246]

Thank you for the wonderful course!!

[@sergiosanchezpadilla6941]

Why did we evolve the LGN as a separate anatomical structure from the V1, V2, V3, etc. system? Why didn't nature just lump it all together? (as opposed to one structure almost at the edge of the FRONT region of the brain and the other one at the edge of the BACK region of the brain) I bet there must be a very good reason for it, but I just have never heard why.

[@smalljbug]

maybe it's just because evolution builds in steps? He mentioned encephalization earlier, maybe the LGN was independently useful before so much functionality was moved to the neocortex. Before V1 etc. were well developed, LGN might have been the only (or the main) locus for the job it does, and now it's easier to just keep it than to get rid of it and rewire the whole thing. Also, I haven't heard much detail about what the LGN does other than just 'relay', which seems like a useless job, so I'm missing some insight that's probably very crucial. I would imagine that it does some low-level image pre-processing that's useful for the neocortex as well as older regions in the lower brain

[@rbain16]

Paul CIsek's work might have some answers to your question. He looks at the evolution of brains.

[@mattaku9430]

Oh, i do machine learning and i tell you why, there's a perfect reason: You can't rely on high dimensional data, it's very dangerous, current CNN models do that and i'll tell you that a couple pixels in a special pattern can confuse them, it's called adversarial attack. Now we take human vision, parasol cells don't rely on colour, they rely on intensity, which have way less dimensions, also it doesn't rely on high frequency spatial features as much, but on low frequency features to reliably detect edges.