Neural Networks XOR Example

Nicole Rust on transformations from IT to perirhinal cortex
Figure 3: (a) Analogue XOR

Why hello there, internet! I am Patrick Mineault, grad student at the Pack lab, long time blogger over at xcorr.net and first time contributor here.

I was recently at the Cold Spring Harbour Computational Vision summer couse. Two wonderful science-filled weeks with some very smart people; I’ve discussed a lot of the course content already on my own blog.

Nicole Rust delivered a lecture on neural transformations from IT to perirhinal, which I think raised some interesting questions. This follows up on her work on selectivity, invariance, and sparseness transformations that she did while in Jim DiCarlo’s lab.

The setup is the following: the subject is asked to perform a delayed match-to-sample task. There’s 4 possible objects, call them A, B, C and D, so all in all there are 16 possible configurations of sample and probe. Computational work has posited that there should be bingo-type neurons somewhere in the MTL that respond to matches, that is, respond only in the late period to AA, BB, CC and DD conditions. You wouldn’t expect to find such bingo type neurons in IT.

You can quantify the mean spike rate across conditions and project it out of the 16-dimensional spike count vector of a neuron. Once you’ve done that, you can quantify bingoness by computing the inner project of the bingo vector with the spike vector and then project it out. A similar process can be done for the visual dimensions (of which there are only three since you’ve already removed the mean), and so on for the rest of the dimensions.

Source: visneuro

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Stopping cold: scientists turn off the ability to feel cold  — Medical Xpress
(Medical Xpress)—USC neuroscientists have isolated chills at a cellular level, identifying the sensory network of neurons in the skin that relays the sensation of cold. David McKemy, associate professor of neurobiology ... In prior work, McKemy ...

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