There's a big input/output issue here. Which they address, but nonethless which makes the project failure not surprising. The inputs and outputs for a brain/ne

There's a big input/output issue here. Which they address, but nonethless which makes the project failure not surprising. The inputs and outputs for a brain/neural network, e.g. pictures as inputs as recognized object as outputs, are clear. They are not clear for a chip-restricted view where your input binary stream is a post-transformed signal from an Atari joystick, and the output is a pretransformed signal to a display where the pong paddle moves. These complex transformed inputs and outputs at the chip-restricted perspective tremendously reduce the likelihood of making sense of anything. If we know the adaptor functions for the signal to the first layer of neurons in a simple convolutional neural network, for example, and the output layer is indentifiable, it might be easier to track which neurons track which features, etc. But there might be no clear intermediate output layers to a brain, so you might have a look at inputs and outputs wrt various subnetworks to see which is most likely to be the true functional subset corresponding to some definitely-occuring function (another obstacle to define). Or assume a function is occuring and see which neurons classify it under the most circumstances. But with so many connections that sounds like a big rough game to play too. I can see how this project if successful would be another step to creation of the sentient robot overlords but I'm curious as to what people see as the more immediate medical applications would be. Some of the biggest breakthroughs in brain health in the last 20 years have been about getting rid of clots (current best is arguably manual labor) and immune modulation and new biochemical drugs whose effects would require excruciatingly sophisticated and accurate biomolecular connectomic models to model and may be a long time away.