Our paper is out in Nature Communications Biology: https://www.nature.com/articles/s42003-018-0110-y!
We had a cool dataset from 100 patients who looked at some pictures while wearing intracranial electrodes inside of their brain.
Image may be NSFW.
Clik here to view.
With these electrodes we’ve recorded how human brain reacts to images like the one the right. Image may be NSFW.
Clik here to view.
Then we showed the same images to a Deep Convolutional Neural Network that was trained to recognize objects on the images and, same as with humans, recorded how the artificial brain reacted to those images.
And sure enough we went and compared those activations. We have confirmed the similarities between the hierarchies of biological and artificial systems of vision, identified what kind of brain activity matches this hierarchy the closest and did a couple of other interesting observations along the way. Image may be NSFW.
Clik here to view.
See the paper for the details: https://www.nature.com/articles/s42003-018-0110-y
Code and (part of) data are public: https://github.com/kuz/Human-Intracranial-Recordings-and-DCNN-to-Compare-Biological-and-Artificial-Mechanisms-of-Vision
We had a cool dataset from 100 patients who looked at some pictures while wearing intracranial electrodes inside of their brain.
Image may be NSFW.
Clik here to view.
With these electrodes we’ve recorded how human brain reacts to images like the one the right. Image may be NSFW.
Clik here to view.
Then we showed the same images to a Deep Convolutional Neural Network that was trained to recognize objects on the images and, same as with humans, recorded how the artificial brain reacted to those images.
And sure enough we went and compared those activations. We have confirmed the similarities between the hierarchies of biological and artificial systems of vision, identified what kind of brain activity matches this hierarchy the closest and did a couple of other interesting observations along the way. Image may be NSFW.
Clik here to view.
See the paper for the details: https://www.nature.com/articles/s42003-018-0110-yCode and (part of) data are public: https://github.com/kuz/Human-Intracranial-Recordings-and-DCNN-to-Compare-Biological-and-Artificial-Mechanisms-of-Vision
