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Apr 13 '17 at 12:56 history edited CommunityBot
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Mar 8 '16 at 0:39 comment added Seanny123 It's easy to imagine changing the receptive fields of a spiking version of a convolutional neural net depending on the task so that various structures can be extracted. However, implementing it is non-trivial.
Mar 8 '16 at 0:37 comment added Seanny123 The example I gave only covered the case of Raven Progressive Matrice, but hypothetically any structure is possible given a high enough dimensional vector. This raises two sub-questions: 1. What is the limit of the dimensions that the brain can represent? 2. How can we define this in a general manner in the vision system. For the first question, this is discusses somewhere in "How to Build a Brain", but it was a pretty large number, like 4096 if I'm not mistaken. For the second question, it depends on the vision architecture.
Mar 7 '16 at 21:34 comment added danijar Interesting mechanic. Would that also account for multiple types of objects and their positions and counts (assuming the brain can do that which is not clear)?
Mar 7 '16 at 1:21 history answered Seanny123 CC BY-SA 3.0