Deep Belief Networks Toolkit
What is most amazing about the mammalian visual sytem is its invariance property — that it can recognize objects despite wide amounts of variation. Learning these invariant representations in the correct way is one of challenges in vision and neuroscience research.
When I joined the Redwood Institute (RNI) back in 2003, I was still a novice in neuroscience literature. I had implemented a simple version of HMAX/Neocognitron and also re-implemented Rajesh Rao’s hierarchical Kalman filter paper by then, but still did not know enough about other vision research. Having no knowledge can be an advantage sometimes — it enables original thinking. One day I came up with a thought experiment on learning invariant representations.
This thought experiment is given in more detail in my thesis, but I will parahrase it here. A cat walking towards its bowl of milk has to know that it is the same bowl of milk even though the images on cat’s retina vary dramatically from instant to instant. Nobody has supervised the cat to teach it that all those images are actually the same milk bowl — it had to learn it in an unsupervised fashion. Thinking more about it, I concluded that there is no way the cat could do it without using temporal proximity.
Source: Mind Matter
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