Ian, I talked about the problem of using the CEPT at the beginning of the work . We discussed this with Matthew. The problem consists in mixing the structural and semantic data. This approach degrades the recognition and extraction of semantic knowledge.
Of course, the retina makes the image description by the structural features and linear transformations. Invariant pattern recognition can be performed in the framework of these two approaches. I did invariant pattern recognition in neural networks using linear transformation in 1982. It was my graduation work. Thanks for the links. Regards, Ivan ON:Date: Tue, 22 Oct 2013 16:56:37 -0700 Francisco, CEPT -> CLA is a very odd transition. I'm pretty sure the SP won't get you anything useful. CEPT Isn't a "Retina" it's the World Unlike CEPT, the actual retina is not an organized map. It has many copies of the same small number of feature detectors. Each feature detector it has is distributed (more or less) evenly across its surface. The world, on the other hand, has a coordinate system. Height, width, depth, etc. Closeness in the real world are defined by these dimensions. CEPT provides another definition for "closeness" in the context of words. It is the world of words. An object in the CEPT world though is a very very strange thing indeed. It never moves, and never changes, and because you don't recompute the CEPT world again and again, you never get a different 'view' of the world. It's as if the only thing you could see in your entire world was an apple, and you never saw it from any other angle, and it never moved or changed. If the CEPT map is the world, and each word is an object in that world, what would a retina be? The Retina The retina exists as a set of predefined feature detectors evenly distributed across its surface. Red, green, blue, and light. The ganglia then add an initial processing step to get you a second set of evenly distributed feature detectors for light/dark transitions and all the rest. The critical assumption here is that there are a set of common features that could exist anywhere in the 2D projection of the world. The reason for that, of course, is because our view changes over time, and objects move in the real world. The retina evolved as a moving observation platform for a dynamic world. The challenge of the retina and the cortical hierarchy is then to build *invariant* representations of the world. Because the world is noisy and dynamic you need all this circuitry to tease out the repeating patterns and common causes. But the CEPT world isn't like that. It's totally invariant to begin with. No single object ever moves or is viewed from a different direction. A given word will always directly map to the same set of bits. What *could* the SP pick up? What you're really hoping for is not that the SP/CLA will give you smaller and smaller granularity, but that it will discover features that are common across words. Another way to say this is, you hope that your self-organizing map has accidentally captured dimensions other than those used to calculate the centroids and distance metrics. If you already have metrics along those other axis though, it doesn't make a lot of sense to use the SP to try to discover them. You can calculate them directly. Ultimately you want to know how a raven is like a writing desk. But to know that "Poe wrote on both." you have to be able to perceive the world from a very odd angle. The TP On the other hand the TP is a straight-forward sequence learner that takes CEPTWorld like representations as inputs. You should easily get sentence generation, hopefully with a fluent-aphasia like character. The sentences should be nonsense but not garbage. Of course you could also do this with n-grams. Ian
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