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Theory of neural networks as an explanation for mind states.:
Author	Item	Excerpt	Meta data
Pinker, Stephen   Books on Amazon	Connectionism	I 128 ff - 145 Neural Networks/Pinker: Learning/Problem: Incorrect reinforcement with "xor" (Shefferstrich) - Solution: interpose internal representation - ...+... - --- I 142 Rumelhart: return all errors - "hidden levels": several statements that can be true or wrong can be assembled into a complex logical function, the values ​​then vary continuously - System can place the correct emphasis itself if input and output are given - as long as similar inputs lead to similar outputs, no additional training is required ->Homunculi. --- I 144f Connectionism/Rumelhart: mind large neural network - rats have only fewer nets - PinkerVsConnectionism: networks alone are not sufficient for handling symbols - the networks have to be structured in programs - even past tense overstretches a network - precursor: "association of ideas": Locke/Hume/Berkeley/Hartley/Mill - 1) contiguity (context): frequently experienced ideas are associated in the mind - 2) similarity: similar ideas activate each other. --- I 146 Computer variant: is a statistical calculations with multiple levels. --- I 147 VsConnectionism: units with the same representations are indistinguishable - individual should not be construed as the smallest subclass. --- I 151 Cannot explain compositionality of representation. --- I 158 ~ Recursion/Recursive/Neural Networks/Memory/Pinker: recursion solution for the problem of an infinite number of possible thoughts: Separation of short/long-term memory - the whole sentence is not comprehended at once, but words are processed individually in loops. --- I 159 Networks themselves have to been as recursive processor: for thoughts to be well-formed. --- I 166 Neural Networks/Pinker: the networks do not reach down to the rules - they only interpolate between examples that have been put in.	Pi I St. Pinker Wie das Denken im Kopf entsteht München 1998

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Ed. Martin Schulz, access date 2017-05-30