/keuh nek"sheuh niz'euhm/, n. Psychol.
the theory that all mental processes can be described as the operation of inherited or acquired bonds between stimulus and response.

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In cognitive science, an approach that proposes to model human information processing in terms of a network of interconnected units operating in parallel.

The units are typically classified as input units, hidden units, or output units. Each unit has a default activation level that can vary as a function of the strength of (1) the inputs it receives from other units, (2) the different weights associated with its connections to the other units, and (3) its own bias. Connectionism, unlike traditional computational models in cognitive science, holds that information is distributed throughout entire networks instead of being localized in functionally discrete, semantically interpretable states.

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 an approach to artificial intelligence (AI) that developed out of attempts to understand how the human brain works at the neural level and, in particular, how people learn and remember. (For that reason, this approach is sometimes referred to as neuronlike computing.) In 1943 the neurophysiologist Warren McCulloch of the University of Illinois and the mathematician Walter Pitts of the University of Chicago published an influential treatise on neural networks (neural network) and automatons (automaton), according to which each neuron in the brain is a simple digital processor and the brain as a whole is a form of computing machine. As McCulloch put it subsequently, “What we thought we were doing (and I think we succeeded fairly well) was treating the brain as a Turing machine.”

B.J. Copeland

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Universalium. 2010.

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