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What is Connectionism

Handbook of Research on Computational Arts and Creative Informatics
A thread of AI that is characterized by the development of systems, which are composed of numerous simple units that are massively interconnected and inspired by neurons.
Published in Chapter:
Memory Association Machine
Benjamin David Robert Bogart (Simon Fraser University, Canada)
DOI: 10.4018/978-1-60566-352-4.ch013
Abstract
“Memory Association Machine” (also known as “Self-Other Organizing Structure #1”) is the first prototype in a series of site-specific responsive installations inspired by cognitive processes. The artist provides a mechanism that allows the structure of the artwork to change in response to continuous stimulus from its context. Context is defined as those parameters of the environment that are perceivable by the system and make its place in space and time unique. “Memory Association Machine” relates itself to its context using three primary processes: perception, the integration of sensor data into a field of experience, and the free-association through that field. “Memory Association Machine” perceives through a video camera, integrates using a Kohonen Self-Organizing Map, and free-associates through an implementation of Liane M. Gabora’s model of memory and creativity.
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A Virtual Laboratory of Mathematics Education
Psychology theory developed by Edward L. Thorndyke in the beginning of the 20th century. It was a forerunner of behaviourism. Thorndyke argued that bonds between stimulus and responses are strengthened through exercise in which success is rewarded. These principles have been applied in the area of mathematics education by Thorndyke himself.
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Walking the Information Overload Tightrope
A movement in cognitive science aimed at explaining human intellectual abilities using artificial neural networks (also called “neural networks” or “neural nets”).
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Fundamentals of Learning Theories
Stanford Encyclopedia of Philosophy defines connectionism as a movement in cognitive science which hopes to explain human intellectual abilities using artificial neural networks (also known as “neural networks” or “neural nets”). Neural networks are simplified models of the brain composed of large numbers of units (the analogs of neurons) together with weights that measure the strength of connections between the units. These weights model the effects of the synapses that link one neuron to another. Experiments on models of this kind have demonstrated an ability to learn such skills as face recognition, reading, and the detection of simple grammatical structure.
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Artificial Neural Networks and Cognitive Modelling
Connectionism is the term used to describe the application of artificial neural networks to the study of mind. In connectionist accounts, knowledge is represented in the strength of connections between a set of artificial neurons.
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