Changes: Confabulation (neural networks)

Revision as of 23:14, 9 May 2009

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This article is about the neural networks process. For other uses, see Confabulation.

Confabulation is the neural process in S. L. Thaler's [1] theory of cognition and consciousness in which all thoughts and ideas originate in both biological and synthetic neural networks as false memories nucleate upon various forms of neuronal and synaptic fluctuations. Such novel patterns of neural activation are promoted to ideas as other neural nets perceive utility or value to them (i.e., the thalamo-cortical loop). Not only did Thaler theorize about this process in the early 90's, he reduced it to practice in 1994 in his landmark U.S. Patent 5,659,666, "Device for the Autonomous Generation of Useful Information." [2]This patent stems from Thaler's discovery that when a trained neural network is subjected to increasing levels of internal synaptic perturbation, it shifts from rote memory production to confabulation generation. It is in this transition region that ideas are born.[3] In 1995, Thaler first described a universal theory of animal cognition in which novel movement planning schemes arise from various forms of synaptic fluctuations within the central nervous system of a hypothetical creature. Later (1996c) he demonstrated that both inductive and deductive inferences could autonomously form within cascades of confabulating neural networks.

References:

Thaler, S. L. (1995). Death of a gedanken creature, Journal of Near-Death Studies, 13(3), Spring 1995.

Thaler, S. L. (1996a) Creativity via network cavitation – an architecture, implementation, and results, Adaptive Distributive Parallel Computing Symposium, Dayton, Ohio, 8-9 August, 1996.

Thaler, S.L. (1996b). The death dream and near-death darwinism, Journal of Near-Death Studies, 15(1), Fall 1996.

Thaler, (1996c). A Proposed Symbolism for Network-Implemented Discovery Processes, In Proceedings of the World Congress on Neural Networks, (WCNN’96), Lawrence Erlbaum, Mawah, NJ.

Thaler, S. L. (1997). "The Fragmentation of the Universe and the Devolution of Consciousness," U.S. Library of Congress, Registration No. TXU00775586, 1997.

Thaler, S. L. (1998). Predicting ultra-hard binary compounds via cascaded auto- and hetero-associative neural newtorks, Journal of Alloys and Compounds, 279(1998), 47-59.

Thaler, S. L. (1999a). No mystery intended. Neural Networks, Volume 12, Issue 1, January 1999, Pages 193-194.

Thaler, S. L. (1999b), AFRL-ML-WP-TR-1999-4033, Integrated Substrate and Thin Film Design Methods, Materials and Manufacturing Directorate, Air Force Research Laboratory, Air Force Materiel Command, Wright-Patterson Air Force Base, OH 45433-7750

Confabulation is also a neural process in Robert Hecht-Nielsen's theory of inductive reasoning. Confabulation is used to select the expectancy of the concept that follows a particular context. This is not an Aristotelian deductive process, although it yields simple deduction when memory only holds unique events. However, most events and concepts occur in multiple, conflicting contexts and so confabulation yields a consensus of an expected event that may only be minimally more likely than many other events. However, given the winner take all constraint of the theory, that is the event/symbol/concept/attribute that is then expected. This parallel computation on many contexts is postulated to occur in less than a tenth of a second. Confabulation grew out of vector analysis of data retrieval like that of latent semantic analysis and support vector machines. It is currently used to detect credit card fraud. It is being implemented computationally on parallel computers.

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 {{This|the neural networks process|Confabulation}}
+Confabulation is the neural process in S. L. Thaler's [1] theory of cognition and consciousness in which all thoughts and ideas originate in both biological and synthetic neural networks as false memories nucleate upon various forms of neuronal and synaptic fluctuations. Such novel patterns of neural activation are promoted to ideas as other neural nets perceive utility or value to them (i.e., the thalamo-cortical loop). Not only did Thaler theorize about this process in the early 90's, he reduced it to practice in 1994 in his landmark U.S. Patent 5,659,666, "Device for the Autonomous Generation of Useful Information." [2]This patent stems from Thaler's discovery that when a trained neural network is subjected to increasing levels of internal synaptic perturbation, it shifts from rote memory production to confabulation generation. It is in this transition region that ideas are born.[3] In 1995, Thaler first described a universal theory of animal cognition in which novel movement planning schemes arise from various forms of synaptic fluctuations within the central nervous system of a hypothetical creature. Later (1996c) he demonstrated that both inductive and deductive inferences could autonomously form within cascades of confabulating neural networks.
-'''Confabulation''' is a neural process in [[Robert Hecht-Nielsen]]'s theory of [[inductive]] reasoning.
+References:
-Confabulation is used to select the expectancy of the concept that follows a particular context. This is not an [[Aristotelian]] [[deductive]] process, although it yields simple deduction when memory only holds unique events.  However, most events and concepts occur in multiple, conflicting contexts and so confabulation yields a consensus of an expected event that may only be minimally more likely than many other events.However, given the winner take all constraint of the theory, that is the event/symbol/concept/attribute that is then expected.  This parallel computation on many contexts is postulated to occur in less than a tenth of a second.  Confabulation grew out of vector analysis of data retrieval like that of [[latent semantic analysis]] and [[support vector machines]].  It is currently used to detect credit card fraud.  It is being implemented computationally on parallel computers.
+Thaler, S. L. (1995). Death of a gedanken creature, Journal of Near-Death Studies, 13(3), Spring 1995.
+Thaler, S. L. (1996a) Creativity via network cavitation – an architecture, implementation, and results, Adaptive Distributive Parallel Computing Symposium, Dayton, Ohio, 8-9 August, 1996.
+Thaler, S.L. (1996b). The death dream and near-death darwinism, Journal of Near-Death Studies, 15(1), Fall 1996.
+Thaler, (1996c). A Proposed Symbolism for Network-Implemented Discovery Processes, In Proceedings of the World Congress on Neural Networks, (WCNN’96), Lawrence Erlbaum, Mawah, NJ.
+Thaler, S. L. (1997). "The Fragmentation of the Universe and the Devolution of Consciousness," U.S. Library of Congress, Registration No. TXU00775586, 1997.
+Thaler, S. L. (1998). Predicting ultra-hard binary compounds via cascaded auto- and hetero-associative neural newtorks, Journal of Alloys and Compounds, 279(1998), 47-59.
+Thaler, S. L. (1999a). No mystery intended. Neural Networks, Volume 12, Issue 1, January 1999, Pages 193-194.
+Thaler, S. L. (1999b), AFRL-ML-WP-TR-1999-4033, Integrated Substrate and Thin Film Design Methods, Materials and Manufacturing Directorate, Air Force Research Laboratory, Air Force Materiel Command, Wright-Patterson Air Force Base, OH 45433-7750
+Confabulation is also a neural process in Robert Hecht-Nielsen's theory of inductive reasoning. Confabulation is used to select the expectancy of the concept that follows a particular context. This is not an Aristotelian deductive process, although it yields simple deduction when memory only holds unique events. However, most events and concepts occur in multiple, conflicting contexts and so confabulation yields a consensus of an expected event that may only be minimally more likely than many other events. However, given the winner take all constraint of the theory, that is the event/symbol/concept/attribute that is then expected. This parallel computation on many contexts is postulated to occur in less than a tenth of a second. Confabulation grew out of vector analysis of data retrieval like that of latent semantic analysis and support vector machines. It is currently used to detect credit card fraud. It is being implemented computationally on parallel computers.
 [[Category:Neural networks]]