Confabulation (neural networks)

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A confabulation, also known as a false, degraded, or corrupted memory, is a stable pattern of activation in an artificial neural network or neural assembly that does not correspond to any previously learned patterns. The same term is also applied to the (nonartificial) neural mistake-making process leading to a false memory (confabulation).

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Cognitive science

In cognitive science, the generation of confabulatory patterns is symptomatic of some forms of brain trauma. [1] In this, confabulations relate to pathologically induced neural activation patterns depart from direct experience and learned relationships. In computational modeling of such damage, related brain pathologies such as dyslexia and hallucination result from simulated lesioning [2] and neuron death. [3] Forms of confabulation in which missing or incomplete information is incorrectly filled in by the brain are generally modelled by the well known neural network process called pattern completion. [4]

Neural networks

Confabulation is central to a theory of cognition and consciousness by S. L. Thaler in which thoughts and ideas originate in both biological and synthetic neural networks as false or degraded memories nucleate upon various forms of neuronal and synaptic fluctuations and damage. [5] [6] 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). [7] [8] The exploitation of these false memories by other artificial neural networks forms the basis of inventive artificial intelligence systems currently utilized in product design, [9] [10] materials discovery [11] and improvisational military robots. [12] Compound, confabulatory systems of this kind [13] have been used as sensemaking systems for military intelligence and planning, [12] self-organizing control systems for robots and space vehicles, [14] and entertainment. [12] The concept of such opportunistic confabulation grew out of experiments with artificial neural networks that simulated brain cell apoptosis. [15] It was discovered that novel perception, ideation, and motor planning could arise from either reversible or irreversible neurobiological damage. [16] [17]

Computational inductive reasoning

The term confabulation is also used by Robert Hecht-Nielsen in describing inductive reasoning accomplished via Bayesian networks. [18] 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 being implemented computationally on parallel computers.

Related Research Articles

Cognitive science Interdisciplinary scientific study of the mind and its processes

Cognitive science is the interdisciplinary, scientific study of the mind and its processes with input from linguistics, psychology, neuroscience, philosophy, computer science/artificial intelligence, and anthropology. It examines the nature, the tasks, and the functions of cognition. Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, from learning and decision to logic and planning; from neural circuitry to modular brain organization. One of the fundamental concepts of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

Cognitive neuroscience Scientific field

Cognitive neuroscience is the scientific field that is concerned with the study of the biological processes and aspects that underlie cognition, with a specific focus on the neural connections in the brain which are involved in mental processes. It addresses the questions of how cognitive activities are affected or controlled by neural circuits in the brain. Cognitive neuroscience is a branch of both neuroscience and psychology, overlapping with disciplines such as behavioral neuroscience, cognitive psychology, physiological psychology and affective neuroscience. Cognitive neuroscience relies upon theories in cognitive science coupled with evidence from neurobiology, and computational modeling.

Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness, is a field related to artificial intelligence and cognitive robotics. The aim of the theory of artificial consciousness is to "Define that which would have to be synthesized were consciousness to be found in an engineered artifact".

Computational neuroscience is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.

Neuromorphic engineering, also known as neuromorphic computing, is the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.

Functional neuroimaging

Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.

Neural network Structure in biology and artificial intelligence

A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.

Neurophilosophy or philosophy of neuroscience is the interdisciplinary study of neuroscience and philosophy that explores the relevance of neuroscientific studies to the arguments traditionally categorized as philosophy of mind. The philosophy of neuroscience attempts to clarify neuroscientific methods and results using the conceptual rigor and methods of philosophy of science.

Stephen Grossberg American scientist (born 1939)

Stephen Grossberg is a cognitive scientist, theoretical and computational psychologist, neuroscientist, mathematician, biomedical engineer, and neuromorphic technologist. He is the Wang Professor of Cognitive and Neural Systems and a Professor Emeritus of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering at Boston University.

Outline of thought Overview of and topical guide to thought

The following outline is provided as an overview of and topical guide to thought (thinking):

Computational creativity Multidisciplinary endeavour

Computational creativity is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts.

Hava Siegelmann is a professor of computer science. Her academic position is in the school of Computer Science and the Program of Neuroscience and Behavior at the University of Massachusetts Amherst; she is the director of the school's Biologically Inspired Neural and Dynamical Systems Lab and is the Provost Professor of the University of Massachusetts. She was loaned to the federal government DARPA 2016-2019 to initiate and run their most advanced AI programs including her Lifelong Learning Machine (L2M) program. and Guaranteeing AI Robustness against Deceptions (GARD). She received the rarely awarded Meritorious Public Service Medal - one of the highest honors the Department of Defense agency can bestow on a private citizen.

Deep dyslexia is a form of dyslexia that disrupts reading processes. Deep dyslexia may occur as a result of a head injury, stroke, disease, or operation. This injury results in the occurrence of semantic errors during reading and the impairment of nonword reading.

Basic science (psychology) Subdisciplines within psychology

Some of the research that is conducted in the field of psychology is more "fundamental" than the research conducted in the applied psychological disciplines, and does not necessarily have a direct application. The subdisciplines within psychology that can be thought to reflect a basic-science orientation include biological psychology, cognitive psychology, neuropsychology, and so on. Research in these subdisciplines is characterized by methodological rigor. The concern of psychology as a basic science is in understanding the laws and processes that underlie behavior, cognition, and emotion. Psychology as a basic science provides a foundation for applied psychology. Applied psychology, by contrast, involves the application of psychological principles and theories yielded up by the basic psychological sciences; these applications are aimed at overcoming problems or promoting well-being in areas such as mental and physical health and education.

Cognitive musicology is a branch of cognitive science concerned with computationally modeling musical knowledge with the goal of understanding both music and cognition.

In psychology, confabulation is a memory error defined as the production of fabricated, distorted, or misinterpreted memories about oneself or the world. It is generally associated with certain types of brain damage or a specific subset of dementias. While still an area of ongoing research, the basal forebrain is implicated in the phenomenon of confabulation. People who confabulate present with incorrect memories ranging from subtle inaccuracies to surreal fabrications, and may include confusion or distortion in the temporal framing of memories. In general, they are very confident about their recollections, even when challenged with contradictory evidence.

The Dehaene–Changeux model (DCM), also known as the global neuronal workspace or the global cognitive workspace model is a part of Bernard Baars's "global workspace model" for consciousness.

The following outline is provided as an overview of and topical guide to the human brain:

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

Kathleen McDermott is Professor of Psychological and Brain Sciences at Washington University in St. Louis. She is known for her research on how human memory is encoded and retrieved, with a specific interest in how false memories develop. In collaboration with Henry L. (Roddy) Roediger III, she developed the Deese-Roediger-McDermott paradigm used to study the phenomenon of memory illusions. McDermott received the 2004-2005 F.J. McGuigan Young Investigator Prize for research on memory from the American Psychological Foundation and the American Psychological Association's Science Directorate. She was recognized by the Association for Psychological Science as a Rising Star in 2007. McDermott is a Fellow of the Psychonomic Society and was honored with a 2019 Psychonomic Society Mid-Career Award.

References

  1. ↑ Gazzaniga, M. S. (1995). "The Cognitive Neurosciences", A Bradford Book, The MIT Press, Cambridge, Massachusetts.
  2. ↑ Plaut, D.C. (1993). "Deep Dyslexia: A case of connectionist neuropsychology" Archived 2016-03-03 at the Wayback Machine . Cognitive Neuropsychology, 10(5), 377-500.
  3. ↑ Yam, P. (1993). "Daisy, Daisy" Do computers have near-death experience, Scientific American, May 1993.
  4. ↑ "Neural associative memory". rni.org. Archived from the original on 2008-10-20. Retrieved 2009-07-22.
  5. ↑ Thaler, S. L. (1997a). U.S. 5,659,666, "Device for the Autonomous Generation of Useful Information", Issued 8/19/1997.
  6. ↑ Thaler, S. L. (1997b). "A Quantitative Model of Seminal Cognition: the creativity machine paradigm", Proceedings of the Mind II Conference, Dublin, Ireland, 1997.
  7. ↑ Thaler, S. L. (2011, "The Creativity Machine Paradigm: Withstanding the Argument from Consciousness" Archived 2012-11-15 at the Wayback Machine
  8. ↑ Thaler, S. L. (2013) "The Creativity Machine Paradigm, Encyclopedia of Creativity, Invention, Innovation, and Entrepreneurship", (ed.) E.G. Carayannis, Springer Science+Business Media
  9. ↑ Pickover, C. A. (2005). Sex, Drugs, Einstein, & Elves, SmartPublications, Petaluma, CA.
  10. ↑ Plotkin, R. (2009). The Genie in the Machine: How Computer-Automated Inventing is Revolutionizing Law and Business, Stanford University Press
  11. ↑ Thaler, S. L. (1998). Predicting ultra-hard binary compounds via cascaded auto- and hetero-associative neural networks, Journal of Alloys and Compounds, 279(1998), 47-59.
  12. 1 2 3 Hesman, T. (2004). The Machine That Invents, St. Louis Post-Dispatch, Jan. 25, 2004.
  13. ↑ Thaler, S. L. (1996). A Proposed Symbolism for Network-Implemented Discovery Processes, In Proceedings of the World Congress on Neural Networks, (WCNN’96), Lawrence Erlbaum, Mawah, NJ.
  14. ↑ Patrick, M. C., Stevenson-Chavis, K., Thaler, S. L. (2007). "Demonstration of Self-Training Autonomous Neural Networks in Space Vehicle Docking Simulations", Aerospace Conference, 2007, 3–10 March 2007 IEEE
  15. ↑ Yam, P. (1995). As They Lay Dying, Scientific American, May 1995.
  16. ↑ Thaler, S. L. (1995). Death of a gedanken creature, Journal of Near-Death Studies, 13(3), Spring 1995.
  17. ↑ Thaler, S. L. (2012). The Creativity Machine Paradigm: Withstanding the Argument from Consciousness, APA Newsletter, Volume 11, Number 2, Spring 2012.
  18. ↑ Hecht-Nielsen, R (2005). "Cogent confabulation" Archived 2016-01-14 at the Wayback Machine . Neural Networks 18:111-115.
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