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Artificial neural networks (ANNs), usually simply called neural networks (NNs) or, more simply yet, neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
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.
Computational neuroscience is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
The experimental analysis of behavior is school of thought in psychology founded on B. F. Skinner's philosophy of radical behaviorism and defines the basic principles used in applied behavior analysis. A central principle was the inductive reasoning data-driven examination of functional relations, as opposed to the kinds of hypothetico-deductive learning theory that had grown up in the comparative psychology of the 1920–1950 period. Skinner's approach was characterized by observation of measurable behavior which could be predicted and controlled. It owed its early success to the effectiveness of Skinner's procedures of operant conditioning, both in the laboratory and in behavior therapy.
Neuroethology is the evolutionary and comparative approach to the study of animal behavior and its underlying mechanistic control by the nervous system. It is an interdisciplinary science that combines both neuroscience and ethology. A central theme of neuroethology, which differentiates it from other branches of neuroscience, is its focus on behaviors that have been favored by natural selection rather than on behaviors that are specific to a particular disease state or laboratory experiment.
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.
Mathematical psychology is an approach to psychological research that is based on mathematical modeling of perceptual, thought, cognitive and motor processes, and on the establishment of law-like rules that relate quantifiable stimulus characteristics with quantifiable behavior. The mathematical approach is used with the goal of deriving hypotheses that are more exact and thus yield stricter empirical validations. There are five major research areas in mathematical psychology: learning and memory, perception and psychophysics, choice and decision-making, language and thinking, and measurement and scaling.
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.
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although practical ties between the two fields are limited. From the practical standpoint, reusing or transferring information from previously learned tasks for the learning of new tasks has the potential to significantly improve the sample efficiency of a reinforcement learning agent.
William Kaye Estes was an American psychologist. A Review of General Psychology survey, published in 2002, ranked Estes as the 77th most cited psychologist of the 20th century. In order to develop a statistical explanation for the learning phenomena, William Kaye Estes developed the Stimulus Sampling Theory in 1950 which suggested that a stimulus-response association is learned on a single trial; however, the learning process is continuous and consists of the accumulation of distinct stimulus-response pairings.
Michael Lamport Commons is a theoretical behavioral scientist and a complex systems scientist. He developed the model of hierarchical complexity.
Behavioral momentum is a theory in quantitative analysis of behavior and is a behavioral metaphor based on physical momentum. It describes the general relation between resistance to change and the rate of reinforcement obtained in a given situation.
Quantitative analysis of behavior is the application of mathematical models to the experimental analysis of behavior. The aim is to describe and/or predict relations between independent environmental variables and dependent behavioral variables. The parameters in the models hopefully have theoretical meaning beyond their use in fitting models to data. The field was founded by Richard Herrnstein (1961) when he introduced the matching law to quantify the behavior of organisms working on concurrent schedules of reinforcement. The field has integrated models from economics, zoology, philosophy, and other branches of psychology, especially mathematical psychology of which it is a branch. The field is represented by the Society for Quantitative Analysis of Behavior. Quantitative analysis of behavior addresses the following topics among others: behavioral economics, behavioral momentum, connectionist systems or neural networks, integration, hyperbolic discounting including the delay reduction hypothesis, foraging, hunting, errorless learning, creativity, learning, and the Rescorla-Wagner model, matching law, melioration, scalar expectancy, signal detection, neural hysteresis, and reinforcement control.
Walter F. Heiligenberg is best known for his neuroethology work on one of the best neurologically understood behavioral patterns in a vertebrate, Eigenmannia. This weakly electric fish and the neural basis for its jamming avoidance response behavioral process was the main focus of his research, and is fully explored in his 1991 book, "Neural Nets in Electric Fish." As an international scientist, he worked alongside other neuroethologists and researchers to further explain animal behavior in a comprehensive manner and "through the application of a strict analytical and quantitative method". The advancements within neuroethology today are still largely due to his influences, as his life was dedicated to researching that which could be applicable to "all complex nervous systems" and he "[investigated] the general principles of nature".
In science, computing, and engineering, a black box is a system which can be viewed in terms of its inputs and outputs, without any knowledge of its internal workings. Its implementation is "opaque" (black). The term can be used to refer to many inner workings, such as the ones of a transistor, an engine, an algorithm, the human brain, or an institution or government.
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.
David LaBerge is a neuropsychologist specializing in the attention process and the role of apical dendrites in cognition and consciousness.
The Troland Research Awards are an annual prize given by the United States National Academy of Sciences to two researchers in recognition of psychological research on the relationship between consciousness and the physical world. The areas where these award funds are to be spent include but are not limited to areas of experimental psychology, the topics of sensation, perception, motivation, emotion, learning, memory, cognition, language, and action. The award preference is given to experimental work with a quantitative approach or experimental research seeking physiological explanations.
Network of human nervous system comprises nodes that are connected by links. The connectivity may be viewed anatomically, functionally, or electrophysiologically. These are presented in several Wikipedia articles that include Connectionism, Biological neural network, Artificial neural network, Computational neuroscience, as well as in several books by Ascoli, G. A. (2002), Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011), Gerstner, W., & Kistler, W. (2002), and Rumelhart, J. L., McClelland, J. L., and PDP Research Group (1986) among others. The focus of this article is a comprehensive view of modeling a neural network. Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic, mesoscopic, or macroscopic (system) levels. Computational modeling refers to models that are developed using computing tools.
Kanaka Rajan is a neuroscientist and Assistant Professor in the Department of Neuroscience and Friedman Brain Institute at the Icahn School of Medicine at Mount Sinai in New York City. Rajan trained in engineering, biophysics, and neuroscience, and has pioneered novel methods and models to understand how the brain processes sensory information. Her research seeks to understand how important cognitive functions — such as learning, remembering, and deciding — emerge from the cooperative activity of multi-scale neural processes, and how those processes are affected by various neuropsychiatric disease states. The resulting integrative theories about the brain bridge neurobiology and artificial intelligence.
References
- Commons, M. L., (2001). A short history of the Society for the Quantitative Analysis of Behavior. Behavior Analyst Today, 2(3), 275-279 .
- Fields, L., & Nevin, J. A. (1993). Stimulus Equivalence [Special issue]. Psychological Record, 43(4)
- Theories in Progress: Proceedings of the Meeting of the Society for the Quantitative Analyses of Behaviour (2003). Behavioural Processes. 62, (1–3), 1–198
- Proceedings of the Meeting of the Society for the Quantitative Analyses of Behaviour (2004). Behavioural Processes. 66(3), 173–366.
- Proceedings of the Meeting of the Society for the Quantitative Analyses of Behaviour (2005). Behavioural Processes. 69(2), 97–279
- Proceedings of the Meeting of the Society for the Quantitative Analyses of Behaviour (2006). Behavioural Processes. 72(3),191–310.
- Proceedings of the Meeting of the Society for the Quantitative Analyses Behavior(SQAB 2006)(2007). Behavioural Processes. 75(2), 91–252.