Academic literature on the topic 'Mathematical Models of Cognitive Processes and Neural Networks'
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Journal articles on the topic "Mathematical Models of Cognitive Processes and Neural Networks"
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Marshakov, D. V. "Modeling of the processes of extracting rules from Neural Network components with Petri nets." Journal of Physics: Conference Series 2131, no. 2 (December 1, 2021): 022133. http://dx.doi.org/10.1088/1742-6596/2131/2/022133.
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Abstract The paper deals with the use of extended Petri nets in modeling the processes of extracting rules from neural network components. The mathematical model for extracting rules from neural network components based on a modified timed Petri net is constructed, followed by an analysis of its dynamic behavior based on a timed reachability graph, which is a set of all its states that can be reached when a finite number of transitions are fired. The proposed model allows us to move from the initial detailed structure to its simplified description, which preserves the possibility of obtaining information about the structure and dynamic behavior of the neural network system. The proposed approach can be used in the synthesis of cognitive systems with a neural network organization to provide computational support for the functions of forming, learning, and correcting cognitive networks that display neural network models.
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Isomura, Takuya, and Karl Friston. "Reverse-Engineering Neural Networks to Characterize Their Cost Functions." Neural Computation 32, no. 11 (November 2020): 2085–121. http://dx.doi.org/10.1162/neco_a_01315.
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This letter considers a class of biologically plausible cost functions for neural networks, where the same cost function is minimized by both neural activity and plasticity. We show that such cost functions can be cast as a variational bound on model evidence under an implicit generative model. Using generative models based on partially observed Markov decision processes (POMDP), we show that neural activity and plasticity perform Bayesian inference and learning, respectively, by maximizing model evidence. Using mathematical and numerical analyses, we establish the formal equivalence between neural network cost functions and variational free energy under some prior beliefs about latent states that generate inputs. These prior beliefs are determined by particular constants (e.g., thresholds) that define the cost function. This means that the Bayes optimal encoding of latent or hidden states is achieved when the network's implicit priors match the process that generates its inputs. This equivalence is potentially important because it suggests that any hyperparameter of a neural network can itself be optimized—by minimization with respect to variational free energy. Furthermore, it enables one to characterize a neural network formally, in terms of its prior beliefs.
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Musso, Mariel, Eva Kyndt, Eduardo Cascallar, and Filip Dochy. "Predicting Mathematical Performance: The Effect of Cognitive Processes and Self-Regulation Factors." Education Research International 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/250719.
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A substantial number of research studies have investigated the separate influence of working memory, attention, motivation, and learning strategies on mathematical performance and self-regulation in general. There is still little understanding of their impact on performance when taken together, understanding their interactions, and how much each of them contributes to the prediction of mathematical performance. With the emergence of new methodologies and technologies, such as the modelling with predictive systems, it is now possible to study these effects with approaches which use a wide range of data, including student characteristics, to estimate future performance without the need of traditional testing (Boekaerts and Cascallar, 2006). This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables associated with different levels of mathematical performance using artificial neural networks (ANNs). A sample of 800 entering university students was used to develop three ANN models to identify the expected future level of performance in a mathematics test. These ANN models achieved high degree of precision in the correct classification of future levels of performance, showing differences in the pattern of relative predictive weight amongst those variables. The impact on educational quality, improvement, and accountability is highlighted.
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Schubert, Anna-Lena, and Gidon T. Frischkorn. "Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences." Current Directions in Psychological Science 29, no. 2 (February 13, 2020): 140–46. http://dx.doi.org/10.1177/0963721419896365.
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More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of cognition and chronometric analyses of neural processing to identify distinct stages of information processing strongly related to intelligence differences. Evidence from both approaches suggests that the speed of higher-order processing is greater in smarter individuals, which may reflect advantages in the structural and functional organization of brain networks. Adopting a similar neurocognitive-psychometrics approach for other cognitive processes associated with intelligence (e.g., working memory or executive control) may refine our understanding of the basic cognitive processes of intelligence.
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Wang, Yingxu. "The Cognitive Mechanisms and Formal Models of Consciousness." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 2 (April 2012): 23–40. http://dx.doi.org/10.4018/jcini.2012040102.
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Consciousness is the sense of self and the sign of life in natural intelligence. One of the profound myths in cognitive informatics, psychology, brain science, and computational intelligence is how consciousness is generated by physiological organs and neural networks in the bran. This paper presents a formal model and a cognitive process of consciousness in order to explain how abstract consciousness is generated and what its cognitive mechanisms are. The hierarchical levels of consciousness are explored from the facets of neurology, physiology, and computational intelligence. A rigorous mathematical model of consciousness is created that elaborates the nature of consciousness. The cognitive process of consciousness is formally described using denotational mathematics. It is recognized that consciousness is a set of real-time mental information about bodily and emotional status of an individual stored in the cerebellums known as the Conscious Status Memory (CSM) and is processed/interpreted by the thalamus. The abstract intelligence model of consciousness can be applied in cognitive informatics, cognitive computing, and computational intelligence toward the mimicry and simulation of human perception and awareness of the internal states, external environment, and their interactions in reflexive, perceptive, cognitive, and instructive intelligence.
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Gudowska-Nowak, E., M. A. Nowak, D. R. Chialvo, J. K. Ochab, and W. Tarnowski. "From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior." Neural Computation 32, no. 2 (February 2020): 395–423. http://dx.doi.org/10.1162/neco_a_01253.
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The study of neuronal interactions is at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, and the Brainome) that attempt to obtain a detailed map of the entire brain. Under certain constraints, mathematical theory can advance predictions of the expected neural dynamics based solely on the statistical properties of the synaptic interaction matrix. This work explores the application of free random variables to the study of large synaptic interaction matrices. Besides recovering in a straightforward way known results on eigenspectra in types of models of neural networks proposed by Rajan and Abbott ( 2006 ), we extend them to heavy-tailed distributions of interactions. More important, we analytically derive the behavior of eigenvector overlaps, which determine the stability of the spectra. We observe that on imposing the neuronal excitation/inhibition balance, despite the eigenvalues remaining unchanged, their stability dramatically decreases due to the strong nonorthogonality of associated eigenvectors. This leads us to the conclusion that understanding the temporal evolution of asymmetric neural networks requires considering the entangled dynamics of both eigenvectors and eigenvalues, which might bear consequences for learning and memory processes in these models. Considering the success of free random variables theory in a wide variety of disciplines, we hope that the results presented here foster the additional application of these ideas in the area of brain sciences.
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Crumpei, Gabriel, and Alina Gavriluţ. "Emergence, a Universal Phenomenon which Connects Reality to Consciousness, Natural Sciences to Humanities." Human and Social Studies 7, no. 2 (June 1, 2018): 89–106. http://dx.doi.org/10.2478/hssr-2018-0017.
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Abstract Progress in neuroscience has left a central question of psychism unanswered: what is consciousness? Modeling the psyche from a computational perspective has helped to develop cognitive neurosciences, but it has also shown their limits, of which the definition, description and functioning of consciousness remain essential. From Rene Descartes, who tackled the issue of psychism as the brain-mind dualism, to Chambers, who defined qualia as the tough, difficult problem of research in neuroscience, many hypotheses and theories have been issued to encompass the phenomenon of consciousness. Neuroscience specialists, such as Giulio Tononi or David Eagleman, consider consciousness as a phenomenon of emergence of all processes that take place in the brain. This hypothesis has the advantage of being supported by progress made in the study of complex systems in which the issue of emergence can be mathematically formalized and analyzed by physical-mathematical models. The current tendency to associate neural networks within the broad scope of network science also allows for a physical-mathematical formalization of phenomenology in neural networks and the construction of information-symbolic models. The extrapolation of emergence at the level of physical systems, biological systems and psychic systems can bring new models that can also be applied to the concept of consciousness. The meaning and significance that seem to structure the nature of consciousness is found as direction of evolution and teleological finality, of integration in the whole system and in any complex system at all scales. Starting from the wave-corpuscle duality in quantum physics, we can propose a model for structuring reality, based on the emergence of systems that contribute to the integration and coherence of the entire reality. Physical-mathematical models based mainly on (mereo)topology can provide a mathematical formalization path, and the paradigm of information could allow the development of a pattern of emergence, that is common to all systems, including the psychic system, the difference being given only by the degree of information complexity. Thus, the mind-brain duality, which has been dominating the representation on psychism for a few centuries, could be solved by an informational approach, describing the connection between object and subject, reality and human consciousness, between mind and brain, thus unifying the perspective on natural sciences and humanities.
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HOTTON, SCOTT, and JEFF YOSHIMI. "THE DYNAMICS OF EMBODIED COGNITION." International Journal of Bifurcation and Chaos 20, no. 04 (April 2010): 943–72. http://dx.doi.org/10.1142/s0218127410026241.
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Historically cognition was understood as the result of processes occurring solely in the brain. Recently, however, cognitive scientists and philosophers studying "embodied" or "situated" cognition have begun emphasizing the role of the body and environment in which brains are situated, i.e. they view the brain as an "open system". However, these theorists frequently rely on dynamical systems which are traditionally viewed as closed systems. We address this tension by extending the framework of dynamical systems theory. We show how structures which appear in the state space of an embodied agent differ from those that appear in closed systems, and we show how these structures can be used to model representational processes in embodied agents. We focus on neural networks as models of embodied cognition.
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Zhuravskyi, Yurii, Oleg Sova, Serhii Korobchenko, Vitaliy Baginsky, Yurii Tsimura, Leonid Kolodiichuk, Pavlo Khomenko, Nataliia Garashchuk, Olena Orobinska, and Andrii Shyshatskyi. "Development of object state evaluation method in intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 6, no. 9 (114) (December 29, 2021): 54–63. http://dx.doi.org/10.15587/1729-4061.2021.246421.
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Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. A methodological approach to improve the accuracy of assessing the state of the monitored object is proposed. This methodological approach is based on a combination of fuzzy cognitive models, advanced genetic algorithm and evolving artificial neural networks. The methodological approach has the following sequence of actions: building a fuzzy cognitive model; correcting the fuzzy cognitive model and training knowledge bases. The distinctive features of the methodological approach are that the type of data uncertainty and noise is taken into account while constructing the state of the monitored object using fuzzy cognitive models. The novelties while correcting fuzzy cognitive models using a genetic algorithm are taking into account the type of data uncertainty, taking into account the adaptability of individuals to iteration, duration of the existence of individuals and topology of the fuzzy cognitive model. The advanced genetic algorithm increases the efficiency of correcting factors and the relationships between them in the fuzzy cognitive model. This is achieved by finding solutions in different directions by several individuals in the population. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The use of the method allows increasing the efficiency of data processing at the level of 16–24 % using additional advanced procedures. The proposed methodological approach should be used to solve the problems of assessing complex and dynamic processes characterized by a high degree of complexity.
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Pavlic, Tomislav, Krunoslav Kušec, Danijel Radočaj, Alen Britvić, Marin Lukas, Vladimir Milić, and Mladen Crneković. "Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements." Applied Sciences 11, no. 6 (March 20, 2021): 2786. http://dx.doi.org/10.3390/app11062786.
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In recent years in mobile robotics, the focus has been on methods, in which the fusion of measurement data from various systems leads to models of the environment that are of a probabilistic type. The cognitive model of the environment is less accurate than the exact mathematical one, but it is unavoidable in the robot collaborative interaction with a human. The subject of the research proposed in this paper is the development of a model for learning and planning robot operations. The task of operations and mapping the unknown environment, similar to how humans do the same tasks in the same conditions has been explored. The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot. The mobile robot’s motion with developed artificial neural networks and genetic algorithms is defined. The transfer method of obtained knowledge from simulated to a real system (Sim-To-Real; STR) is proposed. This method includes a training step, a simultaneous reasoning step, and an application step of trained and learned knowledge to control a real robot’s motion. Use of the basic cognitive elements language, a robot’s environment, and its correlation to that environment is described. Based on that description, a higher level of information about the mobile robot’s environment is obtained. The information is directly generated by the fusion of measurement data obtained from various systems.
Dissertations / Theses on the topic "Mathematical Models of Cognitive Processes and Neural Networks"
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Vellmer, Sebastian. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21597.
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In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron],
Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden.
Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert.
IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pulsverhalten oft nicht beschreiben und müssen dazu erweitert werden. Im erste Teil dieser Arbeit wird eine Theorie zur Berechnung der Pulszugleistungsspektren von stochastischen, multidimensionalen IF Neuronen entwickelt. Ausgehend von der zugehörigen Fokker-Planck-Gleichung werden partiellen Differentialgleichung abgeleitet, deren Lösung sowohl die stationäre Wahrscheinlichkeitsverteilung und Feuerrate, als auch das Pulszugleistungsspektrum beschreibt.
Im zweiten Teil wird eine Theorie für große, spärlich verbundene und homogene Netzwerke aus IF Neuronen entwickelt, in der berücksichtigt wird, dass die zeitlichen Korrelationen von Pulszügen selbstkonsistent sind. Neuronale Eingangströme werden durch farbiges Gaußsches Rauschen modelliert, das von einem mehrdimensionalen Ornstein-Uhlenbeck Prozess (OUP) erzeugt wird. Die Koeffizienten des OUP sind vorerst unbekannt und sind als Lösung der Theorie definiert. Um heterogene Netzwerke zu untersuchen, wird eine iterative Methode erweitert.
Im dritten Teil wird die Fokker-Planck-Gleichung auf Binärentscheidungen von Diffusionsentscheidungsmodellen [Engl. diffusion-decision models (DDM)] angewendet. Explizite Gleichungen für die Entscheidungszugstatistiken werden für den einfachsten und analytisch lösbaren Fall von der Fokker-Planck-Gleichung hergeleitet. Für nichtliniear Modelle wird die Schwellwertintegrationsmethode erweitert.This thesis is concerned with the calculation of statistics, in particular the power spectra, of point processes generated by stochastic multidimensional integrate-and-fire (IF) neurons, networks of IF neurons and decision-making models from the corresponding Fokker-Planck equations. In the brain, information is encoded by sequences of action potentials. In studies that focus on spike timing, IF neurons that drastically simplify the spike generation have become the standard model. One-dimensional IF neurons do not suffice to accurately model neural dynamics, however, the extension towards multiple dimensions yields realistic behavior at the price of growing complexity. The first part of this work develops a theory of spike-train power spectra for stochastic, multidimensional IF neurons. From the Fokker-Planck equation, a set of partial differential equations is derived that describes the stationary probability density, the firing rate and the spike-train power spectrum. In the second part of this work, a mean-field theory of large and sparsely connected homogeneous networks of spiking neurons is developed that takes into account the self-consistent temporal correlations of spike trains. Neural input is approximated by colored Gaussian noise generated by a multidimensional Ornstein-Uhlenbeck process of which the coefficients are initially unknown but determined by the self-consistency condition and define the solution of the theory. To explore heterogeneous networks, an iterative scheme is extended to determine the distribution of spectra. In the third part, the Fokker-Planck equation is applied to calculate the statistics of sequences of binary decisions from diffusion-decision models (DDM). For the analytically tractable DDM, the statistics are calculated from the corresponding Fokker-Planck equation. To determine the statistics for nonlinear models, the threshold-integration method is generalized.
Books on the topic "Mathematical Models of Cognitive Processes and Neural Networks"
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Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
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Buscema, Massimo. Intelligent Data Mining in Law Enforcement Analytics: New Neural Networks Applied to Real Problems. Dordrecht: Springer Netherlands, 2013.
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Magnani, Lorenzo. Philosophy and Cognitive Science: Western & Eastern Studies. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
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Kozma, Robert. Advances in Neuromorphic Memristor Science and Applications. Dordrecht: Springer Netherlands, 2012.
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Schütze, Oliver. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
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Falmagne, Jean-Claude. Knowledge Spaces: Applications in Education. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
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Falmagne, Jean-Claude, David Eppstein, Christopher Doble, Dietrich Albert, and Xiangen Hu. Knowledge spaces: Applications in education. Heidelberg: Springer, 2013.
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Spain) Neural Computation and Psychology Workshop (13th 2012 San Sebastián. Computational models of cognitive processes: Proceedings of the 13th Neural Computation and Psychology Workshop, San Sebastian, Spain, 12-14 July 2012. Edited by Mayor, Julien, editor of compilation and Gomez, Pablo (Pablo Alegria), editor of compilation. Hackensack,] New Jersey: World Scientific, 2014.
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1947-, Kitamura Tadashi, ed. What should be computed to understand and model brain function?: From robotics, soft computing, biology and neuroscience to cognitive philosophy. xii, 309 p: ill., 2001.
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Søren, Brunak, ed. Bioinformatics: The machine learning approach. 2nd ed. Cambridge, Mass: MIT Press, 2001.
Find full textBook chapters on the topic "Mathematical Models of Cognitive Processes and Neural Networks"
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Edmonds, Bruce, and Scott Moss. "The Importance of Representing Cognitive Processes in Multi-agent Models." In Artificial Neural Networks — ICANN 2001, 759–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_106.
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Ulrich, Rolf. "Uncovering unobservable cognitive mechanisms: The contribution of mathematical models." In Neuroimaging of Human MemoryLinking cognitive processes to neural systems, 25–42. Oxford University Press, 2009. http://dx.doi.org/10.1093/acprof:oso/9780199217298.003.0003.
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"Selected Mathematical Theories Underpinning Decision Models." In Decision Support for Construction Cost Control in Developing Countries, 95–121. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9873-4.ch005.
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In Chapter 4, various decision support systems have been examined. The rational for Chapter 4 was to appraise the diiferent decision-support systems that have been used in construction without necessarily detailing the complexities and mathematical underpinnings. This chapter will provide the theory that underpins some selected decision support systems. These are regression models (RLM), artificial neural networks (ANN), Matrices, Markov decision processes (MDP) and the ontology rule-based decision support systems.
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J. Sanchez-Ruiz, Francisco. "Reactive Distillation Modeling Using Artificial Neural Networks." In Distillation Processes - From Solar and Membrane Distillation to Reactive Distillation Modelling, Simulation and Optimization. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101261.
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The use of artificial intelligence techniques in the design of processes has generated a line of research of interest, in areas of chemical engineering and especially in the so-called separation processes, in this chapter the combination of artificial neural networks (ANN) is presented and fuzzy dynamic artificial neural networks (DFANN). Applied to the calculation of thermodynamic properties and the design of reactive distillation columns, the ANN and DFANN are mathematical models that resemble the behavior of the human brain, the proposed models do not require linearization of thermodynamic equations, models of mass and energy transfer, this provides an approximate and tight solution compared to robust reactive distillation column design models. Generally, the models must be trained according to a dimensionless model, for the design of a reactive column a dimensionless model is not required, it is observed that the use of robust models for the design and calculation of thermodynamic properties give results that provide better results than those calculated with a commercial simulator such as Aspen Plus (R), it is worth mentioning that in this chapter only the application of neural network models is shown, not all the simulation and implementation are presented, mainly because it is a specialized area where not only requires a chapter for its explanation, it is shown that with a neural network of 16 inputs, 2 hidden layers and 16 outputs, it generates a robust calculation system compared to robust thermodynamic models that contain the same commercial simulator, a characteristic of the network presented is the minimization of overlearning in which the network by its very nature is low. In addition, it is shown that it is a dynamic model that presents adjustment as a function of time with an approximation of 96–98% of adjustment for commercial simulator models such as Aspen Plus (R), the DFANN is a viable alternative for implementation in processes of separation, but one of the disadvantages of the implementation of these techniques is the experience of the programmer both in the area of artificial intelligence and in separation processes.
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Wang, Yingxu, Lotfi A. Zadeh, Bernard Widrow, Newton Howard, Françoise Beaufays, George Baciu, D. Frank Hsu, et al. "Abstract Intelligence." In Cognitive Analytics, 52–69. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch005.
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Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC'16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.
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Trappenberg, Thomas P. "Feed-forward mapping networks." In Fundamentals of Computational Neuroscience, 169–213. 3rd ed. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869364.003.0007.
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Abstract This chapter explores the ability of basic neural networks that feed input from and input layer through possible layers of hidden notes to the output layer. It is then shown that such neural networks can implement mapping functions. Mapping neural representations are important in many brain processes and have dominated models in cognitive science in the form of multilayer perceptrons. For this it is important to explore the effects of choosing appropriate values for synaptic weights through learning algorithms. While feedforward networks are not enough to explain cognitive functions alone, they are an important ingredient of brain-style information processing and have contributed greatly to our understanding of adaptive systems. This chapter includes some review about concepts of machine learning and more recent developments such as deep learning. Such techniques are becoming increasingly important for industrial applications as well as analysing neuroscience data. However, we will largely focus on their relation to brain-style information processing.
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Ryman-Tubb, Nick F. "Neural-Symbolic Processing in Business Applications." In Computational Neuroscience for Advancing Artificial Intelligence, 270–314. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-021-1.ch012.
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Neural networks are mathematical models, inspired by biological processes in the human brain and are able to give computers more “human-like” abilities. Perhaps by examining the way in which the biological brain operates, at both the large-scale and the lower level anatomical level, approaches can be devised that can embody some of these remarkable abilities for use in real-world business applications. One criticism of the neural network approach by business is that they are “black boxes”; they cannot be easily understood. To open this black box an outline of neural-symbolic rule extraction is described and its application to fraud-detection is given. Current practice is to build a Fraud Management System (FMS) based on rules created by fraud experts which is an expensive and time-consuming task and fails to address the problem where the data and relationships change over time. By using a neural network to learn to detect fraud and then extracting its’ knowledge, a new approach is presented.
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Kraišniković, Ceca, Wolfgang Maass, and Robert Legenstein. "Chapter 9. Spike-Based Symbolic Computations on Bit Strings and Numbers." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210356.
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The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small spiking neural networks can perform symbolic computations on bit sequences and numbers and even learn such computations using a biologically plausible learning rule. The resulting networks operate in a rather low firing rate regime, where they could not simply emulate artificial neural networks by encoding continuous values through firing rates. Thus, we propose here a new paradigm for symbolic computation in neural networks that provides concrete hypotheses about the organization of symbolic computations in the brain. The employed spike-based network models are the basis for drastically more energy-efficient computer hardware – neuromorphic hardware. Hence, our results can be seen as creating a bridge from symbolic artificial intelligence to energy-efficient implementation in spike-based neuromorphic hardware.
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Burguière, Eric, and Luc Mallet. "Basic mechanisms of, and treatment planning/targets for, obsessive–compulsive disorder." In New Oxford Textbook of Psychiatry, edited by John R. Geddes, Nancy C. Andreasen, and Guy M. Goodwin, 976–86. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198713005.003.0094.
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Despite the range of conventional treatments available for obsessive–compulsive disorders, generally based on pharmacological and behavioural therapy, a significant number of patients receive no benefit from them. Clearly, further work is required to develop alternative therapeutic approaches to improve the treatment of the dysfunctional cognitive processes in obsessive–compulsive disorders and to better understand the neural networks involved. Some innovative tools have recently been developed in the fields of anatomical and functional imaging, neuromodulation, and animal models. These novel approaches offer opportunities to improve our understanding of the functional and pathophysiological basis of obsessive–compulsive disorders.
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Paret, Christian, and Christian Schmahl. "Imaging of personality disorders." In New Oxford Textbook of Psychiatry, edited by John R. Geddes, Nancy C. Andreasen, and Guy M. Goodwin, 1239–46. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198713005.003.0121.
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Altered processing of emotion, cognition, social interactions, and behavioural responding is characteristic for personality disorders. Neuroimaging has contributed to a better understanding of biological markers of inflexible and maladaptive behaviours prevalent in personality disorders. This chapter summarizes results from magnetic resonance imaging and electroencephalography studies, with a focus on research in borderline personality disorder (BPD). Knowledge on neural processes underlying emotion processing and emotion regulation has significantly advanced. Overall, findings corroborate dysfunctions in limbic–prefrontal networks, emphasizing hyper-responsiveness of the amygdala and a lack of prefrontal control. Neuroimaging studies addressed several domains of cognitive functions in BPD and shed light on the processing of social information in the brain. Though profound achievements have been made, comprehensive neurobiological models addressing behavioural dysregulation, impulsivity, and abnormal social interaction in BPD are still pending.
Conference papers on the topic "Mathematical Models of Cognitive Processes and Neural Networks"
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Sun, Qinhua Jenny, Khuong Vo, Kitty Lui, Michael Nunez, Joachim Vandekerckhove, and Ramesh Srinivasan. "Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892272.
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Vermisso, Emmanouil. "Fragmented Layers of Design Thinking: Limitations and Opportunities of Neural Language Model-assisted processes for Design Creativity." In Design Computation Input/Output 2022. Design Computation, 2022. http://dx.doi.org/10.47330/dcio.2022.mmlw2640.
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This paper offers insights about the otherwise limited NLM-driven methodologies, supporting an examination of design creativity following the ‘process’ approach. [Abraham 2018] Recent application of AI models which rely on natural language processing (semantic references) is increasingly popular because of their directness and ease-of-use. Neural Language Models (NLMs) like VQGAN+CLIP, DALL-E, MidJourney) offer promising results, [Rodrigues, et al. 2021] seemingly bypassing the need for expensive datasets and technical expertise. Naturally, such models are limited because they cannot capture the multimodal complexity of architectural thinking and human cognition in general [Penrose 1989]. Alternative approaches propose the combination of NLMs with other artificial neural networks (ANNs) i.e. StyleGAN; CycleGAN which are custom-trained on domain-specific data. [Bolojan, Vermisso and Yousif 2022] Architects seek to expand their agency within such AI-assisted processes by controling the input encoding, so they can subsequently convert the generated outcomes to 3D models fairly directly. Still, AI models of computer vision like NLMs and GANs offer 2-dimensional output, which requires extensive decoding into 3-dimensional format. While this may seem severely constraining, it presents a silver lining when it comes to furthering design creativity. Designers are asked to scrutinize their methods from a cognitive standpoint, because these methodologies not only encourage, but demand thorough interrogation of the design intentionality, the design decision making factors and qualification criteria. Text-to-image correlation, on which NLMs rely, and their 2-dimensional output, ensure that certain important considerations are not circumvented. Instead of obtaining a 3D model, multiple possible -fragmented- versions of it are separately implied. Often, ‘fake’ images generated by the ANNs promote contradictory inferences of space, which require further examination. The hidden opportunity within the limited format of AI models echo Neil Spiller’s comments about the advantage of drawing over animation techniques twenty years ago: “Enigma is a creative tool that allows designers to see bifurcated outcomes in their sketches and drawings; it plays on the inability of drawings to faithfully record the distinct placement and extent of architectural elements”. [Spiller 2001] Comparing animations to static drawings, Spiller praised the drawing’s ability to hold “…an imagined past and an imagined future”. ‘Reading’ these results involves the (human) disentanglement of high and low-level features and consciously allocating their corresponding qualities for curation. The process of evaluating ‘parts-to-whole’ visual relationships is noteworthy because it depends on shifting our attention away from certain features, and an unconscious binding of visual elements. [Dehaene 2014] The philosopher Alain wrote that “The art of paying attention, the great art,…supposes the art of not paying attention…the royal art”. [Dehaene 2021]. According to neuroscientists, the brain uses attention as an amplifier and selective filter, during one of the three major attention systems (Alerting; Orienting; Executive Attention). [Dehaene 2021] Orienting our attention addresses what we focus on and what we don’t. Suppressing the unwanted information, through interfering electrical waves, is useful for processing the object of attention. Considering the ANNs’ results at ‘Gestalt’ level, we can structure the AI-assisted process to ensure low-level features (composition) is retained while enhancing high-level (detail) features (Fig.1a).
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Khan, Mohammad Rasheed, Shams Kalam, Abdul Asad, Rizwan Ahmed Khan, and Muhammad Shahzad Kamal. "Intelligent Predictor for Polymer Viscosity to Enhance Support for EOR Processes." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204839-ms.
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Abstract Research into the use of polymers for enhanced oil recovery (EOR) processes has been going on for more than 6 decades and is now classified as a techno-commercially viable option. A comprehensive evaluation of the polymer's rheology is pivotal to the success of any polymer EOR process. Laboratory-based evaluation is critical to EOR success; however, it is also a time/capital consuming process. Consequently, any tool which can aid in optimizing lab tests design can bring in great value. Accordingly, in this study a novel predictive correlation for viscosity estimation of commonly used "FP 3330S" EOR polymer is presented through use of cutting-edge machine learning neural networks. Mathematical equation for polymer viscosity is developed using machine learning algorithms as a function of polymer concentration, NaCl concentration, and Ca2+ concentration. The measured input data was collected from the literature and sub-divided into training and test sets. A wide-ranging optimization was performed to select the best parameters for the neural network which includes the number of neurons, neuron layers, activation functions between multiple layers, weights, and bias. Furthermore, the Levenberg-Marquardt back-propagation algorithm was utilized to train the model. Finally, measured and estimated viscosities were compared based on error-analysis. Novel correlation is developed for the polymer that can be used in predictive mode. This established correlation can predict polymer viscosity when applied to the test dataset and outperforms other published models with average error in the range of 3-5% and coefficient of determination in excess of 0.95. Moreover, it is shown that neural networks are faster and relatively better than other machine learning algorithms explored in this study. The proposed correlation can map non-linear relationships between polymer viscosity and other rheological parameters such as molecular weight, polymer concentration, and cation concentration of polymer solution. Lastly, through machine learning validation approach, it was possible to examine feasibility of the proposed models which is not done by traditional empirical equations.
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Fei, Dingzhou. "Revisiting the correlation between video game activity and working memory: evidence from machine learning." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002083.
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With the popularity of video games, more and more researchers are trying to understand the relationship between video game activity and cognitive abilities, and one of the important cognitive systems is working memory. Working memory is a limited capacity short-term memory system for processing currently active information and is an important predictor of goal-driven behavioral domains. Its scope of action includes, but is not limited to, fluid intelligence, verbal ability, and mathematical analysis.Due to the importance of working memory for the analysis of human behavior, numerous studies have attempted to describe the architecture and models of working memory. In general, models of working memory can be loosely categorized into content and process models, depending on their focus. The content model focuses on the static material of working memory, which includes mainly verbal and spatial visual material. The process model focuses on the dynamic processes of working memory and includes both Updating and Maintenance of memory.However, this area of research has also been the subject of debate among researchers.Translated with www.DeepL.com/Translator (free version). These disputes involve two main assumptions. According to the so-called core training hypothesis, a potential machine for improving cognitive ability through video games is provided by the so-called core training hypothesis. According to this hypothesis, repeated stress on the cognitive system will induce plastic changes in its neural matrix, leading to improved cognitive response performance. According to this hypothesis, repeated strains of the cognitive system can induce plastic changes in its neural matrix, which is an important reason for the improvement of performance. The other proposed basic mechanism is the meta-learning mechanism, that is, learning how to learn. According to this, video games (especially action games) can improve related motor control skills, such as rule learning, cognitive resource allocation, and probabilistic reasoning skills, which are used in many different situations.A recent study showed that the analysis of certain extreme groups showed that video game players performed better than non-game players in all three WM measurements, and that when extended to the entire sample, video game time and visual space WM and n-back performance. In general, the relationship between cognition and playing video games is very weak.This study used the Waris et al, 2019 dataset to re-investigate the correlation between video game activity and three different dimensions of working memory using seven different supervising learning models. It was concluded that video game activity was most highly correlated with the visuospatial component, slightly less correlated with the mnemonic updating component, and least correlated with the verbal component. This partly confirms Waris et al, 2019's view that the analytic method may be the key to the study.
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Huang, Xiang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Min Wang, et al. "A Universal PINNs Method for Solving Partial Differential Equations with a Point Source." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/533.
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In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems. PDEs with a point source that is expressed as a Dirac delta function in the governing equations are mathematical models of many physical processes. However, they cannot be solved directly by conventional PINNs method due to the singularity brought by the Dirac delta function. In this paper, we propose a universal solution to tackle this problem by proposing three novel techniques. Firstly the Dirac delta function is modeled as a continuous probability density function to eliminate the singularity at the point source; secondly a lower bound constrained uncertainty weighting algorithm is proposed to balance the physics-informed loss terms of point source area and the remaining areas; and thirdly a multi-scale deep neural network with periodic activation function is used to improve the accuracy and convergence speed. We evaluate the proposed method with three representative PDEs, and the experimental results show that our method outperforms existing deep learning based methods with respect to the accuracy, the efficiency and the versatility.
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Sottsau, Aliaksei, Ramir Akbashev, Alexandr Peratsiahin, and Vadim Garnaev. "Modern Solution for Oil Well Multiphase Flows Water Cut Metering." In SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206475-ms.
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Abstract An innovative technology for determining the water cut in well products (without preliminary separation into liquid and gas fractions) uses the results of electrical impedance measurements and its dependence on the alternating current frequency. Water cut meter's sensor includes measuring and current electrodes, between which there is a well's multiphase flow. Imaginary and real components of the impedance quantitatively describe the component composition of the studied oil and gas-water mixtures. In this process, machine learning methods and developed algorithms for features extraction are used. Depending on the type of emulsion, two independent sensors are used in the oil pipeline, one of which measures in a direct emulsion, the other in an inverse emulsion. Tests of the described water cut meter on flow loops in the Russian Federation and in the Netherlands, as well as studies of well flows in oil production facilities in the Russian Federation and the Kingdom of Saudi Arabia, have shown high measurement accuracy in the full range of water cut, with high gas content, as well as at high salinity and in a wide range of flow rates. To do so, modern methods of data classification based on neural networks and regression modeling implemented using machine learning are employed. It was found that the flow rates of liquid and gas do not affect the results of measuring the water cut due to the high frequency of the impedance measurements - up to 100 thousand measurements per second. Use of in-line multiphase water cut meter makes it possible to apply intelligent methods of processing field information and accumulate statistical data for each well, as a big data element for predicting and modeling in-situ processes. It will also allow to introduce promising production processes aimed at increasing oil production and monitoring the baseline indicators of the well. Novelty of the presented technology: Solution of the problem of high-speed determination of water cut in a multiphase flow without preliminary separation using impedance metering. Creation of mathematical models of multiphase flow and methods for determining the type of flow and the type of emulsion. Machine learning methods and neural networks employment for high-speed analysis of flow changes. Development, successful testing and implementation of an affordable multiphase water cut meter of our own design, which has no analogs in industrial applications.
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Gasser, Moamen, Omar Mahmoud, Fatma Ibrahim, and Magdi Abadir. "Using Artificial Intelligence Techniques in Modeling and Predicting the Rheological Behavior of Nano-Based Drilling Fluids." In ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/omae2021-63749.
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Abstract Drilling process is one of the main operations in the extraction of hydrocarbons from petroleum reservoirs. It comes right after the exploration processes. Drilling fluids are necessary for controlling the wells and performing different functions during the drilling operation. They perform many roles in lifting the cuttings from the bottom of the well to the surface and cooling/lubricating the drill pipes and bit. Furthermore, they provide the desired hydrostatic pressure to overbalance pore pressure in addition to produce a thin/impermeable filter cake that can prevent or reduce the possible damage to the formations. It is mandatory to keep monitoring, enhancing, and optimizing the properties of the drilling fluids. Recently, different additives, among which nanoparticles (NPs), have been investigated to improve, and maximize the benefits of the drilling fluids accordingly to meet the new challenges. The rheological behavior of such complex fluids has shown different enhancements up on the utilization of those additives. The rheological properties of the drilling fluids are accurately measured on the surface; however, the behavior of those properties may change with time and under harsh drilling conditions, such as high pressure/high temperature environments. For that, different models are introduced and used to predict and optimize the rheological characteristics of such fluids. Bingham, Herschel-Bulkley, Power Law, Casson and others are commonly used as rheological models to predict the drilling fluid behavior. In the last decade, a new trend of developing new models and correlations using the artificial neural networks (ANN) have been introduced to the petroleum field. Mathematical formulas can be developed using ANN, which then can be used to predict the behavior of certain parameter(s) by knowing other ones. Using ANN have shown to be more reliable and accurate in predicting the rheological properties of the drilling fluids, such as apparent viscosity (AV), plastic viscosity (PV), yield point (YP), maximum shear stress, and change in the mud density at various conditions. This work aims at using ANN technique to develop suitable models that can predict the rheological behavior of nano-based drilling fluids. The effect of NPs-type, -size, -concentration, and drilling fluid formulations will be considered, which may pave the road for new applications and efficient utilizations.