Results 251 to 260 of about 6,198,061 (298)
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Model of psychological functioning
Journal of Theoretical Biology, 1971Abstract A model of psychological functioning, based on the activity of individual neurons, is proposed. A method to identify general patterns of behavior is outlined. A biochemical mechanism to account for synaptic transmission and modification of behavior by experience is suggested and incorporated in a holographic model of memory.
D, Irving, P, Proctor
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A model for the functioning of the striatum
Biological Cybernetics, 1993A model is presented for the operation of the striatum. The model posits that the basal ganglia are responsible for driving smooth transitions of state for an organism. We propose that this is accomplished through the computation of a potential function within the striatum on which a gradient descent is performed toward the goal state.
Christopher I. Connolly, J. Brian Burns
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2002
The modeling and simulation of physical systems is of key importance in many areas of science and engineering, and thus can benefit from high-quality software tools. In previous research we have demonstrated how functional programming can form the basis of an expressive language for causal hybrid modeling and simulation. There is a growing realization,
Henrik Nilsson +2 more
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The modeling and simulation of physical systems is of key importance in many areas of science and engineering, and thus can benefit from high-quality software tools. In previous research we have demonstrated how functional programming can form the basis of an expressive language for causal hybrid modeling and simulation. There is a growing realization,
Henrik Nilsson +2 more
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The nature and function of models
Behavioral and Brain Sciences, 2001There is no best scientific model of anything; there are only models more or less good for different purposes. Thus, there is no general answer to the question of whether one should model biological behavior using computer simulations or robots. It all depends on what one wants to learn. This is not a question about models, but about scientific goals.
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Modeling mitochondrial function
American Journal of Physiology-Cell Physiology, 2006The mitochondrion represents a unique opportunity to apply mathematical modeling to a complex biological system. Understanding mitochondrial function and control is important since this organelle is critical in energy metabolism as well as playing key roles in biochemical synthesis, redox control/signaling, and apoptosis.
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A Functional Model for the Ribosome
Journal of Theoretical Biology, 1997The "ice" structure for the E. coli ribosome clearly illustrates how the ribosome could work and it is used as the basis of a detailed account of ribosome function. There have been objections to this structure as it does not fit with current beliefs about the position of the T-site, for the arriving tRNA, or the Exit-site: these beliefs are wrong.
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A model of the functional psychoses
Acta Psychiatrica Scandinavica, 1980A model of the functional psychoses is described which accounts for their existence, answers certain questions about them and generates refutable predictions.
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Models Are Functioning in Scenarios
2020Models are one of the universal instruments of humans. They are instruments that are used on purpose in scenarios. They are usable and useful within these scenarios. The scenario determines the function and thus the purpose of a model. This determination governs the acceptance in dependence of the utility that a model provides.
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A Functional-Anatomical Model for Lipreading
Journal of Neurophysiology, 2003Regional cerebral blood flow (rCBF) PET scans were used to study the physiological bases of lipreading, a natural skill of extracting language from mouth movements, which contributes to speech perception in everyday life. Viewing connected mouth movements that could not be lexically identified and that evoke perception of isolated speech sounds ...
PAULESU, ERALDO +7 more
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Linear mixed function‐on‐function regression models
Biometrics, 2014SummaryWe develop a linear mixed regression model where both the response and the predictor are functions. Model parameters are estimated by maximizing the log likelihood via the ECME algorithm. The estimated variance parameters or covariance matrices are shown to be positive or positive definite at each iteration.
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