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Artificial neural network model for river flow forecasting
Artificial neural network model for river flow forecasting

... rainfall. These models differ in terms of the additional external inputs being used by the model. The additional inputs are basically the seasonal expectations of both the rainfall index and the observed discharge. The results show that the model, which uses both the seasonal expectation of the obse ...
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the phase-space dynamics of systems of spiking neurons
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Chapter 15 - McGraw Hill Higher Education
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reflex
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Bill Greenough`s research career
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... To account for these seemingly disparate effects of experience during development, Greenough proposed the replacement of the critical and sensitive period conceptualizations with those of Experience-Expectant and Experience-Dependent information storage arising from contact with the environment. Th ...
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Nervous system network models

Network of human nervous system comprises nodes (for example, neurons) that are connected by links (for example, synapses). The connectivity may be viewed anatomically, functionally, or electrophysiologically. These are presented in several Wikipedia articles that include Connectionism (a.k.a. Parallel Distributed Processing (PDP)), Biological neural network, Artificial neural network (a.k.a. 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 (technically neuronal network based on neuron model). Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic (ion and neuron), mesoscopic (functional or population), or macroscopic (system) levels. Computational modeling refers to models that are developed using computing tools.
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