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Books on the topic 'Neurons - Modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

Pacut, Andrzej. Stochastic modeling at diverse scales: From Poisson to network neurons. Oficyna Wydawnicza Politechniki Warszawskiej, 2000.

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2

R, Baylog Louis, ed. Dendritic spines biochemistry, modeling and properties. Nova Science Publishers, 2009.

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3

Christof, Koch, and Segev Idan, eds. Methods in neuronal modeling: From synapses to networks. MIT Press, 1992.

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4

An introduction to the mathematics of neurons: Modeling in the frequency domain. 2nd ed. Cambridge University Press, 1997.

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5

1956-, Koch Christof, and Segev Idan, eds. Methods in neuronal modeling: From synapses to networks. MIT Press, 1989.

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6

Beňušková, L̕. Computational neurogenetic modeling. Springer, 2007.

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7

N, Reeke George, ed. Modeling in the neurosciences: From biological systems to neuromimetic robotics. 2nd ed. Taylor & Francis, 2005.

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8

1956-, Koch Christof, and Segev Idan, eds. Methods in neuronal modeling: From ions to networks. 2nd ed. MIT Press, 1998.

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9

R, Poznanski Roman, ed. Modeling in the neurosciences: From ionic channels to neural networks. Harwood Academic Publishers, 1999.

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10

Börgers, Christoph. An Introduction to Modeling Neuronal Dynamics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51171-9.

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11

Grum, Marcus. Construction of a Concept of Neuronal Modeling. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-35999-7.

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12

Tatsuno, Masami, ed. Analysis and Modeling of Coordinated Multi-neuronal Activity. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1969-7.

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13

Mondaini, Rubem P., ed. Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33050-6.

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14

Kandror, Elena. Modeling the Transcriptional Landscape of in vitro Neuronal Differentiation and ALS Disease. [publisher not identified], 2019.

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15

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24406-8.

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16

G, Burdet, Combe Philippe 1940-, and Parodi O, eds. Neuronal information processing: From biological data to modelling and applications : Cargèse, France, 30 June-12 July 1997. World Scientific, 1999.

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17

Palmeri, Thomas J., Jeffrey D. Schall, and Gordon D. Logan. Neurocognitive Modeling of Perceptual Decision Making. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.15.

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Mathematical psychology and systems neuroscience have converged on stochastic accumulator models to explain decision making. We examined saccade decisions in monkeys while neurophysiological recordings were made within their frontal eye field. Accumulator models were tested on how well they fit response probabilities and distributions of response times to make saccades. We connected these models with neurophysiology. To test the hypothesis that visually responsive neurons represented perceptual evidence driving accumulation, we replaced perceptual processing time and drift rate parameters with

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18

Segev, Idan, and Christof Koch. Methods in Neuronal Modeling: From Ions to Networks. MIT Press, 1998.

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19

Koch, Christof. Methods in Neuronal Modeling: From Synapses to Networks (Computational Neuroscience). Bradford Book, 1989.

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20

Neural modeling and neural networks. Pergamon Press, 1994.

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21

Benuskova, Lubica, and Nikola K. Kasabov. Computational Neurogenetic Modeling. Springer, 2010.

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22

Tatsuno, Masami. Analysis and Modeling of Coordinated Multi-neuronal Activity. Springer, 2016.

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23

Tatsuno, Masami. Analysis and Modeling of Coordinated Multi-Neuronal Activity. Springer, 2014.

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24

Tatsuno, Masami. Analysis and Modeling of Coordinated Multi-neuronal Activity. Springer, 2014.

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25

Sporns, Olaf, Roman R. Poznanski, and Kenneth A. Lindsay. Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics. Taylor & Francis Group, 2005.

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26

Lindsay, K. A., G. N. Reeke, R. R. Poznanski, J. R. Rosenberg, and O. Sporns. Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics. Taylor & Francis Group, 2005.

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27

Lindsay, K. A., G. N. Reeke, R. R. Poznanski, J. R. Rosenberg, and O. Sporns. Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics. Taylor & Francis Group, 2005.

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28

Lindsay, K. A., G. N. Reeke, R. R. Poznanski, J. R. Rosenberg, and O. Sporns. Modeling in the Neurosciences: From Biological Systems to Neuromimetic Robotics. Taylor & Francis Group, 2005.

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29

Benuskova, Lubica, and Nikola Kasabov. Computational Neurogenetic Modeling (Topics in Biomedical Engineering. International Book Series). Springer, 2007.

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30

(Editor), Christof Koch, and Idan Segev (Editor), eds. Methods in Neuronal Modeling - 2nd Edition: From Ions to Networks (Computational Neuroscience). 2nd ed. The MIT Press, 1998.

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31

Gillies, Andrew, David Willshaw, David Sterratt, and Bruce Graham. Principles of Neuronal Modelling. Cambridge University Press, 2008.

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32

Börgers, Christoph. An Introduction to Modeling Neuronal Dynamics. Springer, 2017.

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33

Börgers, Christoph. An Introduction to Modeling Neuronal Dynamics. Springer, 2018.

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34

Grum, Marus. Construction of a Concept of Neuronal Modeling. Springer Fachmedien Wiesbaden GmbH, 2022.

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35

Methods in Neuronal Modeling: From Synapses to Networks. Bradford Books, 1991.

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36

Ditlevsen, Susanne, Jerry J. Batzel, and Mostafa Bachar. Stochastic Biomathematical Models: With Applications to Neuronal Modeling. Springer, 2012.

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37

Gaetz, Michael B., and Kelly J. Jantzen. Electroencephalography. Edited by Ruben Echemendia and Grant L. Iverson. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199896585.013.006.

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Axonal injury is currently considered to be the structural substrate behind most concussion-related neurological dysfunction. Because the principal generators of EEG fields are graded excitatory and inhibitory synaptic potentials of pyramidal neurons, the EEG is well suited for characterizing large-scale functional disruptions associated with concussion induced metabolic and neurochemical changes, and for connecting those disruptions to deficits in behavior and cognition. This essay provides an overview of the use of EEG and newly developed analytical procedures for the measurement of function

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38

Samin, Azfar. Neuronal modelling of baroreflex response to orthostatic stress. 2005.

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39

Paganoni, Sabrina, and Nazem Atassi. Upper Motor Neuron Disorders Hereditary Spastic Paraplegia and Primary Lateral Sclerosis. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0032.

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Upper motor neuron (UMN) syndromes are a group of rare, degenerative neurological disorders that are classified as either hereditary spastic paraplegia (HSP) or primary lateral sclerosis (PLS). Our understanding of their underlying pathophysiology is unfortunately very limited and has been a significant barrier to the development of disease-modifying treatments. Recent advances in genetics and in vitro and in vivo disease modeling have provided new insights into disease mechanisms and hold the promise to lead to the future development of mechanism-based therapies.

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40

Bongard, Josh. Modeling self and others. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0011.

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Embodied cognition is the view that intelligence arises out of the interaction between an agent’s body and its environment. Taking such a view generates novel scientific hypotheses about biological intelligence and opportunities for advancing artificial intelligence. In this chapter we review one such set of hypotheses regarding how a robot may generate models of self, and others, and then exploit those models to recover from damage or exhibit the rudiments of social cognition. This modeling of self and others draws mainly on three concepts from neuroscience and AI: forward and inverse models

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41

Ballard, Dana H. Brain Computation As Hierarchical Abstraction. MIT Press, 2015.

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42

Sejnowski, Terrence J., Dana H. Ballard, and Tomaso A. Poggio. Brain Computation As Hierarchical Abstraction. MIT Press, 2015.

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43

Ballard, Dana H. Brain Computation As Hierarchical Abstraction. MIT Press, 2015.

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44

Sejnowski, Terrence J., Dana H. Ballard, and Tomaso A. Poggio. Brain Computation As Hierarchical Abstraction. MIT Press, 2015.

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45

Brain Computation as Hierarchical Abstraction. The MIT Press, 2015.

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46

Wendling, Fabrice, and Fernando H. Lopes da Silva. Dynamics of EEGs as Signals of Neuronal Populations. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0003.

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This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generatin

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47

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer London, Limited, 2015.

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48

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer, 2015.

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49

Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Springer International Publishing AG, 2016.

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50

Neufeld, Richard W. J. Mathematical and Computational Modeling in Clinical Psychology. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.16.

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This chapter begins with an introduction to the basic ideas behind clinical mathematical and computational modeling. In general, models of normal cognitive-behavioral functioning are titrated to accommodate performance deviations accompanying psychopathology; model features remaining intact indicate functions that are spared; those that are perturbed are triaged as signifying functions that are disorder affected. Distinctions and interrelations among forms of modeling in clinical science and assessment are stipulated, with an emphasis on analytical, mathematical modeling. Preliminary conceptua

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