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Auteur : Grafiati

Publié le 11 septembre 2023

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1

Gerstner, Wulfram. Spiking neuron models : Single neurons, populations, plasticity. Cambridge, U.K : Cambridge University Press, 2002.

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2

Eeckman, Frank H. Computation in neurons and neural systems. New York : Springer, 1994.

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3

H, Eeckman Frank, et Conference on Computation and Neural Systems (1993 : Washington, D.C.), dir. Computation in neurons and neural systems. Boston : Kluwer Academic Publishers, 1994.

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4

Fellin, Tommaso, et Michael Halassa, dir. Neuronal Network Analysis. Totowa, NJ : Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-633-3.

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1931-, Taylor John Gerald, et Mannion C. L. T, dir. Coupled oscillating neurons. London : Springer-Verlag, 1992.

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6

Lek, Sovan, et Jean-François Guégan, dir. Artificial Neuronal Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57030-8.

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7

Lek, Sovan. Artificial Neuronal Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2000.

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8

Aizenberg, Igor. Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20353-4.

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9

service), SpringerLink (Online, dir. Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.

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10

G, Stein Paul S., dir. Neurons, networks, and motor behavior. Cambridge, Mass : MIT Press, 1997.

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11

Momoka, Yoshida, et Sato Haruka, dir. New research on neuronal networks. New York : Nova Science Publishers, 2008.

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12

Neurons and networks : An introduction to neuroscience. Cambridge, Mass : Belknap Press of Harvard University Press, 1992.

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13

Les réseaux de neurones. Grenoble : Presses universitaires de Grenoble, 1994.

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14

Koch, Christof. Biophysics of computation : Information processing in single neurons. New York : Oxford University Press, 1999.

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15

Haycock, R. J. Hardware neurons and synapses for pulse stream neural networks. Manchester : UMIST, 1997.

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16

Chiappalone, Michela, Valentina Pasquale et Monica Frega, dir. In Vitro Neuronal Networks. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11135-9.

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17

Neurons and networks : An introduction to behavioral neuroscience. 2^e éd. Cambridge, Mass : Belknap Press of Harvard University Press, 2001.

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18

Neural networks : A comprehensive foundation. New York : Macmillan, 1994.

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19

Neural networks : A comprehensive foundation. 2^e éd. Upper Saddle River, N.J : Prentice Hall, 1999.

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20

Dayhoff, Judith E. Neural network architectures : An introduction. New York, N.Y : Van Nostrand Reinhold, 1990.

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21

Howard, Eichenbaum, et Davis Joel L. 1942-, dir. Neuronal ensembles : Strategies for recording and decoding. New York : Wiley-Liss, 1998.

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22

1940-, Krüger Jürgen, dir. Neuronal cooperativity. Berlin : Springer-Verlag, 1991.

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23

Josić, Kres̆imir. Coherent behavior in neuronal networks. New York : Springer, 2009.

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24

Josic, Kre¿imir, Jonathan Rubin, Manuel Matias et Ranulfo Romo, dir. Coherent Behavior in Neuronal Networks. New York, NY : Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0389-1.

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25

1951-, Miles Richard, dir. Neuronal networks of the hippocampus. Cambridge : Cambridge University Press, 1991.

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26

1956-, Koch Christof, et Segev Idan, dir. Methods in neuronal modeling : From synapses to networks. Cambridge, Mass : MIT Press, 1989.

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27

Rojas, Raúl. Neural networks : A systematic introduction. Berlin : Springer-Verlag, 1996.

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28

Christian, Hölscher, et Munk Matthias, dir. Information processing by neuronal populations. Cambridge : Cambridge University Press, 2009.

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29

Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

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30

Dienel, Samuel J., et David A. Lewis. Cellular Mechanisms of Psychotic Disorders. Sous la direction de Dennis S. Charney, Eric J. Nestler, Pamela Sklar et Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0018.

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Cognitive dysfunction in schizophrenia, including disturbances in working memory, is a core feature of the illness and the best predictor of long-term functional outcome. Working memory relies on neural network oscillations in the prefrontal cortex. Gamma-aminobutyric acid (GABA) neurons in the prefrontal cortex, which are crucial for this oscillatory activity, exhibit a number of alterations in individuals diagnosed with schizophrenia. These GABA neuron disturbances may be secondary to upstream alterations in excitatory pyramidal cells in the prefrontal cortex. Together, these findings suggest both a neural substrate for working memory impairments in schizophrenia and therapeutic targets for improving functional outcomes in this patient population.

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31

Gerstner, Wulfram, et Werner M. Kistler. Spiking Neuron Models : Single Neurons, Populations, Plasticity. Cambridge University Press, 2002.

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32

Gerstner, Wulfram, et Werner M. Kistler. Spiking Neuron Models : Single Neurons, Populations, Plasticity. Cambridge University Press, 2012.

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33

Gerstner, Wulfram, Werner M. Kistler, Richard Naud et Liam Paninski. Neuronal Dynamics : From Single Neurons to Networks and Models of Cognition. Cambridge University Press, 2014.

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34

Gerstner, Wulfram, Werner M. Kistler, Richard Naud et Liam Paninski. Neuronal Dynamics : From Single Neurons to Networks and Models of Cognition. Cambridge University Press, 2014.

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35

Neuronal Dynamics : From Single Neurons to Networks and Models of Cognition. Cambridge University Press, 2014.

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36

Neuronal Network Research Horizons. Nova Science Publishers, 2007.

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37

Jef ferys, John G. R. Cortical activity : single cell, cell assemblages, and networks. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0004.

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This chapter describes how the activity of neurons produces electrical potentials that can be recorded at the levels of single cells, small groups of neurons, and larger neuronal networks. It outlines how the movement of ions across neuronal membranes produces action potentials and synaptic potentials. It considers how the spatial arrangement of specific ion channels on the neuronal surface can produce potentials that can be recorded from the extracellular space. Finally, it outlines how the layered cellular structure of the neocortex can result in summation of signals from many neurons to be large enough to record through the scalp as evoked potentials or the electroencephalogram.

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38

Vassanelli, Stefano. Implantable neural interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0050.

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Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

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39

Selverston, Allen. Rhythms and oscillations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0021.

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The study of identifiable neurons, a common feature of invertebrate nervous systems, has made it possible to construct a detailed cell-to-cell connectivity map using electrophysiological methods that can inspire the design of biomimetic systems. This chapter describes how the analysis of the neural circuitry in the lobster stomatogastric ganglion (STG) has provided some general principles underlying oscillatory and rhythmic behavior in all animals. The rhythmic and oscillatory patterns produced by the two STG central pattern generating (CPG) circuits are a result of two cooperative mechanisms, intrinsically bursting pacemaker neurons and synaptic network properties. Also covered are the major neuromodulatory and neural control mechanisms. The chapter discusses how a deep knowledge of the stomatogastric circuitry has led to the development of electronic neurons for biomimetic devices that can be used for experimental and prosthetic applications The chapter concludes with a section on new techniques that may help with unraveling oscillatory circuits in the brain.

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40

Carnevale, Nicholas T., et Michael L. Hines. NEURON Book. Cambridge University Press, 2005.

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41

Carnevale, Nicholas T., et Michael L. Hines. Neuron Book. Cambridge University Press, 2006.

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42

Carnevale, Nicholas T., et Michael L. Hines. The NEURON Book. Cambridge University Press, 2006.

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43

M, McKenna Thomas, Davis Joel L. 1942- et Zornetzer Steven F, dir. Single neuron computation. Boston : Academic Press, 1992.

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44

Davis, Joel L., Thomas M. McKenna et Steven F. Zornetzer. Single Neuron Computation. Elsevier Science & Technology Books, 2014.

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45

Frost, William, et Jian-young Wu. Voltage-Sensitive Dye Imaging. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0008.

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Voltage sensitive dye imaging (VSD) can be used to record neural activity in hundreds of locations in preparations ranging from mammalian cortex to invertebrate ganglia. Because fast VSDs respond to membrane potential changes with microsecond temporal resolution, these are better suited than calcium indicators for recording rapid neural signals. Here we describe methods for using a 464- element photodiode array and fast VSDs to record signals ranging from large scale network activity in brain slices and in vivo mammalian preparations, to action potentials in over 100 individual neurons in invertebrate ganglia.

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46

Carnevale, Nicholas T., et Michael L. Hines. NEURON Book. Cambridge University Press, 2009.

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47

Neural modeling and neural networks. Oxford [England] : Pergamon Press, 1994.

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48

Busch, Allan Einar. Mean field theory of disordered neural networks with multiple neuron interactions. 1988.

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49

Aizenberg, Igor. Complex-Valued Neural Networks with Multi-Valued Neurons. Springer Berlin / Heidelberg, 2016.

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50

Complex-Valued Neural Networks with Multi-Valued Neurons. Springer, 2012.

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