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Livros sobre o tema "Substrat neuronal"

Autor: Grafiati

Publicado: 15 de março de 2025

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1

Petrovici, Mihai Alexandru. Form Versus Function: Theory and Models for Neuronal Substrates. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39552-4.

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2

Hendrik, Gispen Willem, e Routtenberg Aryeh, eds. Protein kinase C and its brain substrates: Role in neuronal growth and plasticity : proceedings of the Third International Meeting on Brain Phosphoproteins, held at Zeist (The Netherlands) 24-26 August, 1990. Amsterdam: Elsevier, 1991.

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3

International Meeting on Brain Phosphoproteins (3rd 1990 Zeist, Netherlands). Protein kinaseC and its brain substrates: Role in neuronal growth and plasticity : proceedings of the Third International Meeting on Brain Phosphoproteins held at Zeist (The Netherlands), 24-26 August, 1990. Amsterdam: Elsevier, 1991.

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4

Steriade, Mircea. Neuronal Substrates of Sleep and Epilepsy. Cambridge University Press, 2009.

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5

Steriade, Mircea. Neuronal Substrates of Sleep and Epilepsy. Cambridge University Press, 2005.

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6

Steriade, Mircea. Neuronal Substrates of Sleep and Epilepsy. Cambridge University Press, 2003.

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7

Steriade, Mircea. Neuronal Substrates of Sleep and Epilepsy. Cambridge University Press, 2003.

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8

Steriade, Mircea. Neuronal Substrates of Sleep and Epilepsy. Cambridge University Press, 2003.

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9

Petrovici, Mihai Alexandru. Form Versus Function: Theory and Models for Neuronal Substrates. Springer, 2018.

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10

Petrovici, Mihai Alexandru. Form Versus Function: Theory and Models for Neuronal Substrates. Springer International Publishing AG, 2016.

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11

Petrovici, Mihai Alexandru. Form Versus Function: Theory and Models for Neuronal Substrates. Springer London, Limited, 2016.

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12

Nieder, Andreas. Neuronal Correlates of Non-verbal Numerical Competence in Primates. Editado por Roi Cohen Kadosh e Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.027.

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Non-verbal numerical competence, such as the estimation of set size, is rooted in biological primitives that can also be explored in animals. Over the past years, the anatomical substrates and neuronal mechanisms of numerical cognition in primates have been unravelled down to the level of single neurons. Studies with behaviourally-trained monkeys have identified a parietofrontal network of individual neurons selectively tuned to the number of items (cardinal aspect) or the rank of items in a sequence (ordinal aspect). The properties of these neurons’ numerosity tuning curves can explain fundamental psychophysical phenomena, such as the numerical distance and size effect. Functionally overlapping groups of parietal neurons represent not only numerable-discrete quantity (numerosity), but also innumerable-continuous quantity (extent) and relations between quantities (proportions), supporting the idea of a generalized magnitude system in the brain. Moreover, many neurons in the prefrontal cortex establish semantic associations between signs and abstract numerical categories, a neuronal precursor mechanisms that may ultimately give rise to symbolic number processing in humans. These studies establish putative homologies between the monkey and human brain, and demonstrate the suitability of non-human primates as model system to explore the neurobiological roots of the brain’s non-verbal quantification system, which may constitute the phylogenetic and ontogenetic foundation of all further, more elaborate numerical skills in humans.

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13

Protein Kinase C and Its Substrates: Role in Neuronal Growth and Plasticity. Elsevier Science & Technology Books, 1991.

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14

Protein kinase C and its brain substrates: Role in neuronal growth and plasticity. Amsterdam: Elsevier Science, 1991.

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15

Dienel, Samuel J., e David A. Lewis. Cellular Mechanisms of Psychotic Disorders. Editado por Dennis S. Charney, Eric J. Nestler, Pamela Sklar e 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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16

Amzica, Florin, e Fernando H. Lopes da Silva. Cellular Substrates of Brain Rhythms. Editado por Donald L. Schomer e Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0002.

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The purpose of this chapter is to familiarize the reader with the basic electrical patterns of the electroencephalogram (EEG). Brain cells (mainly neurons and glia) are organized in multiple levels of intricate networks. The cellular membranes are semipermeable media between extracellular and intracellular solutions, populated by ions and other electrically charged molecules. This represents the basis of electrical currents flowing across cellular membranes, further generating electromagnetic fields that radiate to the scalp electrodes, which record changes in the activity of brain cells. This chapter presents these concepts together with the mechanisms of building up the EEG signal. The chapter discusses the various behavioral conditions and neurophysiological mechanisms that modulate the activity of cells leading to the most common EEG patterns, such as the cellular interactions for alpha, beta, gamma, slow, delta, and theta oscillations, DC shifts, and some particular waveforms such as sleep spindles and K-complexes and nu-complexes.

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17

Campagnola, Luke, e Paul Manis. Patch Clamp Recording in Brain Slices. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0001.

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Patch clamp recording in brain slices allows unparalleled access to neuronal membrane signals in a system that approximates the in-vivo neural substrate while affording greater control of experimental conditions. In this chapter we discuss the theory, methodology, and practical considerations of such experiments including the initial setup, techniques for preparing and handling viable brain slices, and patching and recording signals. A number of practical and technical issues faced by electrophysiologists are also considered, including maintaining slice viability, visualizing and identifying healthy cells, acquiring reliable patch seals, amplifier compensation features, hardware configuration, sources of electrical noise and table vibration, as well as basic data analysis issues and some troubleshooting tips.

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18

Berkowitz, Aaron L. The Cognitive Neuroscience of Improvisation. Editado por George E. Lewis e Benjamin Piekut. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780195370935.013.004.

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Cognitive neuroscience research has begun to elucidate the neural substrates and cognitive processes that are involved in musical improvisation. In turn, the study of improvisation from the perspective of cognitive neuroscience has provided new insights about the brain and cognition. This chapter reviews brain imaging research studies of improvisation and explores the relevance of this work to musicians, musicologists, music educators, and cognitive neuroscientists with respect to the practice and pedagogy of improvisation, comparisons between music and language cognition, mirror neuron systems, and neural plasticity.

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19

Erdem, Uğur Murat, Nicholas Roy, John J. Leonard e Michael E. Hasselmo. Spatial and episodic memory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0029.

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The neuroscience of spatial memory is one of the most promising areas for developing biomimetic solutions to complex engineering challenges. Grid cells are neurons recorded in the medial entorhinal cortex that fire when rats are in an array of locations in the environment falling on the vertices of tightly packed equilateral triangles. Grid cells suggest an exciting new approach for enhancing robot simultaneous localization and mapping (SLAM) in changing environments and could provide a common map for situational awareness between human and robotic teammates. Current models of grid cells are well suited to robotics, as they utilize input from self-motion and sensory flow similar to inertial sensors and visual odometry in robots. Computational models, supported by in vivo neural activity data, demonstrate how grid cell representations could provide a substrate for goal-directed behavior using hierarchical forward planning that finds novel shortcut trajectories in changing environments.

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20

Gaetz, Michael B., e Kelly J. Jantzen. Electroencephalography. Editado por Ruben Echemendia e 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 functional impairment related to sport concussion. Elevations in delta and theta activity can be expected in a percentage of athletes and change in asymmetry and coherence may also be present. Newer techniques are likely to be of critical importance for understanding the anatomical and physiological basis of cognitive deficits and may provide additional insight into susceptibility to future injury. Computational modeling may advance our understanding of concussion.

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21

Klimecki, Olga M., e Tania Singer. The Compassionate Brain. Editado por Emma M. Seppälä, Emiliana Simon-Thomas, Stephanie L. Brown, Monica C. Worline, C. Daryl Cameron e James R. Doty. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190464684.013.9.

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This chapter focuses on the neuroscience of compassion and related social emotions such as empathy, empathic concern, or empathic distress. First, we review neuroscientific literature on empathy and relate empathy to similar social emotions. We then turn to neuroscientific research on caregiving and social connection before describing cross-sectional studies on the neural signatures of compassion. To investigate whether training of compassion can change neural functions, the neural “fingerprints” of compassion expertise were studied in both expert and inexperienced meditators. The latter included the comparison between functional plasticity induced by empathy for suffering as opposed to compassion training. These studies show that compassion training changes neural functions, and that the neural substrates related to empathy for suffering differ experientially as well as neuronally. This is in line with the observation of distinct behavioral patterns related to feelings of empathic distress and compassion, described towards the end of the chapter.

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