Relevant bibliographies by topics / Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)

Academic literature on the topic 'Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)"

1

Gerstner, Wulfram, Henning Sprekeler, and Gustavo Deco. "Theory and Simulation in Neuroscience." Science 338, no. 6103 (October 4, 2012): 60–65. http://dx.doi.org/10.1126/science.1227356.

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Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in order to understand their properties. The second criterion is that of direction of workflow, which can be from microscopic to macroscopic scales (bottom-up) or from behavioral target functions to properties of components (top-down). We review the interaction of theory and simulation using examples of top-down and bottom-up studies and point to some current developments in the fields of computational and theoretical neuroscience.
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Medaglia, John D., Mary-Ellen Lynall, and Danielle S. Bassett. "Cognitive Network Neuroscience." Journal of Cognitive Neuroscience 27, no. 8 (August 2015): 1471–91. http://dx.doi.org/10.1162/jocn_a_00810.

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Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.
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KOCH, PAUL, and GERALD LEISMAN. "Numbers, models, and understanding of natural intelligence: Computational neuroscience in the service of clinical neuropsychology." Journal of the International Neuropsychological Society 6, no. 5 (July 2000): 580–82. http://dx.doi.org/10.1017/s1355617700655078.

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What we call computational neuroscience involves construction of mathematical and numerical models for understanding cognitive phenomena. This issue is devoted to showing how it can also be used to help in the analysis of cognitive defects. Although the models may seem abstract to clinicians, they are based on the reality of brain anatomy. The theoretical papers presented here are connectionist: They posit a network of cells connected by synapses whose weights are modified during learning. Architecture of connectionist models has progressed and ramified considerably since they were first introduced, and we include some examples of the current state of the art. The final work presented here is concerned with the connection of the constructed models with clinical experience and experiment.
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Fellous, Jean-Marc, and Christiane Linster. "Computational Models of Neuromodulation." Neural Computation 10, no. 4 (May 1, 1998): 771–805. http://dx.doi.org/10.1162/089976698300017476.

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Computational modeling of neural substrates provides an excellent theoretical framework for the understanding of the computational roles of neuromodulation. In this review, we illustrate, with a large number of modeling studies, the specific computations performed by neuromodulation in the context of various neural models of invertebrate and vertebrate preparations. We base our characterization of neuromodulations on their computational and functional roles rather than on anatomical or chemical criteria. We review the main framework in which neuromodulation has been studied theoretically (central pattern generation and oscillations, sensory processing, memory and information integration). Finally, we present a detailed mathematical overview of how neuromodulation has been implemented at the single cell and network levels in modeling studies. Overall, neuromodulation is found to increase and control computational complexity.
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Poznanski, Roman R. "Book Review: "Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems", P. Dayan and L. F. Abbott, eds., (2001)." Journal of Integrative Neuroscience 05, no. 03 (September 2006): 489–91. http://dx.doi.org/10.1142/s0219635206001197.

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Vyas, Saurabh, Matthew D. Golub, David Sussillo, and Krishna V. Shenoy. "Computation Through Neural Population Dynamics." Annual Review of Neuroscience 43, no. 1 (July 8, 2020): 249–75. http://dx.doi.org/10.1146/annurev-neuro-092619-094115.

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Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.
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Georgopoulos, Apostolos P. "Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Computational Neuroscience. By Peter Dayan and , L F Abbott. Cambridge (Massachusetts): MIT Press. $50.00. xv + 460 p; ill.; index. ISBN: 0–262–04199–5. 2001." Quarterly Review of Biology 79, no. 1 (March 2004): 113. http://dx.doi.org/10.1086/421681.

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Nieus, Thierry, Elisabetta Sola, Jonathan Mapelli, Elena Saftenku, Paola Rossi, and Egidio D'Angelo. "LTP Regulates Burst Initiation and Frequency at Mossy Fiber–Granule Cell Synapses of Rat Cerebellum: Experimental Observations and Theoretical Predictions." Journal of Neurophysiology 95, no. 2 (February 2006): 686–99. http://dx.doi.org/10.1152/jn.00696.2005.

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Long-term potentiation (LTP) is a synaptic change supposed to provide the cellular basis for learning and memory in brain neuronal circuits. Although specific LTP expression mechanisms could be critical to determine the dynamics of repetitive neurotransmission, this important issue remained largely unexplored. In this paper, we have performed whole cell patch-clamp recordings of mossy fiber–granule cell LTP in acute rat cerebellar slices and studied its computational implications with a mathematical model. During LTP, stimulation with short impulse trains at 100 Hz revealed earlier initiation of granule cell spike bursts and a smaller nonsignificant spike frequency increase. In voltage-clamp recordings, short AMPA excitatory postsynaptic current (EPSC) trains showed short-term facilitation and depression and a sustained component probably generated by spillover. During LTP, facilitation disappeared, depression accelerated, and the sustained current increased. The N-methyl-d-aspartate (NMDA) current also increased. In agreement with a presynaptic expression caused by increased release probability, similar changes were observed by raising extracellular [Ca2+]. A mathematical model of mossy fiber–granule cell neurotransmission showed that increasing release probability efficiently modulated the first-spike delay. Glutamate spillover, by causing tonic NMDA and AMPA receptor activation, accelerated excitatory postsynaptic potential (EPSP) temporal summation and maintained a sustained spike discharge. The effect of increasing neurotransmitter release could not be replicated by increasing receptor conductance, which, like postsynaptic manipulations enhancing intrinsic excitability, proved very effective in raising granule cell output frequency. Independent regulation of spike burst initiation and frequency during LTP may provide mechanisms for temporal recoding and gain control of afferent signals at the input stage of cerebellar cortex.
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Engbert, Ralf, and Reinhold Kliegl. "The game of word skipping: Who are the competitors?" Behavioral and Brain Sciences 26, no. 4 (August 2003): 481–82. http://dx.doi.org/10.1017/s0140525x03270102.

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Computational models such as E-Z Reader and SWIFT are ideal theoretical tools to test quantitatively our current understanding of eye-movement control in reading. Here we present a mathematical analysis of word skipping in the E-Z Reader model by semianalytic methods, to highlight the differences in current modeling approaches. In E-Z Reader, the word identification system must outperform the oculomotor system to induce word skipping. In SWIFT, there is competition among words to be selected as a saccade target. We conclude that it is the question of competitors in the “game” of word skipping that must be solved in eye movement research.
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Geisler, Caroline, Nicolas Brunel, and Xiao-Jing Wang. "Contributions of Intrinsic Membrane Dynamics to Fast Network Oscillations With Irregular Neuronal Discharges." Journal of Neurophysiology 94, no. 6 (December 2005): 4344–61. http://dx.doi.org/10.1152/jn.00510.2004.

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During fast oscillations in the local field potential (40–100 Hz gamma, 100–200 Hz sharp-wave ripples) single cortical neurons typically fire irregularly at rates that are much lower than the oscillation frequency. Recent computational studies have provided a mathematical description of such fast oscillations, using the leaky integrate-and-fire (LIF) neuron model. Here, we extend this theoretical framework to populations of more realistic Hodgkin–Huxley-type conductance-based neurons. In a noisy network of GABAergic neurons that are connected randomly and sparsely by chemical synapses, coherent oscillations emerge with a frequency that depends sensitively on the single cell's membrane dynamics. The population frequency can be predicted analytically from the synaptic time constants and the preferred phase of discharge during the oscillatory cycle of a single cell subjected to noisy sinusoidal input. The latter depends significantly on the single cell's membrane properties and can be understood in the context of the simplified exponential integrate-and-fire (EIF) neuron. We find that 200-Hz oscillations can be generated, provided the effective input conductance of single cells is large, so that the single neuron's phase shift is sufficiently small. In a two-population network of excitatory pyramidal cells and inhibitory neurons, recurrent excitation can either decrease or increase the population rhythmic frequency, depending on whether in a neuron the excitatory synaptic current follows or precedes the inhibitory synaptic current in an oscillatory cycle. Detailed single-cell properties have a substantial impact on population oscillations, even though rhythmicity does not originate from pacemaker neurons and is an emergent network phenomenon.
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Books on the topic "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)"

1

F, Abbott L., ed. Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, Mass: Massachusetts Institute of Technology Press, 2001.

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Abbott, L. F., and Peter Dayan. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 2005.

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Abbott, L. F., and Peter Dayan. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 2001.

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(Editor), Idan Segev, John Rinzel (Editor), and Gordon M. Shepherd (Editor), eds. The Theoretical Foundations of Dendritic Function: The Collected Papers of Wilfrid Rall with Commentaries (Computational Neuroscience). The MIT Press, 1994.

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National Institute of Mental Health (U.S.), ed. Department of Health and Human Services, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute of Mental Health (NIMH) and National Institutes of Health, National Institute of Neurological and Communicative Disorders and Stroke (NINCDS) program announcement: Mathematical/computational/theoretical neuroscience. [Rockville, Md: National Institute of Mental Health, 1988.

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6

Cohen Kadosh, Roi, and Ann Dowker, eds. The Oxford Handbook of Numerical Cognition. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.001.0001.

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This book provides a comprehensive overview of numerical cognition by bringing together writing by leading researchers in psychology, neuroscience, and education, covering work using different methodological approaches in humans and animals. During the last decade there had been an explosion of studies and new findings with theoretical and translational implications. This progress has been made thanks to technological advances enabling sophisticated human neuroimaging techniques and neurophysiological studies of monkeys, and to advances in more traditional psychological and educational research. This has resulted in an enormous advance in our understanding of the neural and cognitive mechanisms of numerical cognition. In addition, there has recently been increasing interest and concern about pupils' mathematical achievement, resulting in attempts to use research to guide mathematics instruction in schools, and to develop interventions for children with mathematical difficulties. This book aims to provide a broad and extensive review of the field of numerical cognition, bringing together work from varied areas. The book covers research on important aspects of numerical cognition, involving findings from the areas of developmental psychology, cognitive psychology, human and animal neuroscience, computational modeling, neuropsychology and rehabilitation, learning disabilities education and individual differences, cross-cultural and cross-linguistic studies, and philosophy. It also includes an overview 'navigator' chapter for each section to provide a brief up-to-date review of the current literature, and to introduce and integrate the topics of the chapters in the section.
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Book chapters on the topic "Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)"

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Roccasalvo, Iolanda Morana, Silvestro Micera, and Pier Nicola Sergi. "Chemotactic Guidance of Growth Cones: A Hybrid Computational Model." In Mathematical and Theoretical Neuroscience, 45–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68297-6_3.

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Venkadesh, Siva, and Giorgio A. Ascoli. "Computational Modeling as a Means to Defining Neuronal Spike Pattern Behaviors." In Mathematical and Theoretical Neuroscience, 25–43. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68297-6_2.

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Epstein, Joshua M., and Julia Chelen. "Advancing Agent_Zero." In Complexity and Evolution. The MIT Press, 2016. http://dx.doi.org/10.7551/mitpress/9780262035385.003.0016.

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Agent_Zero is a mathematical and computational individual that can generate important, but insufficiently understood, social dynamics from the bottom up. First published by Epstein (2013), this new theoretical entity possesses emotional, deliberative, and social modules, each grounded in contemporary neuroscience. Agent_Zero’s observable behavior results from the interaction of these internal modules. When multiple Agent_Zeros interact with one another, a wide range of important, even disturbing, collective dynamics emerge. These dynamics are not straightforwardly generated using the canonical rational actor which has dominated mathematical social science since the 1940s. Following a concise exposition of the Agent_Zero model, this chapter offers a range of fertile research directions, including the use of realistic geographies and population levels, the exploration of new internal modules and new interactions among them, the development of formal axioms for modular agents, empirical testing, the replication of historical episodes, and practical applications. These may all serve to advance the Agent_Zero research program.
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Plerou, Antonia, and Panayiotis Vlamos. "Evaluation of Mathematical Cognitive Functions with the Use of EEG Brain Imaging." In Advances in Multimedia and Interactive Technologies, 284–306. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8659-5.ch014.

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During the last decades, the interest displayed in neurocognitive and brain science research is relatively high. In this chapter, the cognitive neuroscience field approach focuses in the aspect of the way that cognitive functions are produced by neural circuits in the brain. Within this frame, the effects of impairment to the brain and subsequent changes in the thought processes due to changes in neural circuitry resulting from the ensued damage are analyzed and evaluated. All cognitive functions result from the integration of many simple processing mechanisms, distributed throughout the brain. Brain cortex structures, linked with cognitive disorders, are located in several parts like the frontal, the parietal, the temporal, the occipital lobe and more are analyzed and specified. A critical topic of this chapter in the evaluation of brain operations is mapping regions that control cognitive and mathematical concepts functions. Dyscalculia, in this chapter, is described as a specific disorder of managing and conceiving mathematical concepts. Dyscalculia could be identified by difficulties in visual perception, in spatial number organization, in basic mathematical operations and in mathematical induction logic. Moreover, people who deal with dyscalculia present problems, in Euclidean and Non-Euclidean Geometry concepts perception, in Calculus aspects as well as in solving algorithmic problems where the design, the description and the application of algorithmic steps are required. In order to enhance cognitive brain functions perception, the use of EEG brain imaging is proposed measuring cerebral activity and event-related potentials. The procedure described in this chapter is about the comparison and contrasts EEG brain imaging patterns of healthy volunteers to EEG samples taken of adults considered being at risk of mathematics learning disabilities such as Dyscalculia and algorithmic thinking difficulties. EEG interpretation analysis is to follow where the deviation of a normal and an abnormal range of wave's frequency are defined. Several visualized EEG patterns in relevance with specific abnormalities are presented while several neurocognitive generated disorders could be identified with the use of EEG Brain-imaging technique. The electroencephalogram EEG brain imaging procedure, in order to evaluate problems associated with brain function, is to be further analyzed in this chapter as well. The EEG is the depiction of the electrical activity occurring at the surface of the brain. The recorded waveforms reflect the cortical electrical activity and they are generally classified according to their frequency (Delta, Theta, Beta, Alpha, Beta, and Gamma) amplitude, and shape. EEG Implementation with the use of 10/20 system of the standardized position of scalp electrodes placement for a classical EEG recording is described as well. The EEG implementation objective is to identify, classify and evaluate those frequencies and regions in the brain that best characterize brain activity associated with mathematical learning disabilities. Mapping the brain with non-invasive techniques based on trigger and sensing/evaluation experimental multimedia methods similar to those used in computer games and applications are expected to provide relevant results in order to enhance and confirm theoretical cognitive aspects. At that point, a cognitive and mathematical perception evaluation is to follow and specifically the assessment of the relation of difficulties in mathematics with particular parts of the human brain. EEG wave data visualization is contacted with the use of Acknowledge an interactive, intuitive program which provides data analysis instantly. At the end of this chapter EEG computational evaluation with the use of pattern recognition methods as well as the intuition of author's future work in relevance with the use of experimental multimedia technologies to enhance the dynamic recognition and evaluation of user cognitive responses during EEG implementation are noted.
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