Relevant bibliographies by topics / Computational neuroimaging, cognitive neuroscience

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computational neuroimaging, cognitive neuroscience'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Computational neuroimaging, cognitive 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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McIntosh, Randy, Sean Hill, and Olaf Sporns. "Editorial: Focus feature on consciousness and cognition." Network Neuroscience 6, no. 4 (2022): 934–36. http://dx.doi.org/10.1162/netn_e_00273.

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Abstract Consciousness and cognition are an increasing focus of theoretical and experimental research in neuroscience, leveraging the methods and tools of brain dynamics and connectivity. This Focus Feature brings together a collection of articles that examine the various roles of brain networks in computational and dynamic models, and in studies of physiological and neuroimaging processes that underpin and enable behavioral and cognitive function.
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Rudrauf, David. "Structure-Function Relationships behind the Phenomenon of Cognitive Resilience in Neurology: Insights for Neuroscience and Medicine." Advances in Neuroscience 2014 (August 4, 2014): 1–28. http://dx.doi.org/10.1155/2014/462765.

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The phenomenon of cognitive resilience, that is, the dynamical preservation of normal functions despite neurological disorders, demonstrates that cognition can be highly robust to devastating brain injury. Here, cognitive resilience is considered across a range of neurological conditions. Simple computational models of structure-function relationships are used to discuss hypotheses about the neural mechanisms of resilience. Resilience expresses functional redundancies in brain networks and suggests a process of dynamic rerouting of brain signals. This process is underlined by a global renormalization of effective connectivity, capable of restoring information transfer between spared brain structures via alternate pathways. Local mechanisms of synaptic plasticity mediate the renormalization at the lowest level of implementation, but it is also driven by top-down cognition, with a key role of self-awareness in fostering resilience. The presence of abstraction layers in brain computation and networking is hypothesized to account for the renormalization process. Future research directions and challenges are discussed regarding the understanding and control of resilience based on multimodal neuroimaging and computational neuroscience. The study of resilience will illuminate ways by which the brain can overcome adversity and help inform prevention and treatment strategies. It is relevant to combating the negative neuropsychological impact of aging and fostering cognitive enhancement.
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Nadel, L., A. Samsonovich, L. Ryan, and M. Moscovitch. "Multiple trace theory of human memory: Computational, neuroimaging, and neuropsychological results." Hippocampus 10, no. 4 (2000): 352–68. http://dx.doi.org/10.1002/1098-1063(2000)10:4<352::aid-hipo2>3.0.co;2-d.

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Zmigrod, Leor, and Manos Tsakiris. "Computational and neurocognitive approaches to the political brain: key insights and future avenues for political neuroscience." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1822 (February 22, 2021): 20200130. http://dx.doi.org/10.1098/rstb.2020.0130.

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Although the study of political behaviour has been traditionally restricted to the social sciences, new advances in political neuroscience and computational cognitive science highlight that the biological sciences can offer crucial insights into the roots of ideological thought and action. Echoing the dazzling diversity of human ideologies, this theme issue seeks to reflect the multiplicity of theoretical and methodological approaches to understanding the nature of the political brain. Cutting-edge research along three thematic strands is presented, including (i) computational approaches that zoom in on fine-grained mechanisms underlying political behaviour, (ii) neurocognitive perspectives that harness neuroimaging and psychophysiological techniques to study ideological processes, and (iii) behavioural studies and policy-minded analyses of such understandings across cultures and across ideological domains. Synthesizing these findings together, the issue elucidates core questions regarding the nature of uncertainty in political cognition, the mechanisms of social influence and the cognitive structure of ideological beliefs. This offers key directions for future biologically grounded research as well as a guiding map for citizens, psychologists and policymakers traversing the uneven landscape of modern polarization, misinformation, intolerance and dogmatism. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms'.
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Popal, Haroon, Yin Wang, and Ingrid R. Olson. "A Guide to Representational Similarity Analysis for Social Neuroscience." Social Cognitive and Affective Neuroscience 14, no. 11 (November 1, 2019): 1243–53. http://dx.doi.org/10.1093/scan/nsz099.

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Abstract Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. In the context of neuroimaging, mass-univariate analyses and other multivariate analyses can provide information on what and where information is represented but have limitations in their ability to address how information is represented. Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict behavior, how representations differ between groups and how multimodal data can be compared to inform theories. The goal of this paper is to provide a practical as well as theoretical guide to implementing RSA in social neuroscience studies.
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Chatham, Christopher H., Seth A. Herd, Angela M. Brant, Thomas E. Hazy, Akira Miyake, Randy O'Reilly, and Naomi P. Friedman. "From an Executive Network to Executive Control: A Computational Model of the n-back Task." Journal of Cognitive Neuroscience 23, no. 11 (November 2011): 3598–619. http://dx.doi.org/10.1162/jocn_a_00047.

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A paradigmatic test of executive control, the n-back task, is known to recruit a widely distributed parietal, frontal, and striatal “executive network,” and is thought to require an equally wide array of executive functions. The mapping of functions onto substrates in such a complex task presents a significant challenge to any theoretical framework for executive control. To address this challenge, we developed a biologically constrained model of the n-back task that emergently develops the ability to appropriately gate, bind, and maintain information in working memory in the course of learning to perform the task. Furthermore, the model is sensitive to proactive interference in ways that match findings from neuroimaging and shows a U-shaped performance curve after manipulation of prefrontal dopaminergic mechanisms similar to that observed in studies of genetic polymorphisms and pharmacological manipulations. Our model represents a formal computational link between anatomical, functional neuroimaging, genetic, behavioral, and theoretical levels of analysis in the study of executive control. In addition, the model specifies one way in which the pFC, BG, parietal, and sensory cortices may learn to cooperate and give rise to executive control.
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Mujica-Parodi, Lilianne R., and Helmut H. Strey. "Making Sense of Computational Psychiatry." International Journal of Neuropsychopharmacology 23, no. 5 (March 27, 2020): 339–47. http://dx.doi.org/10.1093/ijnp/pyaa013.

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Abstract In psychiatry we often speak of constructing “models.” Here we try to make sense of what such a claim might mean, starting with the most fundamental question: “What is (and isn’t) a model?” We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building—suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.
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Parkinson, Carolyn. "Computational methods in social neuroscience: recent advances, new tools and future directions." Social Cognitive and Affective Neuroscience 16, no. 8 (June 24, 2021): 739–44. http://dx.doi.org/10.1093/scan/nsab073.

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Abstract Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized—many of which contain instructive materials (e.g. tutorials and code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments—at the levels of stimuli, paradigms and the webs of social relationships that surround people—with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand and navigate their complex social worlds.
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Rojek-Giffin, Michael, Mael Lebreton, H. Steven Scholte, Frans van Winden, K. Richard Ridderinkhof, and Carsten K. W. De Dreu. "Neurocognitive Underpinnings of Aggressive Predation in Economic Contests." Journal of Cognitive Neuroscience 32, no. 7 (July 2020): 1276–88. http://dx.doi.org/10.1162/jocn_a_01545.

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Competitions are part and parcel of daily life and require people to invest time and energy to gain advantage over others and to avoid (the risk of) falling behind. Whereas the behavioral mechanisms underlying competition are well documented, its neurocognitive underpinnings remain poorly understood. We addressed this using neuroimaging and computational modeling of individual investment decisions aimed at exploiting one's counterpart (“attack”) or at protecting against exploitation by one's counterpart (“defense”). Analyses revealed that during attack relative to defense (i) individuals invest less and are less successful; (ii) computations of expected reward are strategically more sophisticated (reasoning level k = 4 vs. k = 3 during defense); (iii) ventral striatum activity tracks reward prediction errors; (iv) risk prediction errors were not correlated with neural activity in either ROI or whole-brain analyses; and (v) successful exploitation correlated with neural activity in the bilateral ventral striatum, left OFC, left anterior insula, left TPJ, and lateral occipital cortex. We conclude that, in economic contests, coming out ahead (vs. not falling behind) involves sophisticated strategic reasoning that engages both reward and value computation areas and areas associated with theory of mind.
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Dissertations / Theses on the topic "Computational neuroimaging, cognitive neuroscience"

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Salimi-Khorshidi, Gholamreza. "Statistical models for neuroimaging meta-analytic inference." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:40a10327-7f36-42e7-8120-ae04bd8be1d4.

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A statistical meta-analysis combines the results of several studies that address a set of related research hypotheses, thus increasing the power and reliability of the inference. Meta-analytic methods are over 50 years old and play an important role in science; pooling evidence from many trials to provide answers that any one trial would have insufficient samples to address. On the other hand, the number of neuroimaging studies is growing dramatically, with many of these publications containing conflicting results, or being based on only a small number of subjects. Hence there has been increasing interest in using meta-analysis methods to find consistent results for a specific functional task, or for predicting the results of a study that has not been performed directly. Current state of neuroimaging meta-analysis is limited to coordinate-based meta-analysis (CBMA), i.e., using only the coordinates of activation peaks that are reported by a group of studies, in order to "localize" the brain regions that respond to a certain type of stimulus. This class of meta-analysis suffers from a series of problems and hence cannot result in as accurate results as desired. In this research, we describe the problems that existing CBMA methods are suffering from and introduce a hierarchical mixed-effects image-based metaanalysis (IBMA) solution that incorporates the sufficient statistics (i.e., voxel-wise effect size and its associated uncertainty) from each study. In order to improve the statistical-inference stage of our proposed IBMA method, we introduce a nonparametric technique that is capable of adjusting such an inference for spatial nonstationarity. Given that in common practice, neuroimaging studies rarely provide the full image data, in an attempt to improve the existing CBMA techniques we introduce a fully automatic model-based approach that employs Gaussian-process regression (GPR) for estimating the meta-analytic statistic image from its corresponding sparse and noisy observations (i.e., the collected foci). To conclude, we introduce a new way to approach neuroimaging meta-analysis that enables the analysis to result in information such as “functional connectivity” and networks of the brain regions’ interactions, rather than just localizing the functions.
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Cooke, Megan E. "Integrating Genetics and Neuroimaging to study Subtypes of Binge Drinkers." VCU Scholars Compass, 2017. https://scholarscompass.vcu.edu/etd/5167.

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Risky alcohol use is a major health concern among college students, with 40.1% reporting binge drinking (5 or more drinks in one occasion) and 14.4% reporting heavy drinking (binge drinking on 5 or more occasions) in the past month. Risky alcohol use is thought to be the result of a complex interplay between genes, biological processes, and other phenotypic characteristics. Understanding this complex relationship is further complicated by known phenotypic heterogeneity in the development of alcohol use. Developmental studies have suggested two pathways to risky alcohol use, characterized by externalizing and internalizing characteristics, respectively. However, the underlying biological processes that differentiate these pathways are not fully understood. Neuroimaging studies have assessed reward sensitivity, emotion reactivity, and behavioral inhibition using fMRI and separately demonstrate associations in externalizing and internalizing disorders more broadly. In addition, previous genetic studies have found associations between specific polymorphisms and these externalizing and internalizing subtypes. Therefore, we sought further characterize the biological influences on binge drinking subtypes through the following specific aims: 1) determine the genetic relationship between externalizing and internalizing characteristics in binge drinkers, 2) test whether externalizing and internalizing binge drinkers show differences in brain activation in response to tasks measuring emotion reactivity, reward sensitivity, and behavioral inhibition. In order to achieve these aims, we conducted a series of genetic analyses assessing differences in overall SNP-based heritability and specific associated variants between the externalizing and internalizing subtypes. There were a few variants that reached genome-wide significance, the most notable being a cluster of SNPs associated with internalizing characteristics that were located in the RP3AL gene. In a subset of these binge drinking young adults, brain activation was measured on tasks assessing behavioral inhibition, reward sensitivity, and emotion reactivity. We found some preliminary differences with regard to emotion reactivity, that suggest internalizing binge drinkers are more reactive to faces overall but have blunted reaction to sad faces compared to externalizers. These findings provide an initial step to better understanding the underlying biology between the classic externalizing and internalizing alcohol use subtypes, which has the potential to elucidate new subtype specific targets for prevention and intervention.
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Cronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008.
Includes bibliographical references (p. 101-106).
Two key fields of computational neuroscience involve, respectively, the analysis of experimental recordings to understand the functional properties of neurons, and modeling how neurons and networks process sensory information in order to represent the environment. In both of these endeavors, it is crucial to understand and quantify uncertainty - when describing how the brain itself draws conclusions about the physical world, and when the experimenter interprets neuronal data. Bayesian modeling and inference methods provide many advantages for doing so. Three projects are presented that illustrate the advantages of the Bayesian approach. In the first, Markov chain Monte Carlo (MCMC) sampling methods were used to answer a range of scientific questions that arise in the analysis of physiological data from tuning curve experiments; in addition, a software toolbox is described that makes these methods widely accessible. In the second project, the model developed in the first project was extended to describe the detailed dynamics of orientation tuning in neurons in cat primary visual cortex. Using more sophisticated sampling-based inference methods, this model was applied to answer specific scientific questions about the tuning properties of a recorded population. The final project uses a Bayesian model to provide a normative explanation of sensory adaptation phenomena. The model was able to explain a range of detailed physiological adaptation phenomena.
by Beau D. Cronin.
Ph.D.
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Lundh, Dan. "A computational neuroscientific model for short-term memory." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324742.

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Vellmer, Sebastian. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21597.

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In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron], Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden. Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert. IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pulsverhalten oft nicht beschreiben und müssen dazu erweitert werden. Im erste Teil dieser Arbeit wird eine Theorie zur Berechnung der Pulszugleistungsspektren von stochastischen, multidimensionalen IF Neuronen entwickelt. Ausgehend von der zugehörigen Fokker-Planck-Gleichung werden partiellen Differentialgleichung abgeleitet, deren Lösung sowohl die stationäre Wahrscheinlichkeitsverteilung und Feuerrate, als auch das Pulszugleistungsspektrum beschreibt. Im zweiten Teil wird eine Theorie für große, spärlich verbundene und homogene Netzwerke aus IF Neuronen entwickelt, in der berücksichtigt wird, dass die zeitlichen Korrelationen von Pulszügen selbstkonsistent sind. Neuronale Eingangströme werden durch farbiges Gaußsches Rauschen modelliert, das von einem mehrdimensionalen Ornstein-Uhlenbeck Prozess (OUP) erzeugt wird. Die Koeffizienten des OUP sind vorerst unbekannt und sind als Lösung der Theorie definiert. Um heterogene Netzwerke zu untersuchen, wird eine iterative Methode erweitert. Im dritten Teil wird die Fokker-Planck-Gleichung auf Binärentscheidungen von Diffusionsentscheidungsmodellen [Engl. diffusion-decision models (DDM)] angewendet. Explizite Gleichungen für die Entscheidungszugstatistiken werden für den einfachsten und analytisch lösbaren Fall von der Fokker-Planck-Gleichung hergeleitet. Für nichtliniear Modelle wird die Schwellwertintegrationsmethode erweitert.
This thesis is concerned with the calculation of statistics, in particular the power spectra, of point processes generated by stochastic multidimensional integrate-and-fire (IF) neurons, networks of IF neurons and decision-making models from the corresponding Fokker-Planck equations. In the brain, information is encoded by sequences of action potentials. In studies that focus on spike timing, IF neurons that drastically simplify the spike generation have become the standard model. One-dimensional IF neurons do not suffice to accurately model neural dynamics, however, the extension towards multiple dimensions yields realistic behavior at the price of growing complexity. The first part of this work develops a theory of spike-train power spectra for stochastic, multidimensional IF neurons. From the Fokker-Planck equation, a set of partial differential equations is derived that describes the stationary probability density, the firing rate and the spike-train power spectrum. In the second part of this work, a mean-field theory of large and sparsely connected homogeneous networks of spiking neurons is developed that takes into account the self-consistent temporal correlations of spike trains. Neural input is approximated by colored Gaussian noise generated by a multidimensional Ornstein-Uhlenbeck process of which the coefficients are initially unknown but determined by the self-consistency condition and define the solution of the theory. To explore heterogeneous networks, an iterative scheme is extended to determine the distribution of spectra. In the third part, the Fokker-Planck equation is applied to calculate the statistics of sequences of binary decisions from diffusion-decision models (DDM). For the analytically tractable DDM, the statistics are calculated from the corresponding Fokker-Planck equation. To determine the statistics for nonlinear models, the threshold-integration method is generalized.
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Cattinelli, I. "INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155482.

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This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems.
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Petitet, Pierre. "Sensorimotor adaptation : mechanisms, modulation and rehabilitation potential." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:5935d96d-625a-4778-b42d-bb56c96d96cc.

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Adaptation is a fundamental property of the nervous system that underlies the maintenance of successful actions through flexible reconfiguration of sensorimotor processing. The primary aims of this thesis are 1) to investigate the computational and neural underpinnings of sensorimotor memory formation during prism adaptation (PA) in humans, and 2) how they interact with anodal transcranial direct current stimulation (a-tDCS) of the primary motor cortex (M1), in order to 3) improve efficacy of prism therapy for post-stroke spatial neglect. In chapter 4, we modify an influential state-space model of adaptation in order to characterize the contribution of short and long memory timescales to motor behaviour as sensorimotor after-effects (AEs) develop during PA. This enables us, in the multimodal 7 Tesla MRI experiment reported in chapter 5, to demonstrate that the level of M1 excitation:inhibition causally sets the relative contribution of long versus short memory timescales during PA, thus determining behavioural persistence of the AE at retention in young healthy adults. This finding offers a bridge between different levels of investigation by providing a biologically plausible neuro-computational model of how sensorimotor memories are formed and enhanced by a-tDCS. In chapter 6, we use the ageing motor system as a model of reduced GABAergic inhibition and show that the age-related decrease in M1 GABA explains why older adults demonstrate more persistent prism AEs. Taken together, these data indicate that the reduction in M1 GABAergic inhibition via excitatory a-tDCS during PA has the potential to enhance persistence of adaptation memory in both young and older adults. Informed by these results, we subsequently ask whether standard (multi-session) PA therapy combined with left M1 a-tDCS translates to greater and/or longer-lasting clinical improvements in post-stroke spatial neglect patients. In chapter 7, we compare the multimodal neuroimaging data of six neglect patients to normative data of age-matched controls. We show that in all patients, the lesion interrupted long-range frontoparietal connections, and we provide direct evidence for a pathological left dominance of activity within the lateral occipital cortex during deployment of bilateral visuospatial attention. In chapter 8, we present the behavioural performance of these patients throughout the two phases of the clinical study (i.e. before and after either PA + real M1 a-tDCS or PA + sham M1 atDCS). There was no clear effect of a-tDCS on the therapeutic effect of PA in these patients. The results of the studies presented in this thesis provide a novel insight into the neurocomputational mechanisms of sensorimotor memory formation and its modulation by a-tDCS in the healthy brain. Further investigation of how these mechanisms relate to therapeutic improvements following PA in certain neglect patients is needed.
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Wright, Sean Patrick. "Cognitive neuroscience of episodic memory: behavioral, genetic, electrophysiological, and computational approaches to sequence memory." Thesis, Boston University, 2003. https://hdl.handle.net/2144/27805.

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Boston University. University Professors Program Senior theses.
PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
2031-01-02
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Vellmer, Sebastian [Verfasser]. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience / Sebastian Vellmer." Berlin : Humboldt-Universität zu Berlin, 2020. http://d-nb.info/1214240682/34.

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Ging-Jehli, Nadja Rita. "On the implementation of Computational Psychiatry within the framework of Cognitive Psychology and Neuroscience." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555338342285251.

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Books on the topic "Computational neuroimaging, cognitive neuroscience"

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Zhao, Qi, ed. Computational and Cognitive Neuroscience of Vision. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-0213-7.

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Gallistel, C. R. Memory and the computational brain: Why cognitive science will transform neuroscience. Chichester, West Sussex, UK: Wiley-Blackwell, 2009.

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Frank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.

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Frank, Rösler, ed. Neuroimaging of human memory: Linking cognitive processes to neural systems. Oxford: Oxford University Press, 2009.

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G, Hillary Frank, and DeLuca John 1956-, eds. Functional neuroimaging in clinical populations. New York: Guilford Press, 2007.

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Brain-inspired Cognitive Systems Conference (2010 : Madrid, Spain). From brains to systems: Brain-inspired cognitive systems 2010. Edited by Hernández Carlos. New York: Springer, 2011.

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Roberto, Cabeza, and Kingstone Alan, eds. Handbook of functional neuroimaging of cognition. 2nd ed. Cambridge, MA: MIT Press, 2005.

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International Conference on Intelligent Computing (3rd 2007 Qingdao, China). Advanced intelligent computing theories and applications: With aspects of artifical intelligence ; third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007 ; proceedings. Berlin: Springer, 2007.

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W, Cottrell Garrison, ed. Proceedings of the eighteenth annual conference of the Cognitive Science Society: July 12-15, 1996, University of California, San Diego. Mahwah, N.J: Lawrence Erlbaum Associates, 1996.

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De-Shuang, Huang, Li Kang, and Irwin G. W. 1950-, eds. International Conference on Intelligent Computing: ICIC 2006, Kunming, China, August 16-19, 2006 : proceedings. Berlin: Springer, 2006.

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Book chapters on the topic "Computational neuroimaging, cognitive neuroscience"

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Ray, Kimberly, and Angela Marie Richmond Laird. "Meta-analysis in Neuroimaging." In Encyclopedia of Computational Neuroscience, 1687–89. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_542.

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Ray, Kimberly, and Angela Laird. "Meta-analysis in Neuroimaging." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_542-1.

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Lahmiri, Salim, Mounir Boukadoum, and Antonio Di Ieva. "Fractals in Neuroimaging." In Springer Series in Computational Neuroscience, 295–309. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3995-4_19.

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Shimosegawa, Eku. "Advances in Neuroimaging Techniques with PET." In Cognitive Neuroscience Robotics B, 171–87. Tokyo: Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-54598-9_8.

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Zednik, Carlos. "Computational cognitive neuroscience." In The Routledge Handbook of the Computational Mind, 357–69. Milton Park, Abingdon, Oxon ; New York : Routledge, 2019. |: Routledge, 2018. http://dx.doi.org/10.4324/9781315643670-27.

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Kawato, Mitsuo. "Brain-Machine Interface and Neuroimaging." In Encyclopedia of Computational Neuroscience, 441–43. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_523.

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Poline, Jean Baptiste, and David Kennedy. "Software for Neuroimaging Data Analysis." In Encyclopedia of Computational Neuroscience, 2733–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_538.

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Ozaki, Tohru. "Statistical Analysis of Neuroimaging Data." In Encyclopedia of Computational Neuroscience, 2868–70. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_539.

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Bojak, Ingo, and Michael Breakspear. "Neuroimaging, Neural Population Models for." In Encyclopedia of Computational Neuroscience, 1919–44. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_70.

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Kawato, Mitsuo. "Brain Machine Interface and Neuroimaging." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_523-1.

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Conference papers on the topic "Computational neuroimaging, cognitive neuroscience"

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Steinkamp, Simon, Iyadh Chaker, Félix Hubert, David Meder, and Oliver Hulme. "Computational Parametric Mapping: A Method For Mapping Cognitive Models Onto Neuroimaging Data." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1124-0.

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Baykova, Reny, and Warrick Roseboom. "Effects of Sensory Precision on Behavioral and Neuroimaging Perceptual Biases in Duration Estimation." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1280-0.

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Kemtur, Anirudha, Francois Paugam, Basile Pinsard, Pravish sainath, Yann Harel, Maximilien Le clei, Julie Boyle, Karim Jerbi, and Pierre Bellec. "AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1303-0.

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Thomas, Armin, Hauke R. Heekeren, Klaus-Robert Müller, and Wojciech Samek. "DeepLight: A Structured Framework For The Analysis of Neuroimaging Data Through Recurrent Deep Learning Models." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1226-0.

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Niu, Xin, Hualou Liang, and Fengqing Zhang. "Brain age prediction for post-traumatic stress disorder patients with convolutional neural networks: a multi-modal neuroimaging study." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1121-0.

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Schrouff, J., and J. Mourao-Miranda. "Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?" In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2018. http://dx.doi.org/10.1109/prni.2018.8423944.

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Park, Seongmin, Maryam Zolfaghar, Jacob Russin, Douglas Miller, Randall O’Reilly, and Erie Boorman. "The geometry of cognitive maps under dynamic cognitive control." In 2022 Conference on Cognitive Computational Neuroscience. San Francisco, California, USA: Cognitive Computational Neuroscience, 2022. http://dx.doi.org/10.32470/ccn.2022.1023-0.

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Maley, Corey. "Analog Computation in Computational Cognitive Neuroscience." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1178-0.

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F. da Costa, Pedro, Sebastian Popescu, Robert Leech, and Romy Lorenz. "Elucidating Cognitive Processes Using LSTMs." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1201-0.

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Wegner, Katharina, Charlie Wilson, Emannuel Procyk, Karl Friston, and Daniele Marinazzo. "Cognitive Effort Modulates Frontal Effective Connections." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1232-0.

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Reports on the topic "Computational neuroimaging, cognitive neuroscience"

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Schunn, C. D. A Review of Human Spatial Representations Computational, Neuroscience, Mathematical, Developmental, and Cognitive Psychology Considerations. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada440864.

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