Download or read book A Study of Cortical Network Models with Realistic Connectivity written by Marina Vegué and published by . This book was released on 2018 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Structure is fundamental in shaping the types of computations that neuronal circuits can perform. Explaining the laws that determine the connectivity properties of brain networks and their implications in neuronal dynamics is therefore an important step in the understanding of how brains operate. The local circuits of cortex, which are considered to carry out the basic and essential computations for brain functioning, exhibit a highly stereotyped and organized architecture, which is, in very general terms, conserved across different species, brain areas and individuals. An appropriate way to mathematically represent this family of networks is by means of models defined by a set of connectivity laws that include a certain degree of randomness. These laws reflect the common structural scaffold, whereas the randomness should be interpreted as the variability across the different networks in the ensemble. There is growing experimental evidence that the local circuits of cerebral cortex are far from the simplest random model, according to which connections appear independently with a fixed probability. This evidence is based on a set of observed features that have been collectively called the "nonrandomness" of the cortical circuitry. In this thesis we have explored to what extent several alternative architectures (clustered networks, networks with distance-dependent connectivity and networks that exhibit a given in/out-degree distribution) could be compatible with the reported nonrandom features. We showed that all these structural models can explain the experimental observations, which implies that these nonrandom properties do not provide much information about the underlying organization. This is mainly due to the fact that real data are collected from sparse neuronal samples due to experimental limitations. We sought a local measure that can nevertheless help to distinguish between different alternatives, and we found it in the "sample degree correlation" (SDC), or the correlation coefficient between in- and out-degrees in small groups of neurons. The analysis of the SDC in real data suggests that cortical microcircuits are heterogeneous in structure and possibly shaped through a mixture of distance-dependent and non-symmetrical organizational principles. We finally explored some of the dynamical consequences of imposing a heterogeneous structure in models of neuronal activity. This heterogeneity appears through an arbitrary joint in/out-degree distribution in the entire network. By means of both mean-field approximations and spectral analysis, we demonstrate that broad and positively correlated degree distributions can have an important effect on neuronal dynamics, which suggests that this particular type of structural heterogeneity might allow for richer network computations as compared to standard random models.
Download or read book Structural Models of Cortical Networks with Long range Connectivity written by Nicole Voges and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Most current studies of neuronal activity dynamics in cortex are based on network models with completely random wiring. Such models are chosen for mathematical convenience, rather than biological grounds, and additionally reflect the notorious lack of knowledge about the neuroanatomical microstructure. Here, we describe some families of new, more realistic network models and explore some of their properties. Specifically, we consider spatially embedded networks and impose specific distance-dependent connectivity profiles. Each of these network models can cover the range from purely local to completely random connectivity, controlled by a single parameter. Stochastic graph theory is then used to describe and analyze the structure and the topology of these networks
Download or read book Estimation of Cortical Connectivity in Humans written by Laura Astolfi and published by Morgan & Claypool Publishers. This book was released on 2008 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last ten years many different brain imaging devices have conveyed a lot of information about the brain functioning in different experimental conditions. In every case, the biomedical engineers, together with mathematicians, physicists and physicians are called to elaborate the signals related to the brain activity in order to extract meaningful and robust information to correlate with the external behaviour of the subjects. In such attempt, different signal processing tools used in telecommunications and other field of engineering or even social sciences have been adapted and re-used in the neuroscience field. The present book would like to offer a short presentation of several methods for the estimation of the cortical connectivity of the human brain. The methods here presented are relatively simply to implement, robust and can return valuable information about the causality of the activation of the different cortical areas in humans using non invasive electroencephalographic recordings. The knowledge of such signal processing tools will enrich the arsenal of the computational methods that a engineer or a mathematician could apply in the processing of brain signals.
Download or read book Towards an Integrated Approach to Measurement Analysis and Modeling of Cortical Networks written by A. Ravishankar Rao and published by Frontiers Media SA. This book was released on 2016-03-17 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of data being produced by neuroscientists is increasing rapidly, driven by advances in neuroimaging and recording techniques spanning multiple scales of resolution. The availability of such data poses significant challenges for their processing and interpretation. To gain a deeper understanding of the surrounding issues, the Editors of this e-Book reached out to an interdisciplinary community, and formed the Cortical Networks Working Group, and the genesis of this e-Book thus began with the formation of this Working Group, which was supported by the National Institute for Mathematical and Biological Synthesis in the USA. The Group consisted of scientists from neuroscience, physics, psychology and computer science, and meetings were held in person. (A detailed list of the group members is presented in the Editorial that follows.) At the time we started, in 2010, the term “big data” was hardly in existence, though the volume of data we were handling would certainly have qualified. Furthermore, there was significant interest in harnessing the power of supercomputers to perform large scale neuronal simulations, and in creating specialized hardware to mimic neural function. We realized that the various disciplines represented in our Group could and should work together to accelerate progress in Neuroscience. We searched for common threads that could define the foundation for an integrated approach to solve important problems in the field. We adopted a network-centric perspective to address these challenges, as the data are derived from structures that are themselves network-like. We proposed three inter-twined threads, consisting of measurement of neural activity, analysis of network structures deduced from this activity, and modeling of network function, leading to theoretical insights. This approach formed the foundation of our initial call for papers. When we issued the call for papers, we were not sure how many papers would fall into each of these threads. We were pleased that we found significant interest in each thread, and the number of submissions exceeded our expectations. This is an indication that the field of neuroscience is ripe for the type of integration and interchange that we had anticipated. We first published a special topics issue after we received a sufficient number of submissions. This is now being converted to an e-book to strengthen the coherence of its contributions. One of the strong themes emerging in this e-book is that network-based measures capture better the dynamics of brain processes, and provide features with greater discriminative power than point-based measures. Another theme is the importance of network oscillations and synchrony. Current research is shedding light on the principles that govern the establishment and maintenance of network oscillation states. These principles could explain why there is impaired synchronization between different brain areas in schizophrenics and Parkinson’s patients. Such research could ultimately provide the foundation for an understanding of other psychiatric and neurodegenerative conditions. The chapters in this book cover these three main threads related to cortical networks. Some authors have combined two or more threads within a single chapter. We expect the availability of related work appearing in a single e-book to help our readers see the connection between different research efforts, and spur further insights and research.
Download or read book The Relevance of the Time Domain to Neural Network Models written by A. Ravishankar Rao and published by Springer Science & Business Media. This book was released on 2011-09-18 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps [6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs. The purpose of this book is to provide a unified view of how the time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal firing patterns of a neuron or a group of neurons. There is a growing body of research on the use of oscillatory neural networks, and their ability to synchronize under the right conditions. Such networks of synchronizing elements have been shown to be effective in image processing and segmentation tasks, and also in solving the binding problem, which is of great significance in the field of neuroscience. The oscillatory neural models can be employed at multiple scales of abstraction, ranging from individual neurons, to groups of neurons using Wilson-Cowan modeling techniques and eventually to the behavior of entire brain regions as revealed in oscillations observed in EEG recordings. A second interesting direction to consider is to understand the effect of different neural network topologies on their ability to create the desired synchronization. A third direction of interest is the extraction of temporal signaling patterns from brain imaging data such as EEG and fMRI. Hence this Special Session is of emerging interest in the brain sciences, as imaging techniques are able to resolve sufficient temporal detail to provide an insight into how the time domain is deployed in cognitive function. The following broad topics will be covered in the book: Synchronization, phase-locking behavior, image processing, image segmentation, temporal pattern analysis, EEG analysis, fMRI analyis, network topology and synchronizability, cortical interactions involving synchronization, and oscillatory neural networks. This book will benefit readers interested in the topics of computational neuroscience, applying neural network models to understand brain function, extracting temporal information from brain imaging data, and emerging techniques for image segmentation using oscillatory networks
Download or read book Analysis of Cortical Connectivity Using Hopfield Neural Network written by S. Dixit and published by . This book was released on 2018 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is striking similarity in the connectivity between perceptrons in an Artificial Neural Network and neurons in brain. Therefore it is a natural logical step to investigate cortical connectivity using Artificial Neural Networks. Present approaches to ascertaining cortical connectivity, e.g. Structural Equation Modeling between various regions of interest (ROI) in the active brain are-tedious and time-consuming . For example, modeling the connectivity of a large number of brain regions often involves numerous parameter changes to achieve a good fit. Functional Magnetic Resonance Imaging (fMRI) is increasing recognized as a standard technique for brain mapping. This study explores the utility of a Hopfield Neural Network to determine cortical connectivity in an fMRI data set.
Download or read book Micro Meso and Macro Connectomics of the Brain written by Henry Kennedy and published by Springer. This book was released on 2016-03-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has brought together leading investigators who work in the new arena of brain connectomics. This includes ‘macro-connectome’ efforts to comprehensively chart long-distance pathways and functional networks; ‘micro-connectome’ efforts to identify every neuron, axon, dendrite, synapse, and glial process within restricted brain regions; and ‘meso-connectome’ efforts to systematically map both local and long-distance connections using anatomical tracers. This book highlights cutting-edge methods that can accelerate progress in elucidating static ‘hard-wired’ circuits of the brain as well as dynamic interactions that are vital for brain function. The power of connectomic approaches in characterizing abnormal circuits in the many brain disorders that afflict humankind is considered. Experts in computational neuroscience and network theory provide perspectives needed for synthesizing across different scales in space and time. Altogether, this book provides an integrated view of the challenges and opportunities in deciphering brain circuits in health and disease.
Download or read book Connectionist Models of Development written by Philip T. Quinlan and published by Psychology Press. This book was released on 2004-03-01 with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connectionist Models of Development is an edited collection of essays on the current work concerning connectionist or neural network models of human development. The brain comprises millions of nerve cells that share myriad connections, and this book looks at how human development in these systems is typically characterised as adaptive changes to the strengths of these connections. The traditional accounts of connectionist learning, based on adaptive changes to weighted connections, are explored alongside the dynamic accounts in which networks generate their own structures as learning proceeds. Unlike most connectionist accounts of psychological processes which deal with the fully-mature system, this text brings to the fore a discussion of developmental processes. To investigate human cognitive and perceptual development, connectionist models of learning and representation are adopted alongside various aspects of language and knowledge acquisition. There are sections on artificial intelligence and how computer programs have been designed to mimic the development processes, as well as chapters which describe what is currently known about how real brains develop. This book is a much-needed addition to the existing literature on connectionist development as it includes up-to-date examples of research on current controversies in the field as well as new features such as genetic connectionism and biological theories of the brain. It will be invaluable to academic researchers, post-graduates and undergraduates in developmental psychology and those researching connectionist/neural networks as well as those in related fields such as psycholinguistics.
Download or read book Lectures in Supercomputational Neuroscience written by Peter Graben and published by Springer. This book was released on 2007-10-19 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written from the physicist’s perspective, this book introduces computational neuroscience with in-depth contributions by system neuroscientists. The authors set forth a conceptual model for complex networks of neurons that incorporates important features of the brain. The computational implementation on supercomputers, discussed in detail, enables you to adapt the algorithm for your own research. Worked-out examples of applications are provided.
Download or read book Advances in Neural Information Processing Systems 7 written by Gerald Tesauro and published by MIT Press. This book was released on 1995 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: November 28-December 1, 1994, Denver, Colorado NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus remains the study of a wide variety of learning algorithms and architectures, for both supervised and unsupervised learning. The 139 contributions are divided into eight parts: Cognitive Science, Neuroscience, Learning Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Visual Processing, and Applications. Topics of special interest include the analysis of recurrent nets, connections to HMMs and the EM procedure, and reinforcement- learning algorithms and the relation to dynamic programming. On the theoretical front, progress is reported in the theory of generalization, regularization, combining multiple models, and active learning. Neuroscientific studies range from the large-scale systems such as visual cortex to single-cell electrotonic structure, and work in cognitive scientific is closely tied to underlying neural constraints. There are also many novel applications such as tokamak plasma control, Glove-Talk, and hand tracking, and a variety of hardware implementations, with particular focus on analog VLSI.
Download or read book Steady state Learning and Synaptic Connectivity in Local Cortical Networks of Excitatory and Ihibitory Neurons written by Julio Ivan Chapeton and published by . This book was released on 2014 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning and memory storage are arguably the most fundamental and well-studied functions of the mammalian cortex. It is established that these functions are mediated by many forms of synaptic plasticity, which shape neural circuits in the course of learning by creating, modifying, and eliminating individual synaptic connections. Nevertheless, the effects of learning and memory storage on the cortical connectivity diagram in the adult are largely unknown. In general, it is difficult to find examples where the link between a function and the connectivity of the underlying neural circuit is completely understood. Experiments have shown that some connectivity features are ubiquitously present in local cortical networks. These features include very sparse connectivity of excitatory neuron axons, much denser connectivity established by the axons of many inhibitory neuron classes, and stereotypically distributed connection weights. Given the pervasiveness of these features, is it possible that they could have arisen as a direct consequence of learning? To answer this question, in Chapter 2 we examine a biologically realistic, yet exactly solvable model of associative memory which is based on the hypothesis that synaptic connectivity in a given local circuit of adult cortex is in a steady-state; in this state the associative memory storage capacity of the circuit is maximal and learning of new associations is accompanied by forgetting of some of the old ones. The model is applicable to networks of multiple excitatory and inhibitory neuron classes and can account for homeostatic constraints on the number and the overall weight of functional connections received by each neuron. In Chapter 3 we describe how the model was solved analytically by using the replica theory from statistical physics, and we highlight the most salient features of synaptic connectivity which arise from steady-state learning. Chapter 4 is devoted to testing the validity of the model by comparing these features with a large dataset of published experimental studies reporting amplitudes of unitary postsynaptic potentials and probabilities of connections between various classes of excitatory and inhibitory neurons in the cerebellum, neocortex, and hippocampus. The theoretical results are in good agreement with these experimental measurements, suggesting that stereotypic features of adult connectivity can form despite functional differences among brain areas and diverse learning experiences of individual animals. Lastly, in Chapter 5 we show how biologically constrained learning can be used in a machine learning methodology to accurately trace sparsely labeled neurites in light microscopy stacks of images.
Download or read book The Graph Theoretical Approach in Brain Functional Networks written by Fabrizio Fallani and published by Morgan & Claypool Publishers. This book was released on 2010-09-09 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present book illustrates the theoretical aspects of several methodologies related to the possibility of i) enhancing the poor spatial information of the electroencephalographic (EEG) activity on the scalp and giving a measure of the electrical activity on the cortical surface. ii) estimating the directional influences between any given pair of channels in a multivariate dataset. iii) modeling the brain networks as graphs. The possible applications are discussed in three different experimental designs regarding i) the study of pathological conditions during a motor task, ii) the study of memory processes during a cognitive task iii) the study of the instantaneous dynamics throughout the evolution of a motor task in physiological conditions. The main outcome from all those studies indicates clearly that the performance of cognitive and motor tasks as well as the presence of neural diseases can affect the brain network topology. This evidence gives the power of reflecting cerebral "states" or "traits" to the mathematical indexes derived from the graph theory. In particular, the observed structural changes could critically depend on patterns of synchronization and desynchronization - i.e. the dynamic binding of neural assemblies - as also suggested by a wide range of previous electrophysiological studies. Moreover, the fact that these patterns occur at multiple frequencies support the evidence that brain functional networks contain multiple frequency channels along which information is transmitted. The graph theoretical approach represents an effective means to evaluate the functional connectivity patterns obtained from scalp EEG signals. The possibility to describe the complex brain networks sub-serving different functions in humans by means of "numbers" is a promising tool toward the generation of a better understanding of the brain functions. Table of Contents: Introduction / Brain Functional Connectivity / Graph Theory / High-Resolution EEG / Cortical Networks in Spinal Cord Injured Patients / Cortical Networks During a Lifelike Memory Task / Application to Time-varying Cortical Networks / Conclusions
Download or read book Connectome written by Sebastian Seung and published by HMH. This book was released on 2012-02-07 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Accessible, witty . . . an important new researcher, philosopher and popularizer of brain science . . . on par with cosmology’s Brian Greene and the late Carl Sagan” (The Plain Dealer). One of the Wall Street Journal’s 10 Best Nonfiction Books of the Year and a Publishers Weekly “Top Ten in Science” Title Every person is unique, but science has struggled to pinpoint where, precisely, that uniqueness resides. Our genome may determine our eye color and even aspects of our character. But our friendships, failures, and passions also shape who we are. The question is: How? Sebastian Seung is at the forefront of a revolution in neuroscience. He believes that our identity lies not in our genes, but in the connections between our brain cells—our particular wiring. Seung and a dedicated group of researchers are leading the effort to map these connections, neuron by neuron, synapse by synapse. It’s a monumental effort, but if they succeed, they will uncover the basis of personality, identity, intelligence, memory, and perhaps disorders such as autism and schizophrenia. Connectome is a mind-bending adventure story offering a daring scientific and technological vision for understanding what makes us who we are, as individuals and as a species. “This is complicated stuff, and it is a testament to Dr. Seung’s remarkable clarity of exposition that the reader is swept along with his enthusiasm, as he moves from the basics of neuroscience out to the farthest regions of the hypothetical, sketching out a spectacularly illustrated giant map of the universe of man.” —TheNew York Times “An elegant primer on what’s known about how the brain is organized and how it grows, wires its neurons, perceives its environment, modifies or repairs itself, and stores information. Seung is a clear, lively writer who chooses vivid examples.” —TheWashington Post
Download or read book Local Cortical Circuits written by Moshe Abeles and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neurophysiologists are often accused by colleagues in the physical sci ences of designing experiments without any underlying hypothesis. This impression is attributable to the ease of getting lost in the ever-increasing sea of professional publications which do not state explicitly the ultimate goal of the research. On the other hand, many of the explicit models for brain function in the past were so far removed from experimental reality that they had very little impact on further research. It seems that one needs much intimate experience with the real nerv-. ous system before a reasonable model can be suggested. It would have been impossible for Copernicus to suggest his model of the solar system without the detailed observations and tabulations of star and planet motion accu mulated by the preceeding generations. This need for intimate experience with the nervous system before daring to put forward some hypothesis about its mechanism of action is especially apparent when theorizing about cerebral cortex function. There is widespread agreement that processing of information in the cor tex is associated with complex spatio-temporal patterns of activity. Yet the vast majority of experimental work is based on single neuron recordings or on recordings made with gross electrodes to which tens of thousands of neurons contribute in an unknown fashion. Although these experiments have taught us a great deal about the organization and function of the cor tex, they have not enabled us to examine the spatio-temporal organization of neuronal activity in any detail.
Download or read book Connectivity driven parcellation methods for the human cerebral cortex written by Salim Arslan and published by Salim Arslan. This book was released on 2017-11-01 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: The macro connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform cognitive functions. It embodies the notion of representing, analysing, and understanding all connections within the brain as a network, while the subdivision of the brain into interacting cortical units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Parcellations derived from anatomical brain atlases or random parcellations are traditionally used for node identification, however these approaches do not always fully reflect the functional/structural organisation of the brain. Connectivity-driven methods have arisen only recently, aiming to delineate parcellations that are more faithful to the underlying connectivity. Such parcellation methods face several challenges, including but not limited to poor signal-to-noise ratio, the curse of dimensionality, and functional/structural variations inherent in individual brains, which are only limitedly addressed by the current state of the art. In this thesis, we present robust and fully-automated methods for the subdivision of the entire human cerebral cortex based on connectivity information. Our contributions are four-fold: First, we propose a clustering approach to delineate a cortical parcellation that provides a reliable abstraction of the brain's functional organisation. Second, we cast the parcellation problem as a feature reduction problem and make use of manifold learning and image segmentation techniques to identify cortical regions with distinct structural connectivity patterns. Third, we present a multi-layer graphical model that combines within- and between-subject connectivity, which is then decomposed into a cortical parcellation that can represent the whole population, while accounting for the variability across subjects. Finally, we conduct a large-scale, systematic comparison of existing parcellation methods, with a focus on providing some insight into the reliability of brain parcellations in terms of reflecting the underlying connectivity, as well as, revealing their impact on network analysis. We evaluate the proposed parcellation methods on publicly available data from the Human Connectome Project and a plethora of quantitative and qualitative evaluation techniques investigated in the literature. Experiments across multiple resolutions demonstrate the accuracy of the presented methods at both subject and group levels with regards to reproducibility and fidelity to the data. The neuro-biological interpretation of the proposed parcellations is also investigated by comparing parcel boundaries with well-structured properties of the cerebral cortex. Results show the advantage of connectivity-driven parcellations over traditional approaches in terms of better fitting the underlying connectivity. However, the benefit of using connectivity to parcellate the brain is not always as clear regarding the agreement with other modalities and simple network analysis tasks carried out across healthy subjects. Nonetheless, we believe the proposed methods, along with the systematic evaluation of existing techniques, offer an important contribution to the field of brain parcellation, advancing our understanding of how the human cerebral cortex is organised at the macroscale.
Download or read book Fundamentals of Neural Network Modeling written by Randolph W. Parks and published by MIT Press. This book was released on 1998 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
Download or read book Anatomy and Plasticity in Large Scale Brain Models written by Markus Butz and published by Frontiers Media SA. This book was released on 2017-01-05 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercomputing facilities are becoming increasingly available for simulating activity dynamics in large-scale neuronal networks. On today's most advanced supercomputers, networks with up to a billion of neurons can be readily simulated. However, building biologically realistic, full-scale brain models requires more than just a huge number of neurons. In addition to network size, the detailed local and global anatomy of neuronal connections is of crucial importance. Moreover, anatomical connectivity is not fixed, but can rewire throughout life (structural plasticity)—an aspect that is missing in most current network models, in which plasticity is confined to changes in synaptic strength (synaptic plasticity). The papers in this Ebook, which may broadly be divided into three themes, aim to bring together high-performance computing with recent experimental and computational research in neuroanatomy. In the first theme (fiber connectivity), new methods are described for measuring and data-basing microscopic and macroscopic connectivity. In the second theme (structural plasticity), novel models are introduced that incorporate morphological plasticity and rewiring of anatomical connections. In the third theme (large-scale simulations), simulations of large-scale neuronal networks are presented with an emphasis on anatomical detail and plasticity mechanisms. Together, the articles in this Ebook make the reader aware of the methods and models by which large-scale brain networks running on supercomputers can be extended to include anatomical detail and plasticity.
