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 Analysis of the Hopfield Neural Networks and Their Application to Pattern Recognition written by Bernard F Gerasimas (Jr) and published by . This book was released on 1988 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-part discussion provides the necessary background in understanding the make-up and operation of the generalized biological neuron and its role in memory and pattern recognition in the brain. There are many specific types of neurons which have adapted to perform specialized operations as part of the central nervous system. Motor neurons deal with the operation of the muscles; optical neurons deal with the operations of the eyes. Although there are differences in physical make-up, their basic operation is similiar and is known as the generalized biological neuron. The physical structure and operation of this neuron will be examined. A brief discussion will follow on how the inter-connection and paralleled structure of neural networks in the optical system and cortex of the brain are able to send, store, and recall information dealing with pattern recognition. This will be followed by a brief discussion of the first mathematical model developed of the generalized biological model. Its make-up is the building block for the vast majority of neural network models which followed. Keywords: Computerized simulation.
Download or read book Computational Neuroscience Trends in Research 2004 written by E. De Schutter and published by Elsevier. This book was released on 2004-06-26 with total page 1260 pages. Available in PDF, EPUB and Kindle. Book excerpt: The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as many different experimental preparations and theoretical approaches. The papers published here range from pure experimental neurobiology, to neuro-ethology, mathematics, physics, and engineering. In all cases the research described is focused on understanding how nervous systems compute. The actual subjects of the research include a highly diverse number of preparations, modeling approaches and analysis techniques. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.
Download or read book Neural Engineering written by Chris Eliasmith and published by MIT Press. This book was released on 2003 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
Download or read book Neurocomputing written by Francoise Fogelman Soulie and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the collected papers of the NATO Conference on Neurocomputing, held in Les Arcs in February 1989. For many of us, this conference was reminiscent of another NATO Conference, in 1985, on Disordered Systems [1], which was the first conference on neural nets to be held in France. To some of the participants that conference opened, in a way, the field of neurocomputing (somewhat exotic at that time!) and also allowed for many future fruitful contacts. Since then, the field of neurocomputing has very much evolved and its audience has increased so widely that meetings in the US have often gathered more than 2000 participants. However, the NATO workshops have a distinct atmosphere of free discussions and time for exchange, and so, in 1988, we decided to go for another session. This was an ~casion for me and some of the early birds of the 1985 conference to realize how much, and how little too, the field had matured.
Download or read book Handbook of Brain Connectivity written by Viktor K. Jirsa and published by Springer. This book was released on 2007-08-16 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring structural and functional connectivity in the brain. Part three provides an overview of the analysis techniques currently available and highlights new developments. Part four introduces the application and translation of the concepts of brain connectivity to behavior, cognition and the clinical domain.
Download or read book Cortex Statistics and Geometry of Neuronal Connectivity written by Valentino Braitenberg and published by Springer. This book was released on 2012-11-01 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: By means of quantitative analysis of the tissue components in the cortex of the mouse, this book presents an overall picture of the cortical network which is then related to various theories on cortical function. Centering around the idea of a diffuse network in a fairly homogeneous population of excitatory neurons, that of the pyramidal cells, it shows that the whole organisation in the cortical skeleton of pryramidal cells corresponds well with the idea of an associative memory and with the theory of cell assemblies. Provides the reader with information on quantitative neuroanatomy and also on the methods used, in particular those that vary from the norm.
Download or read book Cortical Connectivity written by Robert Chen and published by Springer Science & Business Media. This book was released on 2012-10-17 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study and modulation of cortical connections is a rapidly growing area in neuroscience. This unique book by prominent researchers in the field covers recent advances in this area. The first section of the book describes studies of cortical connections, modulation of cortical connectivity and changes in cortical connections with activities such as motor learning and grasping in primates. The second section covers the use of non-invasive brain stimulation to study and modulate cortical connectivity in humans. The last section describes changes in brain connectivity in neurological and psychiatric diseases, and potential new treatments that manipulate brain connectivity. This book provides an up-to-date view of the study of cortical connectivity, and covers its role in both fundamental neuroscience and potential clinical applications.
Download or read book Estimating Functional Connectivity and Topology in Large Scale Neuronal Assemblies written by Vito Paolo Pastore and published by Springer Nature. This book was released on 2020-11-06 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes a set of novel statistical algorithms designed to infer functional connectivity of large-scale neural assemblies. The algorithms are developed with the aim of maximizing computational accuracy and efficiency, while faithfully reconstructing both the inhibitory and excitatory functional links. The book reports on statistical methods to compute the most significant functional connectivity graph, and shows how to use graph theory to extract the topological features of the computed network. A particular feature is that the methods used and extended at the purpose of this work are reported in a fairly completed, yet concise manner, together with the necessary mathematical fundamentals and explanations to understand their application. Furthermore, all these methods have been embedded in the user-friendly open source software named SpiCoDyn, which is also introduced here. All in all, this book provides researchers and graduate students in bioengineering, neurophysiology and computer science, with a set of simplified and reduced models for studying functional connectivity in in silico biological neuronal networks, thus overcoming the complexity of brain circuits.
Download or read book Knowledge based Intelligent Information Engineering Systems and Allied Technologies written by Ernesto Damiani and published by . This book was released on 2002 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation The book contains the Proceedings of KES 2002, the Sixth Edition of the Knowledge-Based Intelligent Information & Engineering Systems International Conference. The conference papers presented new research results, focusing on three main areas of interest: Generic Intelligent Techniques: This area includes results on basic disciplines underlying knowledge-based and intelligent systems, such as artificial neural networks, machine learning, knowledge-based systems, case-based reasoning, intelligent agents and soft computing. Applications of Intelligent Systems: The second area presents results on vertical applications of intelligent systems, including condition monitoring, fault diagnosis, industrial control, medical systems, image processing, financial & stock market monitoring and prediction, natural language processing and others. Allied Technologies: This area includes novel contributions on intelligent systems' applications to traditional research fields such as digital and computer communications, signal processing, virtual reality, multi-media, web-based technologies, human-computer interfaces and software engineering."
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 Analysis of Cortical Connectivity written by Malcolm P. Young and published by Landes Bioscience. This book was released on 1995 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Neural Computation in Hopfield Networks and Boltzmann Machines written by James P. Coughlin and published by University of Delaware Press. This book was released on 1995 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: "One hundred years ago, the fundamental building block of the central nervous system, the neuron, was discovered. This study focuses on the existing mathematical models of neurons and their interactions, the simulation of which has been one of the biggest challenges facing modern science." "More than fifty years ago, W. S. McCulloch and W. Pitts devised their model for the neuron, John von Neumann seemed to sense the possibilities for the development of intelligent systems, and Frank Rosenblatt came up with a functioning network of neurons. Despite these advances, the subject had begun to fade as a major research area until John Hopfield arrived on the scene. Drawing an analogy between neural networks and the Ising spin models of ferromagnetism, Hopfield was able to introduce a "computational energy" that would decline toward stable minima under the operation of the system of neurodynamics devised by Roy Glauber." "Like a switch, a neuron is said to be either "on" or "off." The state of the neuron is determined by the states of the other neurons and the connections between them, and the connections are assumed to be reciprocal - that is, neuron number one influences neuron number two exactly as strongly as neuron number two influences neuron number one. According to the Glauber dynamics, the states of the neurons are updated in a random serial way until an equilibrium is reached. An energy function can be associated with each state, and equilibrium corresponds to a minimum of this energy. It follows from Hopfield's assumption of reciprocity that an equilibrium will always be reached." "D. H. Ackley, G. E. Hinton, and T. J. Sejnowski modified the Hopfield network by introducing the simulated annealing algorithm to search out the deepest minima. This is accomplished by - loosely speaking - shaking the machine. The violence of the shaking is controlled by a parameter called temperature, producing the Boltzmann machine - a name designed to emphasize the connection to the statistical physics of Ising spin models." "The Boltzmann machine reduces to the Hopfield model in the special case where the temperature goes to zero. The resulting network, under the Glauber dynamics, produces a homogeneous, irreducible, aperiodic Markov chain as it wanders through state space. The entire theory of Markov chains becomes applicable to the Boltzmann machine." "With ten chapters, five appendices, a list of references, and an index, this study should serve as an introduction to the field of neural networks and its application, and is suitable for an introductory graduate course or an advanced undergraduate course."--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved
Download or read book Cortical Oscillations and Synaptic Plasticity written by Xiumin Li and published by . This book was released on 2011 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finally, in order to understand how synchronous activity emerges from self-organized neural networks, we propose a novel network refined from spike-timing dependent plasticity (STDP). Due to the existence of heterogeneity in neurons which exhibit different degrees of excitability, the network finally evolves into a sparse and active-neuron-dominant structure. That is, strong connections are mainly distributed to the synapses from active neuronsto inactive ones. We argue that this self-emergent topology essentially reflects the competition of different neurons and encodes the heterogeneity. This structure is shown to significantly promote synchronization and enhance the coherence resonance and stochastic resonance of the entire network, indicating its high efficiency in information processing. Based on this work, we further develop another network organized from two stages of learning process, including STDP and another burst-based plasticity, i.e., burst-timing dependent plasticity (BTDP). The likely relationship between the learning rules with different timescales and the formation of architecture with different special scales is explored. The final network exhibits a two-level hierarchical structure after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficient. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the neural network.
Download or read book Functional Brain Mapping and the Endeavor to Understand the Working Brain written by Francesco Signorelli and published by BoD – Books on Demand. This book was released on 2013-06-19 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional brain mapping has by now gained a high impact on research and clinical practice: huge funds are unveiled all over the world in order to boost the research and clinical applications of this field of neuroscience. The most successful approach to unlock the mysteries of the brain, to tell it with Jay Ingram, is to bring together an interdisciplinary network of scientists and clinicians and encourage an interchange of ideas. It is this crossfire we try to promote with this book.
Download or read book Organization of Neural Networks written by Werner Seelen and published by VCH Publishers. This book was released on 1988 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt:
