Download or read book Modeling and Analyzing Neural Dynamics and Information Processing Over Multiple Time Scales written by Sensen Liu and published by . This book was released on 2018 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: The brain produces complex patterns of activity that occur at different spatio-temporal scales. One of the fundamental questions in neuroscience is to understand how exactly these dynamics are related to brain function, for example our ability to extract and process information from the sensory periphery. This dissertation presents two distinct lines of inquiry related to different aspects of this high-level question. In the first part of the dissertation, we study the dynamics of burst suppression, a phenomenon in which brain electrical activity exhibits bistable dynamics. Burst suppression is frequently encountered in individuals who are rendered unconscious through general anesthesia and is thus a brain state associated with profound reductions in awareness and, presumably, information processing. Our primary contribution in this part of the dissertation is a new type of dynamical systems model whose analysis provides insights into the mechanistic underpinnings of burst suppression. In particular, the model yields explanations for the emergence of the characteristic two time-scales within burst suppression, and its synchronization across wide regions of the brain.The second part of the dissertation takes a different, more abstract approach to the question of multiple time-scale brain dynamics. Here, we consider how such dynamics might contribute to the process of learning in brain and brain-like networks, so as to enable neural information processing and subsequent computation. In particular, we consider the problem of optimizing information-theoretic quantities in recurrent neural networks via synaptic plasticity. In a recurrent network, such a problem is challenging since the modification of any one synapse (connection) has nontrivial dependency on the entire state of the network. This form of global learning is computationally challenging and moreover, is not plausible from a biological standpoint. In our results, we overcome these issues by deriving a local learning rule, one that modifies synapses based only on the activity of neighboring neurons. To do this, we augment from first principles the dynamics of each neuron with several auxiliary variables, each evolving at a different time-scale. The purpose of these variables is to support the estimation of global information-based quantities from local neuronal activity. It turns out that the synthesized dynamics, while providing only an approximation of the true solution, nonetheless are highly efficacious in enabling learning of representations of afferent input. Later, we generalize this framework in two ways, first to allow for goal-directed reinforcement learning and then to allow for information-based neurogenesis, the creation of neurons within a network based on task needs. Finally, the proposed learning dynamics are demonstrated on a range of canonical tasks, as well as a new application domain: the exogenous control of neural activity.
Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
Download or read book Handbook of Neural Engineering written by Metin Akay and published by John Wiley & Sons. This book was released on 2007-01-09 with total page 681 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important new work establishing a foundation for future developments in neural engineering The Handbook of Neural Engineering provides theoretical foundations in computational neural science and engineering and current applications in wearable and implantable neural sensors/probes. Inside, leading experts from diverse disciplinary groups representing academia, industry, and private and government organizations present peer-reviewed contributions on the brain-computer interface, nano-neural engineering, neural prostheses, imaging the brain, neural signal processing, the brain, and neurons. The Handbook of Neural Engineering covers: Neural signal and image processing--the analysis and modeling of neural activity and EEG-related activities using the nonlinear and nonstationary analysis methods, including the chaos, fractal, and time-frequency and time-scale analysis methods--and how to measure functional, physiological, and metabolic activities in the human brain using current and emerging medical imaging technologies Neuro-nanotechnology, artificial implants, and neural prosthesis--the design of multi-electrode arrays to study how the neurons of human and animals encode stimuli, the evaluation of functional changes in neural networks after stroke and spinal cord injuries, and improvements in therapeutic applications using neural prostheses Neurorobotics and neural rehabilitation engineering--the recent developments in the areas of biorobotic system, biosonar head, limb kinematics, and robot-assisted activity to improve the treatment of elderly subjects at the hospital and home, as well as the interactions of the neuron chip, neural information processing, perception and neural dynamics, learning memory and behavior, biological neural networks, and neural control
Download or read book Analysis and Modeling of Coordinated Multi neuronal Activity written by Masami Tatsuno and published by Springer. This book was released on 2014-11-13 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since information in the brain is processed by the exchange of spikes among neurons, a study of such group dynamics is extremely important in understanding hippocampus dependent memory. These spike patterns and local field potentials (LFPs) have been analyzed by various statistical methods. These studies have led to important findings of memory information processing. For example, memory-trace replay, a reactivation of behaviorally induced neural patterns during subsequent sleep, has been suggested to play an important role in memory consolidation. It has also been suggested that a ripple/sharp wave event (one of the characteristics of LFPs in the hippocampus) and spiking activity in the cortex have a specific relationship that may facilitate the consolidation of hippocampal dependent memory from the hippocampus to the cortex. The book will provide a state-of-the-art finding of memory information processing through the analysis of multi-neuronal data. The first half of the book is devoted to this analysis aspect. Understanding memory information representation and its consolidation, however, cannot be achieved only by analyzing the data. It is extremely important to construct a computational model to seek an underlying mathematical principle. In other words, an entire picture of hippocampus dependent memory system would be elucidated through close collaboration among experiments, data analysis, and computational modeling. Not only does computational modeling benefit the data analysis of multi-electrode recordings, but it also provides useful insight for future experiments and analyses. The second half of the book will be devoted to the computational modeling of hippocampus-dependent memory.
Download or read book Advances in Cognitive Neurodynamics VII written by Alessandra Lintas and published by Springer Nature. This book was released on 2021-09-30 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains original articles submitted to the Seventh International Conference on Cognitive Neurodynamics (ICCN 2019). The brain is an endless case study of a complex system characterized by multiple levels of integration, multiple time scales of activity, and multiple coding and decoding properties. The contribution of several disciplines, mathematics, physics, computer science, neurobiology, pharmacology, physiology, and behavioral and clinical sciences, is necessary in order to cope with such seemingly unattainable complexity that transforms the experimental information into a tricky puzzle which hides the correspondence with model predictions. This conference gathered active participants to discuss ideas and pose new questions from different viewpoints, ranging from single neurons and neural networks to animal/human behavior in theoretical and experimental studies. The conference is organized with plenary lectures, mini-symposia, interdisciplinary round tables, and oral and poster sessions.
Download or read book Time Series Modeling of Neuroscience Data written by Tohru Ozaki and published by CRC Press. This book was released on 2012-01-26 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required.Time Series Modeling of Neuroscience Data shows how to
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 Nonlinear Analysis in Neuroscience and Behavioral Research written by Tobias A. Mattei and published by Frontiers Media SA. This book was released on 2016-10-31 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although nonlinear dynamics have been mastered by physicists and mathematicians for a long time (as most physical systems are inherently nonlinear in nature), the recent successful application of nonlinear methods to modeling and predicting several evolutionary, ecological, physiological, and biochemical processes has generated great interest and enthusiasm among researchers in computational neuroscience and cognitive psychology. Additionally, in the last years it has been demonstrated that nonlinear analysis can be successfully used to model not only basic cellular and molecular data but also complex cognitive processes and behavioral interactions. The theoretical features of nonlinear systems (such unstable periodic orbits, period-doubling bifurcations and phase space dynamics) have already been successfully applied by several research groups to analyze the behavior of a variety of neuronal and cognitive processes. Additionally the concept of strange attractors has lead to a new understanding of information processing which considers higher cognitive functions (such as language, attention, memory and decision making) as complex systems emerging from the dynamic interaction between parallel streams of information flowing between highly interconnected neuronal clusters organized in a widely distributed circuit and modulated by key central nodes. Furthermore, the paradigm of self-organization derived from the nonlinear dynamics theory has offered an interesting account of the phenomenon of emergence of new complex cognitive structures from random and non-deterministic patterns, similarly to what has been previously observed in nonlinear studies of fluid dynamics. Finally, the challenges of coupling massive amount of data related to brain function generated from new research fields in experimental neuroscience (such as magnetoencephalography, optogenetics and single-cell intra-operative recordings of neuronal activity) have generated the necessity of new research strategies which incorporate complex pattern analysis as an important feature of their algorithms. Up to now nonlinear dynamics has already been successfully employed to model both basic single and multiple neurons activity (such as single-cell firing patterns, neural networks synchronization, autonomic activity, electroencephalographic measurements, and noise modulation in the cerebellum), as well as higher cognitive functions and complex psychiatric disorders. Similarly, previous experimental studies have suggested that several cognitive functions can be successfully modeled with basis on the transient activity of large-scale brain networks in the presence of noise. Such studies have demonstrated that it is possible to represent typical decision-making paradigms of neuroeconomics by dynamic models governed by ordinary differential equations with a finite number of possibilities at the decision points and basic heuristic rules which incorporate variable degrees of uncertainty. This e-book has include frontline research in computational neuroscience and cognitive psychology involving applications of nonlinear analysis, especially regarding the representation and modeling of complex neural and cognitive systems. Several experts teams around the world have provided frontline theoretical and experimental contributions (as well as reviews, perspectives and commentaries) in the fields of nonlinear modeling of cognitive systems, chaotic dynamics in computational neuroscience, fractal analysis of biological brain data, nonlinear dynamics in neural networks research, nonlinear and fuzzy logics in complex neural systems, nonlinear analysis of psychiatric disorders and dynamic modeling of sensorimotor coordination. Rather than a comprehensive compilation of the possible topics in neuroscience and cognitive research to which non-linear may be used, this e-book intends to provide some illustrative examples of the broad range of
Download or read book An Introductory Course in Computational Neuroscience written by Paul Miller and published by MIT Press. This book was released on 2018-10-02 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.
Download or read book The Dynamic Brain written by Timothy Roger Mullen and published by . This book was released on 2014 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The mind is the music that neural networks play." This quote from computational neurobiologist T.J. Sejnowski underscores a growing scientific consensus that studying the structure and function of vast networks of connections between brain regions is essential to understanding cognitive and affective state maintenance, sensorimotor information processing and control, etiologies and remedies for numerous neuropathologies, as well as a host of other facets of our conscious (and non-conscious) experience. Towards this goal, an ongoing challenge lies in identifying - in vivo in humans - spatiotemporal cortical network dynamics, at the level of individuals and groups, across experimental task conditions, and at the level of single trials. In the opening chapter of this dissertation, I introduce the Source Information Flow Toolbox (SIFT), a novel open-source software package for identification of neuronal dynamics and causal interactions in electrophysiological source and sensor data. The software integrates with the widely used EEGLAB analysis suite, addressing a need for robust tools for identifying single- and multi-trial multivariate brain network dynamics across time, frequency, anatomical source location, and subjects. I then introduce and assess two new methods (Measure Projection Analysis and Multi-view Hierarchical Bayesian Learning) for statistical analysis of source-level dynamics (including connectivity) across groups of subjects in the presence of missing data. The remaining chapters focus on applications of dynamical modeling approaches in SIFT to open problems within the fields of cognitive neuroscience, clinical neuroscience and neuroengineering. I first present three studies examining single-trial time-varying spatiotemporal network dynamics underlying generation and maintenance of epileptic seizures. Next I present a case study examining the effect of visual feedback on an occipito-parietal-motor network in freezing-of-gait in patients with Parkinson's disease. The final chapters focus on new directions in neuroengineering and brain-computer interfaces (BCI) leveraging neural dynamical system identification. We first review the history and state of the BCI field and summarize important new directions in BCI design. I then present a novel system for real-time mobile brain imaging, artifact rejection, neuronal system identification, and cognitive state prediction, and demonstrate its application in predicting response error commission from cortical network dynamics using a new high-density mobile dry EEG system.
Download or read book Neural Information Processing with Dynamical Synapses written by Si Wu and published by Frontiers E-books. This book was released on 2015-01-08 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Neural Dynamics and Signal Transmission Delay written by Jianhong Wu and published by Walter de Gruyter. This book was released on 2001 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the design of a neural network, either for biological modeling, cognitive simulation, numerical computation or engineering applications, it is important to investigate the network's computational performance which is usually described by the long-term behaviors, called dynamics, of the model equations. The purpose of this book is to give an introduction to the mathematical modeling and analysis of networks of neurons from the viewpoint of dynamical systems.
Download or read book Multiscale Analysis and Nonlinear Dynamics written by Misha Meyer Pesenson and published by John Wiley & Sons. This book was released on 2013-09-13 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since modeling multiscale phenomena in systems biology and neuroscience is a highly interdisciplinary task, the editor of the book invited experts in bio-engineering, chemistry, cardiology, neuroscience, computer science, and applied mathematics, to provide their perspectives. Each chapter is a window into the current state of the art in the areas of research discussed and the book is intended for advanced researchers interested in recent developments in these fields. While multiscale analysis is the major integrating theme of the book, its subtitle does not call for bridging the scales from genes to behavior, but rather stresses the unifying perspective offered by the concepts referred to in the title. It is believed that the interdisciplinary approach adopted here will be beneficial for all the above mentioned fields.
Download or read book Neural Information Processing written by Long Cheng and published by Springer. This book was released on 2018-12-03 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 11301, is organized in topical sections on deep neural networks, convolutional neural networks, recurrent neural networks, and spiking neural networks.
Download or read book Computational Psychiatry written by Alan Anticevic and published by Academic Press. This book was released on 2017-09-19 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Psychiatry: Mathematical Modeling of Mental Illness is the first systematic effort to bring together leading scholars in the fields of psychiatry and computational neuroscience who have conducted the most impactful research and scholarship in this area. It includes an introduction outlining the challenges and opportunities facing the field of psychiatry that is followed by a detailed treatment of computational methods used in the service of understanding neuropsychiatric symptoms, improving diagnosis and guiding treatments. This book provides a vital resource for the clinical neuroscience community with an in-depth treatment of various computational neuroscience approaches geared towards understanding psychiatric phenomena. Its most valuable feature is a comprehensive survey of work from leaders in this field. Offers an in-depth overview of the rapidly evolving field of computational psychiatry Written for academics, researchers, advanced students and clinicians in the fields of computational neuroscience, clinical neuroscience, psychiatry, clinical psychology, neurology and cognitive neuroscience Provides a comprehensive survey of work from leaders in this field and a presentation of a range of computational psychiatry methods and approaches geared towards a broad array of psychiatric problems
Download or read book Methods in Neuronal Modeling written by Christof Koch and published by MIT Press. This book was released on 1998 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kinetic Models of Synaptic Transmission / Alain Destexhe, Zachary F. Mainen, Terrence J. Sejnowski / - Cable Theory for Dendritic Neurons / Wilfrid Rall, Hagai Agmon-Snir / - Compartmental Models of Complex Neurons / Idan Segev, Robert E. Burke / - Multiple Channels and Calcium Dynamics / Walter M. Yamada, Christof Koch, Paul R. Adams / - Modeling Active Dendritic Processes in Pyramidal Neurons / Zachary F. Mainen, Terrence J. Sejnowski / - Calcium Dynamics in Large Neuronal Models / Erik De Schutter, Paul Smolen / - Analysis of Neural Excitability and Oscillations / John Rinzel, Bard Ermentrout / - Design and Fabrication of Analog VLSI Neurons / Rodney Douglas, Misha Mahowald / - Principles of Spike Train Analysis / Fabrizio Gabbiani, Christof Koch / - Modeling Small Networks / Larry Abbott, Eve Marder / - Spatial and Temporal Processing in Central Auditory Networks / Shihab Shamma / - Simulating Large Networks of Neurons / Alexander D. Protopapas, Michael Vanier, James M. Bower / ...
Download or read book An Introduction to Modeling Neuronal Dynamics written by Christoph Börgers and published by Springer. This book was released on 2017-04-17 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology. Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity. An extensive online collection of Matlab programs generating the figures accompanies the book.
