Analysis And Modeling Of Coordinated Multi Neuronal Activity

Author : Masami Tatsuno
Publisher : Springer
ISBN 13 : 1493919695
Total Pages : 353 pages
Book Rating : 4.97/5 ( download)

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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.

Author : Sensen Liu
Publisher :
ISBN 13 :
Total Pages : 153 pages
Book Rating : 4.41/5 ( download)

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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.

Author : Zhe Chen
Publisher : Springer
ISBN 13 : 3319719769
Total Pages : 327 pages
Book Rating : 4.64/5 ( download)

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Download or read book Dynamic Neuroscience written by Zhe Chen and published by Springer. This book was released on 2017-12-27 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers.

Author : Asim Roy
Publisher : Frontiers Media SA
ISBN 13 : 2889455963
Total Pages : 147 pages
Book Rating : 4.66/5 ( download)

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Download or read book Representation in the Brain written by Asim Roy and published by Frontiers Media SA. This book was released on 2018-09-28 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook contains ten articles on the topic of representation of abstract concepts, both simple and complex, at the neural level in the brain. Seven of the articles directly address the main competing theories of mental representation – localist and distributed. Four of these articles argue – either on a theoretical basis or with neurophysiological evidence – that abstract concepts, simple or complex, exist (have to exist) at either the single cell level or in an exclusive neural cell assembly. There are three other papers that argue for sparse distributed representation (population coding) of abstract concepts. There are two other papers that discuss neural implementation of symbolic models. The remaining paper deals with learning of motor skills from imagery versus actual execution. A summary of these papers is provided in the Editorial.

Author : Richard W. Morris
Publisher : Academic Press
ISBN 13 : 0128120991
Total Pages : 484 pages
Book Rating : 4.96/5 ( download)

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Download or read book Goal-Directed Decision Making written by Richard W. Morris and published by Academic Press. This book was released on 2018-08-23 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations performed by associated circuits, but then moves on to in-depth examinations on how goal-directed learning interacts with other forms of choice and response selection. This is the only book that embraces the multidisciplinary nature of this area of decision-making, integrating our knowledge of goal-directed decision-making from basic, computational, clinical, and ethology research into a single resource that is invaluable for neuroscientists, psychologists and computer scientists alike. The book presents discussions on the broader field of decision-making and how it has expanded to incorporate ideas related to flexible behaviors, such as cognitive control, economic choice, and Bayesian inference, as well as the influences that motivation, context and cues have on behavior and decision-making. Details the neural circuits functionally involved in goal-directed decision-making and the computations these circuits perform Discusses changes in goal-directed decision-making spurred by development and disorders, and within real-world applications, including social contexts and addiction Synthesizes neuroscience, psychology and computer science research to offer a unique perspective on the central and emerging issues in goal-directed decision-making

Author : Ning Lan
Publisher : Frontiers Media SA
ISBN 13 : 2889451305
Total Pages : 180 pages
Book Rating : 4.02/5 ( download)

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Download or read book Neural and Computational Modeling of Movement Control written by Ning Lan and published by Frontiers Media SA. This book was released on 2017-04-17 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the study of sensorimotor systems, an important research goal has been to understand the way neural networks in the spinal cord and brain interact to control voluntary movement. Computational modeling has provided insight into the interaction between centrally generated commands, proprioceptive feedback signals and the biomechanical responses of the moving body. Research in this field is also driven by the need to improve and optimize rehabilitation after nervous system injury and to devise biomimetic methods of control in robotic devices. This research topic is focused on efforts dedicated to identify and model the neuromechanical control of movement. Neural networks in the brain and spinal cord are known to generate patterned activity that mediates coordinated activation of multiple muscles in both rhythmic and discrete movements, e.g. locomotion and reaching. Commands descending from the higher centres in the CNS modulate the activity of spinal networks, which control movement on the basis of sensory feedback of various types, including that from proprioceptive afferents. The computational models will continue to shed light on the central strategies and mechanisms of sensorimotor control and learning. This research topic demonstrated that computational modeling is playing a more and more prominent role in the studies of postural and movement control. With increasing ability to gather data from all levels of the neuromechanical sensorimotor systems, there is a compelling need for novel, creative modeling of new and existing data sets, because the more systematic means to extract knowledge and insights about neural computations of sensorimotor systems from these data is through computational modeling. While models should be based on experimental data and validated with experimental evidence, they should also be flexible to provide a conceptual framework for unifying diverse data sets, to generate new insights of neural mechanisms, to integrate new data sets into the general framework, to validate or refute hypotheses and to suggest new testable hypotheses for future experimental investigation. It is thus expected that neural and computational modeling of the sensorimotor system should create new opportunities for experimentalists and modelers to collaborate in a joint endeavor to advance our understanding of the neural mechanisms for postural and movement control. The editors would like to thank Professor Arthur Prochazka, who helped initially to set up this research topic, and all authors who contributed their articles to this research topic. Our appreciation also goes to the reviewers, who volunteered their time and effort to help achieve the goal of this research topic. We would also like to thank the staff members of editorial office of Frontiers in Computational Neuroscience for their expertise in the process of manuscript handling, publishing, and in bringing this ebook to the readers. The support from the Editor-in-Chief, Dr. Misha Tsodyks and Dr. Si Wu is crucial for this research topic to come to a successful conclusion. We are indebted to Dr. Si Li and Ms. Ting Xu, whose assistant is important for this ebook to become a reality. Finally, this work is supported in part by grants to Dr. Ning Lan from the Ministry of Science and Technology of China (2011CB013304), the Natural Science Foundation of China (No. 81271684, No. 61361160415, No. 81630050), and the Interdisciplinary Research Grant cross Engineering and Medicine by Shanghai Jiao Tong University (YG20148D09). Dr. Vincent Cheung is supported by startup funds from the Faculty of Medicine of The Chinese University of Hong Kong. Guest Associate Editors Ning Lan, Vincent Cheung, and Simon Gandevia

Author : Ahmed El Hady
Publisher : Academic Press
ISBN 13 : 0128026413
Total Pages : 304 pages
Book Rating : 4.10/5 ( download)

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Download or read book Closed Loop Neuroscience written by Ahmed El Hady and published by Academic Press. This book was released on 2016-09-08 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Closed Loop Neuroscience addresses the technical aspects of closed loop neurophysiology, presenting the implementation of these approaches spanning several domains of neuroscience, from cellular and network neurophysiology, through sensory and motor systems, and then clinical therapeutic devices. Although closed-loop approaches have long been a part of the neuroscientific toolbox, these techniques are only now gaining popularity in research and clinical applications. As there is not yet a comprehensive methods book addressing the topic as a whole, this volume fills that gap, presenting state-of-the-art approaches and the technical advancements that enable their application to different scientific problems in neuroscience. Presents the first volume to offer researchers a comprehensive overview of the technical realities of employing closed loop techniques in their work Offers application to in-vitro, in-vivo, and hybrid systems Contains an emphasis on the actual techniques used rather than on specific results obtained Includes exhaustive protocols and descriptions of software and hardware, making it easy for readers to implement the proposed methodologies Encompasses the clinical/neuroprosthetic aspect and how these systems can also be used to contribute to our understanding of basic neurophysiology Edited work with chapters authored by leaders in the field from around the globe – the broadest, most expert coverage available

Author : Matthias Bethge
Publisher : Frontiers E-books
ISBN 13 : 2889190129
Total Pages : 209 pages
Book Rating : 4.26/5 ( download)

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Download or read book Statistical analysis of multi-cell recordings: linking population coding models to experimental data written by Matthias Bethge and published by Frontiers E-books. This book was released on 2012-01-01 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern recording techniques such as multi-electrode arrays and 2-photon imaging are capable of simultaneously monitoring the activity of large neuronal ensembles at single cell resolution. This makes it possible to study the dynamics of neural populations of considerable size, and to gain insights into their computations and functional organization. The key challenge with multi-electrode recordings is their high-dimensional nature. Understanding this kind of data requires powerful statistical techniques for capturing the structure of the neural population responses and their relation with external stimuli or behavioral observations. Contributions to this Research Topic should advance statistical modeling of neural populations. Questions of particular interest include: 1. What classes of statistical methods are most useful for modeling population activity? 2. What are the main limitations of current approaches, and what can be done to overcome them? 3. How can statistical methods be used to empirically test existing models of (probabilistic) population coding? 4. What role can statistical methods play in formulating novel hypotheses about the principles of information processing in neural populations? This Research Topic is connected to a one day workshop at the Computational Neuroscience Meeting 2009 in Berlin (http://www.cnsorg.org/2009/workshops.shtml and http://www.kyb.tuebingen.mpg.de/bethge/workshops/cns2009/)

Author : Frank H. Eeckman
Publisher : Springer Science & Business Media
ISBN 13 : 1461540100
Total Pages : 400 pages
Book Rating : 4.06/5 ( download)

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Download or read book Analysis and Modeling of Neural Systems written by Frank H. Eeckman and published by Springer Science & Business Media. This book was released on 2012-02-02 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: I - Analysis and Modeling Tools and Techniques.- Section 1: Analysis.- Assembly Connectivity and Activity: Methods, Results, Interpretations.- Visualization of Cortical Connections With Voltage Sensitive Dyes.- Channels, Coupling, and Synchronized Rhythmic Bursting Activity.- Sparse-stimulation and Wiener Kernels.- Quantitative Search for Stimulus-Specific Patterns in the Human Electroencephalogram (EEG) During a Somatosensory Task.- Section 2: Modeling.- Functional Insights About Synaptic Inputs to Dendrites.- Dendritic Control of Hebbian Computations.- Low Threshold Spikes and Rhythmic Oscil.

Author : Tobias A. Mattei
Publisher : Frontiers Media SA
ISBN 13 : 2889199967
Total Pages : 273 pages
Book Rating : 4.69/5 ( download)

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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