Computational Neuroscience Models at Different Levels of Abstraction for Synaptic Plasticity, Astrocyte Modulation of Synchronization and Systems Memory Consolidation

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ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Book Synopsis Computational Neuroscience Models at Different Levels of Abstraction for Synaptic Plasticity, Astrocyte Modulation of Synchronization and Systems Memory Consolidation by : Lisa Blum Moyse

Download or read book Computational Neuroscience Models at Different Levels of Abstraction for Synaptic Plasticity, Astrocyte Modulation of Synchronization and Systems Memory Consolidation written by Lisa Blum Moyse and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, theoretical models with increasing levels of abstraction are developed to address questions arising from neuroscience experiments. They are studied using numerical and analytical approaches. With Laurent Venance's laboratory (Paris), we have developed an ITDP (input-timing-dependent plasticity) protocol model for the plasticity of cortico- and thalamo-striatal synapses. The model has been calibrated with ex vivo data and will be used to determine the presence of synaptic plasticity in vivo, in behavioral experiments aimed at determining the role of cortical and thalamic inputs in motor learning. At the level of neuronal populations, I have studied the modulation of neuronal collective behaviors by astrocytes, in particular Up-Down synchronization, a spontaneous alternation between periods of high collective activity and periods of silence. I have proposed rate and spiking neural network models of interconnected populations of neurons and astrocytes. They offer explanations of how astrocytes induce Up-Down transitions. Astrocytes are also probably involved in the generation of epileptic seizures, during which neuronal synchronization is impaired. Based on the above models, I have developed a neuron-astrocyte network with a cluster connectivity, showing the transition between Up-Down dynamics and events of very high activity mimicking an epileptic seizure. Finally, at the level of the brain itself, I studied the standard theory of consolidation, according to which short-term memory in the hippocampus enables the consolidation of long-term memory in the neocortex. I have sought to explain this phenomenon by integrating biological hypotheses - the size of the neocortex explaining the slowness of learning, and neurogenesis in the hippocampus explaining the erasure of its memory - into a model of interconnected neural fields that well reproduces the main features of the theory.

Computational Systems Biology Of Synaptic Plasticity: Modelling Of Biochemical Pathways Related To Memory Formation And Impairement

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Publisher : #N/A
ISBN 13 : 1786343398
Total Pages : 363 pages
Book Rating : 4.7/5 (863 download)

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Book Synopsis Computational Systems Biology Of Synaptic Plasticity: Modelling Of Biochemical Pathways Related To Memory Formation And Impairement by : Don Kulasiri

Download or read book Computational Systems Biology Of Synaptic Plasticity: Modelling Of Biochemical Pathways Related To Memory Formation And Impairement written by Don Kulasiri and published by #N/A. This book was released on 2017-06-09 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the power of mathematical thinking in understanding the biological complexity that exists within the brain. It looks at the latest research on modelling of biochemical pathways within synapses, and provides a clear background for the study of mathematical models related to systems biology. Discussion then focusses on developments in computational models based on networks linked to synaptic plasticity. The models are used to understand memory formation and impairment and they provide a mathematical basis for memory research.Computational Systems Biology of Synaptic Plasticity is a valuable source of knowledge to postgraduate students and researchers in computational systems biology, and as a reference book for various techniques that are needed in modelling biological processes.

Computational Models for Neuroscience

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Publisher : Springer Science & Business Media
ISBN 13 : 1447100859
Total Pages : 311 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Computational Models for Neuroscience by : Robert Hecht-Nielsen

Download or read book Computational Models for Neuroscience written by Robert Hecht-Nielsen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Formal study of neuroscience (broadly defined) has been underway for millennia. For example, writing 2,350 years ago, Aristotle! asserted that association - of which he defined three specific varieties - lies at the center of human cognition. Over the past two centuries, the simultaneous rapid advancements of technology and (conse quently) per capita economic output have fueled an exponentially increasing effort in neuroscience research. Today, thanks to the accumulated efforts of hundreds of thousands of scientists, we possess an enormous body of knowledge about the mind and brain. Unfortunately, much of this knowledge is in the form of isolated factoids. In terms of "big picture" understanding, surprisingly little progress has been made since Aristotle. In some arenas we have probably suffered negative progress because certain neuroscience and neurophilosophy precepts have clouded our self-knowledge; causing us to become largely oblivious to some of the most profound and fundamental aspects of our nature (such as the highly distinctive propensity of all higher mammals to automatically seg ment all aspects of the world into distinct holistic objects and the massive reorganiza tion of large portions of our brains that ensues when we encounter completely new environments and life situations). At this epoch, neuroscience is like a huge collection of small, jagged, jigsaw puz zle pieces piled in a mound in a large warehouse (with neuroscientists going in and tossing more pieces onto the mound every month).

Computational Models of Brain and Behavior

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Publisher : John Wiley & Sons
ISBN 13 : 1119159180
Total Pages : 845 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Computational Models of Brain and Behavior by : Ahmed A. Moustafa

Download or read book Computational Models of Brain and Behavior written by Ahmed A. Moustafa and published by John Wiley & Sons. This book was released on 2017-09-18 with total page 845 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

Computational Systems Biology of Synaptic Plasticity

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Publisher : Wspc (Europe)
ISBN 13 : 9781786343376
Total Pages : 326 pages
Book Rating : 4.3/5 (433 download)

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Book Synopsis Computational Systems Biology of Synaptic Plasticity by : Don Kulasiri

Download or read book Computational Systems Biology of Synaptic Plasticity written by Don Kulasiri and published by Wspc (Europe). This book was released on 2017 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the power of mathematical thinking in understanding the biological complexity that exists within the brain. It looks at the latest research on modelling of biochemical pathways within synapses, and provides a clear background for the study of mathematical models related to systems biology. Discussion then focusses on developments in computational models based on networks linked to synaptic plasticity. The models are used to understand memory formation and impairment and they provide a mathematical basis for memory research. Computational Systems Biology of Synaptic Plasticity is a valuable source of knowledge to postgraduate students and researchers in computational systems biology, and as a reference book for various techniques that are needed in modelling biological processes.

Emergent neural computation from the interaction of different forms of plasticity

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Publisher : Frontiers Media SA
ISBN 13 : 2889197883
Total Pages : 195 pages
Book Rating : 4.8/5 (891 download)

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Book Synopsis Emergent neural computation from the interaction of different forms of plasticity by : Cristina Savin

Download or read book Emergent neural computation from the interaction of different forms of plasticity written by Cristina Savin and published by Frontiers Media SA. This book was released on 2016-03-22 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the propagation of neural activity through synapses, to the integration of signals in the dendritic arbor, and the processes determining action potential generation, virtually all aspects of neural processing are plastic. This plasticity underlies the remarkable versatility and robustness of cortical circuits: it enables the brain to learn regularities in its sensory inputs, to remember the past, and to recover function after injury. While much of the research into learning and memory has focused on forms of Hebbian plasticity at excitatory synapses (LTD/LTP, STDP), several other plasticity mechanisms have been characterized experimentally, including the plasticity of inhibitory circuits (Kullmann, 2012), synaptic scaling (Turrigiano, 2011) and intrinsic plasticity (Zhang and Linden, 2003). However, our current understanding of the computational roles of these plasticity mechanisms remains rudimentary at best. While traditionally they are assumed to serve a homeostatic purpose, counterbalancing the destabilizing effects of Hebbian learning, recent work suggests that they can have a profound impact on circuit function (Savin 2010, Vogels 2011, Keck 2012). Hence, theoretical investigation into the functional implications of these mechanisms may shed new light on the computational principles at work in neural circuits. This Research Topic of Frontiers in Computational Neuroscience aims to bring together recent advances in theoretical modeling of different plasticity mechanisms and of their contributions to circuit function. Topics of interest include the computational roles of plasticity of inhibitory circuitry, metaplasticity, synaptic scaling, intrinsic plasticity, plasticity within the dendritic arbor and in particular studies on the interplay between homeostatic and Hebbian plasticity, and their joint contribution to network function.

Anatomy and Plasticity in Large-Scale Brain Models

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Publisher : Frontiers Media SA
ISBN 13 : 2889450651
Total Pages : 175 pages
Book Rating : 4.8/5 (894 download)

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Book Synopsis Anatomy and Plasticity in Large-Scale Brain Models by : Markus Butz

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.

Computational Modelling of the Brain

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Publisher : Springer Nature
ISBN 13 : 3030894398
Total Pages : 361 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Computational Modelling of the Brain by : Michele Giugliano

Download or read book Computational Modelling of the Brain written by Michele Giugliano and published by Springer Nature. This book was released on 2022-04-26 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers an up-to-date overview of essential concepts and modern approaches to computational modelling, including the use of experimental techniques related to or directly inspired by them. The book introduces, at increasing levels of complexity and with the non-specialist in mind, state-of-the-art topics ranging from single-cell and molecular descriptions to circuits and networks. Four major themes are covered, including subcellular modelling of ion channels and signalling pathways at the molecular level, single-cell modelling at different levels of spatial complexity, network modelling from local microcircuits to large-scale simulations of entire brain areas and practical examples. Each chapter presents a systematic overview of a specific topic and provides the reader with the fundamental tools needed to understand the computational modelling of neural dynamics. This book is aimed at experimenters and graduate students with little or no prior knowledge of modelling who are interested in learning about computational models from the single molecule to the inter-areal communication of brain structures. The book will appeal to computational neuroscientists, engineers, physicists and mathematicians interested in contributing to the field of neuroscience. Chapters 6, 10 and 11 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

The Rewiring Brain

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Publisher : Academic Press
ISBN 13 : 0128038721
Total Pages : 586 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis The Rewiring Brain by : Arjen van Ooyen

Download or read book The Rewiring Brain written by Arjen van Ooyen and published by Academic Press. This book was released on 2017-06-23 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: The adult brain is not as hard-wired as traditionally thought. By modifying their small- or large-scale morphology, neurons can make new synaptic connections or break existing ones (structural plasticity). Structural changes accompany memory formation and learning, and are induced by neurogenesis, neurodegeneration and brain injury such as stroke. Exploring the role of structural plasticity in the brain can be greatly assisted by mathematical and computational models, as they enable us to bridge the gap between system-level dynamics and lower level cellular and molecular processes. However, most traditional neural network models have fixed neuronal morphologies and a static connectivity pattern, with plasticity merely arising from changes in the strength of existing synapses (synaptic plasticity). In The Rewiring Brain, the editors bring together for the first time contemporary modeling studies that investigate the implications of structural plasticity for brain function and pathology. Starting with an experimental background on structural plasticity in the adult brain, the book covers computational studies on homeostatic structural plasticity, the impact of structural plasticity on cognition and cortical connectivity, the interaction between synaptic and structural plasticity, neurogenesis-related structural plasticity, and structural plasticity in neurological disorders. Structural plasticity adds a whole new dimension to brain plasticity, and The Rewiring Brain shows how computational approaches may help to gain a better understanding of the full adaptive potential of the adult brain. The book is written for both computational and experimental neuroscientists. Reviews the current state of knowledge of structural plasticity in the adult brain Gives a comprehensive overview of computational studies on structural plasticity Provides insights into the potential driving forces of structural plasticity and the functional implications of structural plasticity for learning and memory Serves as inspiration for developing novel treatment strategies for stimulating functional repair after brain damage

Neural and Computational Modeling of Movement Control

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Publisher : Frontiers Media SA
ISBN 13 : 2889451305
Total Pages : 180 pages
Book Rating : 4.8/5 (894 download)

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Book Synopsis Neural and Computational Modeling of Movement Control by : Ning Lan

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

The Relevance of the Time Domain to Neural Network Models

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Publisher : Springer Science & Business Media
ISBN 13 : 1461407249
Total Pages : 234 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis The Relevance of the Time Domain to Neural Network Models by : A. Ravishankar Rao

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

Biophysically based Computational Models of Astrocyte ~ Neuron Coupling and their Functional Significance

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Publisher : Frontiers E-books
ISBN 13 : 2889191788
Total Pages : 140 pages
Book Rating : 4.8/5 (891 download)

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Book Synopsis Biophysically based Computational Models of Astrocyte ~ Neuron Coupling and their Functional Significance by : John Wade

Download or read book Biophysically based Computational Models of Astrocyte ~ Neuron Coupling and their Functional Significance written by John Wade and published by Frontiers E-books. This book was released on 2014-03-21 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuroscientists are increasingly becoming more interested in modelling brain functions where capturing the biophysical mechanisms underpinning these functions requires plausible models at the level of neuron cells. However, cell level models are still very much in the embryo stage and therefore there is a need to advance the level of biological realism at the level of neurons/synapses. Recent publications have highlighted that astrocytes continually exchange information with multiple synapses; if we are to fully appreciate this dynamic and coordinated interplay between these cells then more research on bidirectional signalling between astrocytes and neurons is required. A better understanding of astrocyte-neuron cell coupling would provide the building block for studying the regulatory capability of astrocytes networks on a large scale. For example, it is believed that local and global signalling via astrocytes underpins brain functions like synchrony, learning, memory and self repair. This Research Topic aims to report on current research work which focuses on understanding and modelling the interaction between astrocytes and neurons at the cellular level (Bottom up) and at network level (Top down). Understanding astrocytic regulation of neural activity is crucial if we are to capture how information is represented and processed across large neuronal ensembles in humans.

Multiscale Models of Brain Disorders

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Publisher : Springer Nature
ISBN 13 : 3030188302
Total Pages : 222 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Multiscale Models of Brain Disorders by : Vassilis Cutsuridis

Download or read book Multiscale Models of Brain Disorders written by Vassilis Cutsuridis and published by Springer Nature. This book was released on 2019-10-11 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on our current understanding of brain dynamics in various brain disorders (e.g. epilepsy, Alzheimer’s and Parkinson’s disease) and how the multi-scale, multi-level tools of computational neuroscience can enhance this understanding. In recent years, there have been significant advances in the study of the dynamics of the disordered brain at both the microscopic and the macroscopic levels. This understanding can be furthered by the application of multi-scale computational models as integrative principles that may link single neuron dynamics and the dynamics of local and distant brain regions observed using human EEG, ERPs, MEG, LFPs and fMRI. Focusing on the computational models that are used to study movement, memory and cognitive disorders as well as epilepsy and consciousness related diseases, the book brings together physiologists and anatomists investigating cortical circuits; cognitive neuroscientists studying brain dynamics and behavior by means of EEG and functional magnetic resonance imaging (fMRI); and computational neuroscientists using neural modeling techniques to explore local and large-scale disordered brain dynamics. Covering topics that have a significant impact on the field of medicine, neuroscience and computer science, the book appeals to a diverse group of investigators.

Modeling Temporal Patterns of Neural Synchronization

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Publisher :
ISBN 13 :
Total Pages : 144 pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Modeling Temporal Patterns of Neural Synchronization by : Joel Zirkle

Download or read book Modeling Temporal Patterns of Neural Synchronization written by Joel Zirkle and published by . This book was released on 2020 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural synchrony in the brain at rest is usually variable and intermittent, thus intervals of predominantly synchronized activity are interrupted by intervals of desynchronized activity. Prior studies suggested that this temporal structure of the weakly synchronous activity might be functionally significant: many short desynchronizations may be functionally different from few long desynchronizations, even if the average synchrony level is the same. In this thesis, we use computational neuroscience methods to investigate the effects of (i) spike-timing dependent plasticity (STDP) and (ii) noise on the temporal patterns of synchronization in a simple model. The model is composed of two conductance-based neurons connected via excitatory unidirectional synapses. In (i) these excitatory synapses are made plastic, in (ii) two different types of noise implementation to model the stochasticity of membrane ion channels is considered. The plasticity results are taken from our recently published article, while the noise results are currently being compiled into a manuscript. The dynamics of this network is subjected to the time-series analysis methods used in prior experimental studies. We provide numerical evidence that both STDP and channel noise can alter the synchronized dynamics in the network in several ways. This depends on the time scale that plasticity acts on and the intensity of the noise. However, in general, the action of STDP and noise in the simple network considered here is to promote dynamics with short desynchronizations (i.e. dynamics reminiscent of that observed in experimental studies) over dynamics with longer desynchronizations.

Interaction of Synaptic Plasticity with Oscillations and Connectivity Lesion for Memory and Learning in Neural Network Models

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Publisher :
ISBN 13 :
Total Pages : 210 pages
Book Rating : 4.:/5 (128 download)

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Book Synopsis Interaction of Synaptic Plasticity with Oscillations and Connectivity Lesion for Memory and Learning in Neural Network Models by : Kwan Tung Li

Download or read book Interaction of Synaptic Plasticity with Oscillations and Connectivity Lesion for Memory and Learning in Neural Network Models written by Kwan Tung Li and published by . This book was released on 2021 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning is a common ability, accompanied by gamma oscillation, across species to acquire new knowledge stored in the hippocampus and neocortex into short-term and long-term memory, respectively. Thus, memory is first stored as short-term memory quickly and then consolidated into long-term memory in a longer timescale. Excitatory to excitatory (E → E ) spike-timing-dependent plasticity (STDP), an experimentally observable synaptic plasticity, is a widely used mechanism to form synaptic clusters in neural network models, where memory is proposed to be stored in strengthened synapses within the cluster. However, the interaction between gamma oscillation and STDP is unclear. On the other hand, the role of inhibitory plasticity in memory cluster formation attracts the attention of scientists in recent years, but it is not well understood yet because of the numerous species of inhibitory neurons and their plasticity. Besides, connectivity lesion, such as induced by Alzheimer's disease, causes memory deficits and abnormal gamma oscillation, but its relation to memory cluster is still an open question. My doctoral research thus aimed to study the interaction among different types of synaptic plasticity, gamma oscillation and circuit connectivity in memory learning and recall through computer simulation of the integrate-and-fire neuronal network of excitatory and inhibitory (E-I) neurons. i In the first part of my study, we explored the interaction between gamma oscillation and E → E STDP in an E-I integrate-and-fire neuronal network with triplet STDP, heterosynaptic plasticity, and transmitter-induced plasticity. We show that the plasticity performance depends on the synchronization levels accompanied by the emergence of gamma oscillations. Moreover, gamma oscillation is beneficial to form a unique network structure through synaptic potentiation. Secondly, we were inspired by an experimental result to study the functional role of excitatory to inhibitory ( E → I ) plasticity in memory consolidation through a feedforward two-layer E-I circuit model. We found that E → I plasticity can prevent overexcitation and assist memory cluster formation. We also predict that suitable pulse input to inhibitory neurons can rescue the memory performance deficits in the absence of E → I plasticity. Thirdly, we used E-I neuronal network model to investigate the effect of connectivity reduction as a result of Alzheimer's diseases on the interaction between circuit dynamics and STDP and the rescue of memory performance by optogenetic stimulation found in the experiments. It is found that the firing rate of the persistent activity is increased if connectivity is reduced mildly because of a transition from synchronous state to asynchronous state, while the persistent activity cannot be maintained and the firing rate is reduced with severe connectivity reduction. iv Furthermore, we found that stimulation with gamma frequency in circuits with connectivity lesion is the best for memory rescue because it can suppress the activation of the memory clusters that were initially activated in the lesion circuit. Moreover, we found that connectivity reduction causes the merging of memory clusters and the deterioration of existing memories during learning new memory with STDP. The whole study gives more insight into the co-evolution between microscopic synaptic dynamics, such as synaptic weight change, firing rate and synchronization of neuron spikes, and macroscopic phenomena, like gamma oscillation, memory performance, and connectivity. Our results may have implications in clinical applications to develop suitable brain stimulation schemes for memory rescue in neurodegenerative diseases. Furthermore, the understanding of the interaction among neural connectivity, dynamics, and plasticity may also offer insight into braininspired neural networks in artificial intelligence.

Methods in Neuronal Modeling

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Publisher : Bradford Book
ISBN 13 : 9780262610711
Total Pages : 524 pages
Book Rating : 4.6/5 (17 download)

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Book Synopsis Methods in Neuronal Modeling by : Christof Koch

Download or read book Methods in Neuronal Modeling written by Christof Koch and published by Bradford Book. This book was released on 1991 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Neural and Brain Modeling

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Publisher :
ISBN 13 :
Total Pages : 662 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Neural and Brain Modeling by : Ronald J. MacGregor

Download or read book Neural and Brain Modeling written by Ronald J. MacGregor and published by . This book was released on 1987 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: