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.
Author: Ahmed A. Moustafa
Publisher: John Wiley & Sons
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.
This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks.
Author: David Sterratt
Publisher: Cambridge University Press
The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.
This book caters to researchers and academics from the area of computational cognitive neuroscience.
Author: V. Srinivasa Chakravarthy
The book is a compendium of the aforementioned subclass of models of Basal Ganglia, which presents some the key existent theories of Basal Ganglia function. The book presents computational models of basal ganglia-related disorders, including Parkinson’s disease, schizophrenia, and addiction. Importantly, it highlights the applications of understanding the role of the basal ganglia to treat neurological and psychiatric disorders. The purpose of the present book is to amend and expand on James Houk’s book (MIT press; ASIN: B010BF4U9K) by providing a comprehensive overview on computational models of the basal ganglia. This book caters to researchers and academics from the area of computational cognitive neuroscience.
"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational ...
Author: Alonso, Eduardo
Publisher: IGI Global
"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--
Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques."--Jacket.
Author: Patricia Smith Churchland
Publisher: MIT Press
"The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques."--Jacket.
37 Cognitive Models DANIEL BENNETT AND YAEL NIV abstract Computational
psychiatry is a nascent field that seeks to use computational tools from
neuroscience and cognitive science to understand psychiatric illness. In this
chapter we ...
Author: David Poeppel
Publisher: MIT Press
The sixth edition of the foundational reference on cognitive neuroscience, with entirely new material that covers the latest research, experimental approaches, and measurement methodologies. Each edition of this classic reference has proved to be a benchmark in the developing field of cognitive neuroscience. The sixth edition of The Cognitive Neurosciences continues to chart new directions in the study of the biological underpinnings of complex cognition—the relationship between the structural and physiological mechanisms of the nervous system and the psychological reality of the mind. It offers entirely new material, reflecting recent advances in the field, covering the latest research, experimental approaches, and measurement methodologies. This sixth edition treats such foundational topics as memory, attention, and language, as well as other areas, including computational models of cognition, reward and decision making, social neuroscience, scientific ethics, and methods advances. Over the last twenty-five years, the cognitive neurosciences have seen the development of sophisticated tools and methods, including computational approaches that generate enormous data sets. This volume deploys these exciting new instruments but also emphasizes the value of theory, behavior, observation, and other time-tested scientific habits. Section editors Sarah-Jayne Blakemore and Ulman Lindenberger, Kalanit Grill-Spector and Maria Chait, Tomás Ryan and Charan Ranganath, Sabine Kastner and Steven Luck, Stanislas Dehaene and Josh McDermott, Rich Ivry and John Krakauer, Daphna Shohamy and Wolfram Schultz, Danielle Bassett and Nikolaus Kriegeskorte, Marina Bedny and Alfonso Caramazza, Liina Pylkkänen and Karen Emmorey, Mauricio Delgado and Elizabeth Phelps, Anjan Chatterjee and Adina Roskies
This volume includes papers originally presented at the 7th annual Computational Neuroscience Meeting (CNS'98) held in July of 1998 at the Fess Parker Doubletree Inn in Santa Barbara, California.
Author: J.M. Bower
This volume includes papers originally presented at the 7th annual Computational Neuroscience Meeting (CNS'98) held in July of 1998 at the Fess Parker Doubletree Inn in Santa Barbara, California. 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.
Different approaches, evaluations, methodologies and advanced studies have been included in this book. With state-of-the-art inputs by acclaimed experts of this field, this book targets students and researchers alike.
Author: Scott Carter
Computational neuroscience is the branch of neuroscience that uses mathematical models, theoretical analysis and abstractions, to understand the development, structure and information-processing of the nervous system. It also attempts to understand the principles that govern the physiology and cognitive abilities of the nervous system. Computational neuroscience models help in the understanding of biological phenomena at different spatial-temporal scales. It covers all aspects of membrane currents, proteins, network oscillations, learning, memory, etc. Research in computational neuroscience delves into the concepts of consciousness and the processes of cognition, sensory processing, memory and axonal patterning and development. This book discusses the fundamentals as well as modern approaches of computational neuroscience. It covers all the important aspects of modeling and their applications. Different approaches, evaluations, methodologies and advanced studies have been included in this book. With state-of-the-art inputs by acclaimed experts of this field, this book targets students and researchers alike.
The book "Cognitive and Computational Neuroscience - Principles, Algorithms and Applications" will answer the following question and statements: System-level neural modeling: what and why? We know a lot about the brain!
Author: Seyyed Abed Hosseini
Publisher: BoD – Books on Demand
The book "Cognitive and Computational Neuroscience - Principles, Algorithms and Applications" will answer the following question and statements: System-level neural modeling: what and why? We know a lot about the brain! Need to integrate data: molecular/cellular/system levels. Complexity: need to abstract away higher-order principles. Models are tools to develop explicit theories, constrained by multiple levels (neural and behavioral). Key: models (should) make novel testable predictions on both neural and behavioral levels. Models are useful tools for guiding experiments. The hope is that the information provided in this book will trigger new researches that will help to connect basic neuroscience to clinical medicine.
Computational. Neuroscience. Terrence J. Sejnowski and Tomaso A. Poggio,
editors Neural Nets in Electric Fish, ... John Rinzel, and Gordon M. Shepherd,
1995 Models of Information Processing in the Basal Ganglia, edited by James C.
Author: Paul Miller
Publisher: MIT Press
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.
This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a ...
Author: Daniel Durstewitz
This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. "Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function." Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego “This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “ Bruno B. Averbeck
This text introduces the reader to the main ideas in the field of computational cognitive neuroscience.
Author: Randall C. O'Reilly
Publisher: MIT Press
This text introduces the reader to the main ideas in the field of computational cognitive neuroscience. The aim of the discipline is to understand how the brain embodies the mind by using biologically based computational models which simulate neuronal networks.
Preface Theoretical analysis and computational modeling are important tools for
characterizing what nervous systems do , determining how they function , and
understanding why they operate in particular ways . Neuroscience encompasses
Author: Peter Dayan
Publisher: Computational Neuroscience Series
The construction and analysis of mathematical and computational models of neural systems.
How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities.
Author: Jianfeng Feng
Publisher: CRC Press
How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding.
Computational Models of Cognitive Control . Current Opinion in Neurobiology 20
( 2 ) : 257-261 . O'Reilly , R. C. , and Y. Munakata . 2000. Computational
Explorations in Cognitive Neuroscience . Cambridge , MA : The MIT Press .
Author: Peggy Series
"Computational psychiatry represents a novel and multidisciplinary approach to mental dysfunction. Computational psychiatry seeks to characterize mental dysfunction in terms of deviations from healthy brain computations over multiple time scales. It focuses on building mathematical models of neural or cognitive phenomena relevant to psychiatric diseases. One critical function of these models is their ability to bridge between low-level biological (neuroscience) and high-level cognitive features (psychiatric symptoms). This is the first textbook in the new field of computational psychiatry, designed for the next generation of scientists and clinicians who wish to apply computational models to modern diagnosis and treatment strategies"--
This book presents an integrated framework for developing and testing computational models in psychology and related disciplines.
Author: Simon Farrell
Publisher: Cambridge University Press
This book presents an integrated framework for developing and testing computational models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models.
MPI, OpenMP, Central Processing Units(CPUs), biologicallyinspired
computational models, neuroscience, irregular computation, sparse computation.
Introduction Neuroscience has undoubtedly provided a more in-depth
understanding of ...
Author: I. Foster
Publisher: IOS Press
The year 2019 marked four decades of cluster computing, a history that began in 1979 when the first cluster systems using Components Off The Shelf (COTS) became operational. This achievement resulted in a rapidly growing interest in affordable parallel computing for solving compute intensive and large scale problems. It also directly lead to the founding of the Parco conference series. Starting in 1983, the International Conference on Parallel Computing, ParCo, has long been a leading venue for discussions of important developments, applications, and future trends in cluster computing, parallel computing, and high-performance computing. ParCo2019, held in Prague, Czech Republic, from 10 – 13 September 2019, was no exception. Its papers, invited talks, and specialized mini-symposia addressed cutting-edge topics in computer architectures, programming methods for specialized devices such as field programmable gate arrays (FPGAs) and graphical processing units (GPUs), innovative applications of parallel computers, approaches to reproducibility in parallel computations, and other relevant areas. This book presents the proceedings of ParCo2019, with the goal of making the many fascinating topics discussed at the meeting accessible to a broader audience. The proceedings contains 57 contributions in total, all of which have been peer-reviewed after their presentation. These papers give a wide ranging overview of the current status of research, developments, and applications in parallel computing.
2007 International Symposium on Computational Models of Life Sciences Tuan
D. Pham, Xiaobo Zhou. REFERENCES 1 ... L. White , M. Toft , S. Kvam , M. Farrer
, and J. Aasly , Journal of Neuroscience Research 85 , 1288– 1294 ( 2007 ) . 19.
Author: Tuan D. Pham
Publisher: American Institute of Physics
This conference proceedings text features research papers that address novel applications of computer, physical, engineering and mathematical models for solving modern challenging problems in life sciences. All the papers, presented at the Computational Models for Life Sciences conference held in 2007, have been peer-reviewed. They cover a huge range of topics, including image analysis, computer vision, and pattern analysis and classification, among many others.
Computational models of active states Computational models were based on four
morphologically reconstructed pyramidal neurons from cats (one from layer II—III,
two from layer V and one from layer VI), which were obtained from two ...
Author: J.M. Bower
This volume includes papers originally presented at the 8th annual Computational Neuroscience meeting (CNS'99) held in July of 1999 in Pittsburgh, Pennsylvania. The CNS meetings bring together computational neuroscientists representing many different fields and backgrounds as well as experimental preparations and theoretical approaches. The papers published here range across vast levels of scale from cellular mechanisms to cognitive brain studies. The subjects of the research include many different preparations from invertebrates to humans. In all cases the work described in this volume is focused on understanding how nervous systems compute. The research described includes subjects like neural coding and neuronal dendrites and reflects a trend towards forging links between cognitive research and neurobiology. Accordingly, this volume reflects the breadth and depth of current research in computational neuroscience taking place throughout the world.