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The information presented in the Handbook of Brain Microcircuits was previously dispersed across the literature. In fact, some microcircuits were previously brought together for selected regions in The Synaptic Organization of the Brain edited by Gordon Shepherd (2003) and Microcircuits edited by Sten Grillner and Ann Graybiel (2006). This handbook greatly extends that coverage to over 40 regions of the vertebrate and invertebrate nervous system becoming the go-to source for key circuits within the neurosciences. In order to focus on principles, each chapter is brief, organized around 1-3 wiri

Brain. --- Neural circuitry. --- Neurophysiology. --- Brain --- Neurons --- Nerve Net. --- Synaptic Transmission. --- physiology.

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Virtually all scientific problems in neuroscience require mathematical analysis, and all neuroscientists are increasingly required to have a significant understanding of mathematical methods. There is currently no comprehensive, integrated introductory book on the use of mathematics in neuroscience; existing books either concentrate solely on theoretical modeling or discuss mathematical concepts for the treatment of very specific problems. This book fills this need by systematically introducing mathematical and computational tools in precisely the contexts that first established their important

Computational biology -- Methods. --- Models, Neurological. --- Nerve net. --- Neurons -- Physiology. --- Electrophysiological Processes --- Signal Transduction --- Models, Biological --- Nervous System Physiological Processes --- Cells --- Biological Science Disciplines --- Phenomena and Processes --- Nervous System --- Biology --- Nervous System Physiological Phenomena --- Cell Physiological Processes --- Models, Theoretical --- Electrophysiological Phenomena --- Anatomy --- Natural Science Disciplines --- Biochemical Processes --- Physiological Processes --- Nerve Net --- Synaptic Transmission --- Mathematical Concepts --- Neurosciences --- Computational Biology --- Neurons --- Models, Neurological --- Investigative Techniques --- Cell Physiological Phenomena --- Musculoskeletal and Neural Physiological Phenomena --- Biochemical Phenomena --- Physiological Phenomena --- Chemical Processes --- Disciplines and Occupations --- Chemical Phenomena --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Medicine --- Neurosciences. --- Mathematics. --- Medical mathematics --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system --- Health Workforce

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Neuroscience has long been focused on understanding neural plasticity in both development and adulthood. However, experimental work in this area has focused almost entirely on plasticity at excitatory synapses. A growing body of evidence suggests that plasticity at inhibitory GABAergic and glycinergic synapses is of critical importance during both development and aging. Only a few investigators have been engaged in research on how inhibitory circuits are formed during development or how they are involved in plasticity of developing sensory and motor circuitry. Developmental Plasticity of Inhibitory Circuitry approaches the subject of inhibitory plasticity from several levels of analysis, from synapses to circuits to systems to clinical, summarizing several possible mechanisms and collecting some of the most fascinating work in this under-studied area. It is meant to provide an overview for basic and clinical researchers and students interested in neural plasticity and to stimulate further research. About the Editor: Dr. Sarah L. Pallas is a Professor of Neuroscience and Biology at Georgia State University. She earned her Ph.D. in Neurobiology and Behavior at Cornell University, under the tutelage of Dr. Barbara Finlay. Her postdoctoral training was received at M.I.T. in the laboratory of Dr. Mriganka Sur. Her research concerns developmental neurobiology and sensory physiology, and in particular the role of sensory experience in the development and plasticity of neural circuits.

Cognitive neuroscience. --- Developmental neurobiology. --- Posner, Michael I. --- Neural circuitry --- Neuroplasticity --- Developmental neurobiology --- Biological Science Disciplines --- Physiological Processes --- Cells --- Nervous System Physiological Processes --- Anatomy --- Growth and Development --- Nervous System --- Neurons --- Neuronal Plasticity --- Physiology --- Physiological Phenomena --- Natural Science Disciplines --- Nervous System Physiological Phenomena --- Phenomena and Processes --- Disciplines and Occupations --- Musculoskeletal and Neural Physiological Phenomena --- Medicine --- Human Anatomy & Physiology --- Neuroscience --- Neurology --- Health & Biological Sciences --- Neural circuitry. --- Adaptation. --- Adaptation of neural circuitry --- Circuitry, Neural --- Circuits, Neural --- Nerve net --- Nerve network --- Neural circuits --- Neurocircuitry --- Neuronal circuitry --- Medicine. --- Neurosciences. --- Neurology. --- Rehabilitation medicine. --- Developmental biology. --- Neurobiology. --- Biomedicine. --- Developmental Biology. --- Rehabilitation Medicine. --- Adaptation (Physiology) --- Electrophysiology --- Nervous system --- Neural networks (Neurobiology) --- Reflexes --- Rehabilitation. --- Neurosciences --- Development (Biology) --- Biology --- Growth --- Ontogeny --- Neuropsychiatry --- Neural sciences --- Neurological sciences --- Medical sciences --- Diseases --- Neurology . --- Medicine, Rehabilitation --- Rehabilitation medicine --- Rehabilitation --- Medicine, Physical

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Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.

Neural networks (Computer science). --- Sensitivity theory (Mathematics). --- Neural networks (Computer science) --- Sensitivity theory (Mathematics) --- Mechanical Engineering --- Engineering & Applied Sciences --- Computer Science --- Mechanical Engineering - General --- Information Technology --- Artificial Intelligence --- Neural circuitry. --- Circuitry, Neural --- Circuits, Neural --- Nerve net --- Nerve network --- Neural circuits --- Neurocircuitry --- Neuronal circuitry --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Computer science. --- Artificial intelligence. --- Computer simulation. --- Pattern recognition. --- Statistical physics. --- Dynamical systems. --- Engineering design. --- Control engineering. --- Robotics. --- Mechatronics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Control, Robotics, Mechatronics. --- Statistical Physics, Dynamical Systems and Complexity. --- Pattern Recognition. --- Simulation and Modeling. --- Engineering Design. --- Mechanical engineering --- Microelectronics --- Microelectromechanical systems --- Automation --- Machine theory --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Programmable controllers --- Design, Engineering --- Engineering --- Industrial design --- Strains and stresses --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mathematical statistics --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Design --- Statistical methods --- Electrophysiology --- Nervous system --- Neural networks (Neurobiology) --- Reflexes --- Artificial intelligence --- Natural computation --- Soft computing --- Optical pattern recognition. --- Artificial Intelligence. --- Complex Systems. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Automation. --- System theory. --- Pattern recognition systems. --- Control, Robotics, Automation. --- Automated Pattern Recognition. --- Computer Modelling. --- Pattern classification systems --- Pattern recognition computers --- Computer vision --- Systems, Theory of --- Systems science --- Automatic factories --- Automatic production --- Computer control --- Engineering cybernetics --- Factories --- Industrial engineering --- Mechanization --- Assembly-line methods --- Automatic control --- Automatic machinery --- CAD/CAM systems --- Robotics --- Philosophy