Computers And Computations In The Neurosciences
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Computers and Computations in the Neurosciences
Methods in Neurosciences, Volume 10: Computers and Computations in the Neurosciences discusses the use of computers in the neurosciences. The book deals with data collection, analysis, and modeling, with emphasis on the use of computers. Section I involves data collection using a personal microcomputer system. One paper presents a tutorial on using a PC-based motor control composed of an electronic circuit to adjust the motion of a light microscope stage through a software program. Other papers discuss computer methods in nuclei cartography and a computer-assisted quantitative receptor autoradiography in studying receptor density distribution. Section II deals with data analysis and some computer programs for kinetic modeling of gene expression in neurons. The book also discusses a computerized analysis of opioid receptor heterogeneity by ligand binding in test animals using computerized programs instead of employing manual or graphical methods. Computerized curve-fitting allows the researcher to utilize a more precise mathematical model to describe the binding of one ligand to one class of sites. Section III evaluates data modeling and simulations and describes the practicality of using computers to design model ion channels. Another paper discusses a graphical interaction program called MEMPOT to simulate an electrophysiological investigation of the properties of the membrane potential in stimulated cells. The book also presents a quantitative data gathered from computer simulation of the factors that affect neuronal density per measured sections. The book is suitable for microbiologists, biochemists, neuroscientists, and researchers in the field of medical research, as well as for advanced computer programmers in medical research work.
From Computer to Brain
Author: William W. Lytton
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-05-08
Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common ground. This book does this by making ancillary material available in an appendix and providing basic explanations without becoming bogged down in unnecessary details. The book will be suitable for undergraduates and beginning graduate students taking a computational neuroscience course and also to anyone with an interest in the uses of the computer in modeling the nervous system.
Quantum Computing for the Brain
Quantum Computing for the Brain argues that the brain is the killer application for quantum computing. No other system is as complex, as multidimensional in time and space, as dynamic, as less well-understood, as of peak interest, and as in need of three-dimensional modeling as it functions in real-life, as the brain. Quantum computing has emerged as a platform suited to contemporary data processing needs, surpassing classical computing and supercomputing. This book shows how quantum computing's increased capacity to model classical data with quantum states and the ability to run more complex permutations of problems can be employed in neuroscience applications such as neural signaling and synaptic integration. State-of-the-art methods are discussed such as quantum machine learning, tensor networks, Born machines, quantum kernel learning, wavelet transforms, Rydberg atom arrays, ion traps, boson sampling, graph-theoretic models, quantum optical machine learning, neuromorphic architectures, spiking neural networks, quantum teleportation, and quantum walks. Quantum Computing for the Brain is a comprehensive one-stop resource for an improved understanding of the converging research frontiers of foundational physics, information theory, and neuroscience in the context of quantum computing.