Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes.Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation.Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

This collection of fifteen previously published papers, some of them not widely available, have been carefully chosen and annotated by Rall's colleagues and other leading neuroscientists.

The authoritative reference on NEURON, the simulation environment for modeling biological neurons and neural networks that enjoys wide use in the experimental and computational neuroscience communities. This book shows how to use NEURON to construct and apply empirically based models. Written primarily for neuroscience investigators, teachers, and students, it assumes no previous knowledge of computer programming or numerical methods. Readers with a background in the physical sciences or mathematics, who have some knowledge about brain cells and circuits and are interested in computational modeling, will also find it helpful. The NEURON Book covers material that ranges from the inner workings of this program, to practical considerations involved in specifying the anatomical and biophysical properties that are to be represented in models. It uses a problem-solving approach, with many working examples that readers can try for themselves.

During the second half of the 20th century, the introduction of a variety of new techniques greatly expanded our knowledge of the structure of nerve and neuroglial cells and of the organization of the nervous system at the cellular level. This new information has, however, generally been scattered throughout the literature, making it difficult for investigators to gain an overall vision of the discipline. The author has marshalled these scattered data to provide a systematic overview of the field placed within a historical context. He has correlated the major new findings with the classical notions of light microscopy and has integrated them with elements of biochemistry and cell physiology. After the publication of the first edition, the introduction of new microscopies and especially the growth of molecular biology have greatly expanded our knowledge, in particular on the intercellular communication in the nervous system and on the roles of neuroglial cells. These achievements have made a second edition of the book necessary. The fully revised and updated text, together with unique high-quality illustrations of structures of the central and peripheral nervous system, ensure that this new edition will be invaluable for neuroscientists, particularly those involved in cellular neuropathology, neurochemistry, neurophysiology and molecular neurobiology.

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.

Dendrites are complex neuronal structures that receive and integrate synaptic input from other nerve cells. They therefore play a critical role in brain function. Although dendrites were discovered over a century ago, due to the development of powerful new techniques there has been a dramatic resurgence of interest in the properties and function of these beautiful structures. This is the third edition of the first book devoted exclusively to dendrites. It contains a comprehensive survey of the current state of dendritic research across a wide range of topics, from dendritic morphology, evolution, development, and plasticity through to the electrical, biochemical and computational properties of dendrites, and finally to the key role of dendrites in brain disease. The third edition has been thoroughly revised, with the addition of a number of new chapters and comprehensive updates or rewrites of existing chapters by leading experts. "Dendrites" will be of interest to researchers and students in neuroscience and related fields, as well as to anyone interested in how the brain works.

This book describes a unique combination of research programs based on a striking variety of hypotheses and procedures directed toward understanding the sources and consequences of neurobehavioral plasticity. This remarkable attribute of the nervous system -- to be pliable and capable of being shaped or formed by natural or artificial sources toward adaptation or maladaptation -- is considered in terms of the neurochemical forces and neuroanatomical structure that has been found to be pivotal for this function. The impetus for this volume was a symposium held to honor Robert L. Isaacson for his scientific and pedagogical achievements as well as his contributions to behavioral neuroscience. Corresponding to his three major research interests, the book is divided into three sections as follows: * the first explores the relationship between the limbic system and behavior, with an emphasis on learning and memory; * the second considers -- through a wide range of approaches -- issues of plasticity in behavior and brain; and * the third deals with neural and chemical determinants of normal and abnormal behavior. This volume is not only a fitting tribute to Isaacson, but also an unusual collection of new evidence, procedures, and theories destined to have significant influence on behavioral neuroscience.