Bøger i Springer Series in Computational Neuroscience serien

This book brings together a selection of papers by George Gerstein, representing his long-term endeavor of making neuroscience into a more rigorous science inspired by physics, where he had his roots. Professor Gerstein was many years ahead of the field, consistently striving for quantitative analyses, mechanistic models, and conceptual clarity.  In doing so, he pioneered Computational Neuroscience, many years before the term itself was born.  The overarching goal of George Gerstein's research was to understand the functional organization of neuronal networks in the brain. The editors of this book have compiled a selection of George Gerstein's many seminal contributions to neuroscience--be they experimental, theoretical or computational--into a single, comprehensive volume .The aim is to provide readers with a fresh introduction of these various concepts in the original literature.  The volume is organized  in a series of chapters by subject, ordered in time, each one containing one or more of George Gerstein's papers.

Since information in the brain is processed by the exchange of spikes among neurons, a study of such group dynamics is extremely important in understanding hippocampus dependent memory. These spike patterns and local field potentials (LFPs) have been analyzed by various statistical methods. These studies have led to important findings of memory information processing. For example, memory-trace replay, a reactivation of behaviorally induced neural patterns during subsequent sleep, has been suggested to play an important role in memory consolidation. It has also been suggested that a ripple/sharp wave event (one of the characteristics of LFPs in the hippocampus) and spiking activity in the cortex have a specific relationship that may facilitate the consolidation of hippocampal dependent memory from the hippocampus to the cortex. The book will provide a state-of-the-art finding of memory information processing through the analysis of multi-neuronal data. The first half of the book is devoted to this analysis aspect. Understanding memory information representation and its consolidation, however, cannot be achieved only by analyzing the data. It is extremely important to construct a computational model to seek an underlying mathematical principle. In other words, an entire picture of hippocampus dependent memory system would be elucidated through close collaboration among experiments, data analysis, and computational modeling. Not only does computational modeling benefit the data analysis of multi-electrode recordings, but it also provides useful insight for future experiments and analyses. The second half of the book will be devoted to the computational modeling of hippocampus-dependent memory.

This book examines noise in neurons, emphasizing synaptic noise. It includes a combination of experimental, theoretical and computational results showing how noise is inherent to neuronal activity, and how it can be important for neuronal computations.

Reviews the most intriguing applications of fractal analysis in neuroscience with a focus on current and future potential, limits, advantages, and disadvantages.

Neuromechanics is a new, quickly growing field of neuroscience research that merges neurophysiology, biomechanics and motor control and aims at understanding living systems and their elements through interactions between their neural and mechanical dynamic properties. Although research in Neuromechanics is not limited by computational approaches, neuromechanical modeling is a powerful tool that allows for integration of massive knowledge gained in the past several decades in organization of motion related brain and spinal cord activity, various body sensors and reflex pathways, muscle mechanical and physiological properties and detailed quantitative morphology of musculoskeletal systems. Recent work in neuromechanical modeling has demonstrated advantages of such an integrative approach and led to discoveries of new emergent properties of neuromechanical systems. Neuromechanical Modeling of Posture and Locomotion will cover a wide range of topics from theoretical studies linking the organization of reflex pathways and central pattern generating circuits with morphology and mechanics of the musculoskeletal system (Burkholder; Nichols; Shevtsova et al.) to detailed neuromechanical models of postural and locomotor control (Bunderson; Edwards, Marking et al., Ting). Furthermore, uniquely diverse modeling approaches will be presented in the book including a theoretical dynamic analysis of locomotor phase transitions (Spardy and Rubin), a hybrid computational modeling that allows for in vivo interactions between parts of a living organism and a computer model (Edwards et al.), a physical neuromechanical model of the human locomotor system (Lewis), and others.

Then, network models of memory, rhythm generation and spatial navigation are presented, followed by abstract and biophysical models of synaptic plasticity.

Over the last two decades, the recognition that astrocytes - the predominant type of cortical glial cells - could sense neighboring neuronal activity and release neuroactive agents, has been instrumental in the uncovering of many roles that these cells could play in brain processing and the storage of information.

This book serves as a step towards a systematic approach to validation of neuro-computational models used for studying brain diseases and should be useful for all neuro-computational modellers.

Since information in the brain is processed by the exchange of spikes among neurons, a study of such group dynamics is extremely important in understanding hippocampus dependent memory.

This book offers an overview of the diversity of mechanisms that dendrites can employ to shape neural computations. It brings together a wide range of studies, with topics ranging from general to system-specific phenomena.

For each article, its author will select an article originally appearing in a CNS conference proceedings from 15 - 20 years ago. The new articles will describe what has been learned about the subject in the following 20 years, and pose specific challenges for the next 20 years.

Neuromechanical Modeling of Posture and Locomotion

Increasing interest in the study of coordinated activity of brain cell ensembles reflects the current conceptualization of brain information processing and cognition.

This book serves as a step towards a systematic approach to validation of neuro-computational models used for studying brain diseases and should be useful for all neuro-computational modellers.

As a result, a new and pressing challenge for addiction research is to formulate a synthetic theoretical framework that goes well beyond mere scientific eclectism to deepen our understanding of drug addiction and to foster our capacity to prevent and to cure drug addiction.

In addition, it considers different experimental techniques on various spatial scales, such as fMRI and EEG-experiments on the macroscopic scale and single neuron and LFP-measurements on the microscopic scale. In total all book chapters reveal aspects of the neural correlates of sleep and anesthesia motivated by experimental data.

Solid and transparent data analysis is the most important basis for reliable interpretation of experiments. The focus of this book is on concepts and methods of correlation analysis (synchrony, patterns, rate covariance), combined with a solid introduction into approaches for single spike trains, which represent the basis of correlations analysis.

Coherent Behavior in Neuronal Networks features a review of recent research in this area from some of the world's foremost experts on systems neuroscience. It contains cutting-edge mathematical, statistical, and computational techniques.

This book offers an overview of the diversity of mechanisms that dendrites can employ to shape neural computations. It brings together a wide range of studies, with topics ranging from general to system-specific phenomena.

For each article, its author will select an article originally appearing in a CNS conference proceedings from 15 - 20 years ago. The new articles will describe what has been learned about the subject in the following 20 years, and pose specific challenges for the next 20 years.

As a result, a new and pressing challenge for addiction research is to formulate a synthetic theoretical framework that goes well beyond mere scientific eclectism to deepen our understanding of drug addiction and to foster our capacity to prevent and to cure drug addiction.

This book will track advances in the application of phase response (PR) analysis to the study of electrically excitable cells, focusing on applications of PR analysis in the computational neurosciences.

The dynamic-clamp (also called "conductance injection") allows experimentalists and theoreticians to challenge neurons (or any other type of cell) with complex conductance stimuli generated by a computer.The technique can be implemented from neural simulation environments and a variety of custom-made or commercial systems.

In addition, it considers different experimental techniques on various spatial scales, such as fMRI and EEG-experiments on the macroscopic scale and single neuron and LFP-measurements on the microscopic scale. In total all book chapters reveal aspects of the neural correlates of sleep and anesthesia motivated by experimental data.

Increasing interest in the study of coordinated activity of brain cell ensembles reflects the current conceptualization of brain information processing and cognition.

Solid and transparent data analysis is the most important basis for reliable interpretation of experiments. The focus of this book is on concepts and methods of correlation analysis (synchrony, patterns, rate covariance), combined with a solid introduction into approaches for single spike trains, which represent the basis of correlations analysis.