Construction Of A Concept Of Neuronal Modeling

The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.

The business problem of having inefficient processes, imprecise process analyses and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS) and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.

This book is a beginning graduate-level introduction to neural networks which is divided into four parts.

An overview of the techniques used in modern neuroscience research with the emphasis on showing how different techniques can optimally be combined in the study of problems that arise at some levels of nervous system organization. This is essentially a working tool for the scientist in the laboratory and clinic, providing detailed step-by-step protocols with tips and recommendations. Most chapters and protocols are organized such that they can be used independently, while cross-references between the chapters, a glossary, a list of suppliers and appendices provide further help.

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.

Methods in Neuronal Modeling is the first technical handbook on computational neuroscience. Written for researchers and theoreticians alike, it outlines methods and techniques used for simulating on digital computers the functional properties of single neurons from synapses, dendrites, single cells; and small invertebrate networks to large scale neural networks in the mammalian nervous system. The use of new experimental tools such as selective staining methods, membrane patch electrodes, voltage and calcium-dependent dyes, and multielectrode recordings, together with the, advent of universally available powerful computing, makes it possible to construct detailed and realistic models of neuronal systems. Methods in Neuronal Modeling addresses such questions as what can and should be simulated and what techniques should be used; what experimental parameters are crucial for such simulations, and whether these models may be verified experimentally. Chapters cover simulation of passive dendritic trees, compartmental models of single cells including neurons with a number of different ionic channels, calcium current dynamics, simulations of small invertebrate networks, simulations of the mammalian cortex, connectionists' models, and the use of parallel computers in modeling neural networks. Although the chapters were written by several authors, they are uniform in structure and notation. Detailed examples are given to clarify the different approaches. Each chapter concludes with a description of the model discussed and the details of its implementation on the computer. Christof Koch is an Assistant Professor of Computation and Neural Systems at the California Institute of Technology. Idan Segev is a Lecturer in Neurobiology at the Institute of Life Science, Hebrew University of Jerusalem. Methods in Neuronal Modeling inaugurates the new series in Computational Neuroscience, edited by Terrence J. Sejnowski and Tomaso Poggio. A Bradford Book.

Neural field theory has a long-standing tradition in the mathematical and computational neurosciences. Beginning almost 50 years ago with seminal work by Griffiths and culminating in the 1970ties with the models of Wilson and Cowan, Nunez and Amari, this important research area experienced a renaissance during the 1990ties by the groups of Ermentrout, Robinson, Bressloff, Wright and Haken. Since then, much progress has been made in both, the development of mathematical and numerical techniques and in physiological refinement und understanding. In contrast to large-scale neural network models described by huge connectivity matrices that are computationally expensive in numerical simulations, neural field models described by connectivity kernels allow for analytical treatment by means of methods from functional analysis. Thus, a number of rigorous results on the existence of bump and wave solutions or on inverse kernel construction problems are nowadays available. Moreover, neural fields provide an important interface for the coupling of neural activity to experimentally observable data, such as the electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI). And finally, neural fields over rather abstract feature spaces, also called dynamic fields, found successful applications in the cognitive sciences and in robotics. Up to now, research results in neural field theory have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. There is no comprehensive collection of results or reviews available yet. With our proposed book Neural Field Theory, we aim at filling this gap in the market. We received consent from some of the leading scientists in the field, who are willing to write contributions for the book, among them are two of the founding-fathers of neural field theory: Shun-ichi Amari and Jack Cowan.

This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.