Arising from several courses taught by the authors, this book provides a needed overview illustrating how dynamical systems and computational analysis have been used in understanding the types of models that come out of neuroscience.

Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory. Fully revised material and corrected text Additional chapters on extracellular potentials, motion detection and neurovascular coupling Revised selection of exercises with solutions More than 200 Matlab scripts reproducing the figures as well as a selection of equivalent Python scripts

During his long and continuing scholarly career, Patrick Suppes contributed significantly both to the sciences and to their philosophies. The volume consists of papers by an international group of Suppes colleagues, collaborators, and students in many of the areas of his expertise, building on or adding to his insights. Michael Friedman offers an overview of Suppes accomplishments and of his unique perspective on the relation between science and philosophy. Paul Humphreys, Stephen Hartmann, and Tom Ryckman present essays in the philosophy of physics. Jens-Erik Fenstad, Harvey Friedman, and Jaako Hintikka consider problems in the foundations of mathematics, while the late Duncan Luce, Jean-Claude Falmagne, Brian Skyrms, and Hannes Leitgeb have contributed essays in theory of measurement, decision theory and probability. Foundations of economics and political theory are addressed by Adolfo Garcia de la Sienra, Russell Hardin, and Kenneth Arrow. Psychology, language, and philosophy of language are addressed by Elizabeth Loftus, Anne Fagot-Largeault, Willem Levelt, Dagfinn Follesdal, and Marcos Perreau-Guimares and some of Suppes most recent research in neurobiology is addressed in essays by Colleen Crangle, Acadio de Barros and Claudio Carvalhes. Finally Nancy Cartwright and Alexandre Marcelles consider the alignment (or misalignment) of method and policy. Each of the essays is accompanied by a response from Suppes."

The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain.

Writing from a scientifically and philosophically informed perspective, the authors provide a critical overview of the conceptual difficulties encountered in many current neuroscientific and psychological theories.

This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.