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November 29, 2004 Preface to The NEURON Book Preface to The NEURON Book N.T. Carnevale1 and M.L. Hines2 Departments of 1Psychology and 2Computer Science Yale University, New Haven, CT ted.carnevale@yale.edu michael.hines@yale.edu Who should read this book This book is about how to use the NEURON simulation environment to construct and apply empirically-based models of neurons and neural networks. It is written primarily for neuroscience investigators, teachers, and students, but 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 emphasis is on the most productive use of NEURON as a means for testing hypotheses that are founded on experimental observations, and for exploring ideas that may lead to the design of new experiments. Therefore the book uses a problem-solving approach, with many working examples that readers can try for themselves. What this book is, and is not, about Formulating a conceptual model is an attempt to capture the essential features that underlie some particular function. This necessarily involves simplification and abstraction of real-world complexities. Even so, one may not necessarily understand all Copyright © 2001-2004 N.T. Carnevale and M.L. Hines, all rights reserved

Preface to The NEURON Book November 29, 2004 implications of the conceptual model. To evaluate a conceptual model it is often necessary to devise a hypothesis or test in which the behavior of the model is compared against a prediction. Computational models are useful for performing such tests. The conceptual model and the hypothesis should determine what is included in a computational model and what is left out. This book is not about how to come up with conceptual models or hypotheses, but instead focuses on how to use NEURON to create and use computational models as a means for evaluating conceptual models. What to read, and why The first chapter conveys a basic idea of NEURON's primary domain of application by guiding the reader through the construction and use of a model neuron. This exercise is based entirely on NEURON's GUI, and requires no programming ability or prior experience with NEURON whatsoever. The second chapter considers the role of computational modeling in neuroscience research from a general perspective. Chapters 3 and 4 focus on aspects of applied mathematics and numerical methods that are particularly relevant to computational neuroscience. Chapter 5 discusses the concepts and strategies that are used in NEURON to simplify the task of representing neurons, which (at least at the level of synapses and cells) are distributed and continuous in space and time, in a digital computer, where neither time nor numeric values are continuous. Chapter 6 returns to the topic of model construction, emphasizing the use of programming. Chapters 7 and 8 provide "inside information" about NEURON's standard run and initialization systems, so that readers can make best use of their features and customize Page 2 Copyright © 2001-2004 N.T. Carnevale and M.L. Hines, all rights reserved

November 29, 2004 Preface to The NEURON Book them to meet special modeling needs. Chapter 9 shows how to use the NMODL programming language to add new biophysical mechanisms to NEURON. This theme continues in Chapter 10, which starts with mechanisms of communication between cells (gap junctions, graded and spike-triggered synaptic transmission), and moves on to models of artificial spiking neurons (e.g. integrate and fire cells). The first half of Chapter 11 is a tutorial on NEURON's GUI tools for creating simple network models, and the second half shows how to use the strengths of the GUI and hoc programming to create more complex networks. Chapter 12 discusses the elementary features of the hoc programming language itself. Chapter 13 describes the object-oriented extensions that have been added to hoc. These extensions have greatly facilitated construction of NEURON's GUI tools, and they can also be very helpful in many other complex programming tasks such as creating and managing network models. Chapter 14 presents an example of how to use object oriented programming to increase the functionality of NEURON. Appendix 1 presents a mathematical analysis of the IntFire4 artificial spiking cell mechanism, proving a result that is used to achieve computational efficiency when simulating this model. Appendix 2 summarizes the commands for NEURON's built-in text editor. Acknowledgments First and foremost, we want to thank our mentor and colleague John W. Moore for his vision, support, encouragement, and active participation in the development of NEURON, without which neither it nor this book would exist. Through his research and Copyright © 2001-2004 N.T. Carnevale and M.L. Hines, all rights reserved Page 3

Preface to The NEURON Book November 29, 2004 teaching, he was introducing students to "computational neuroscience" long before that glorious term was invented. NEURON had its beginnings in John's laboratory at Duke University almost three decades ago, when he and one of the authors (MLH) started their collaboration to develop simulation software for neuroscience research. Users of NEURON on the Macintosh owe John a particular debt. He continues to participate in the development and dissemination of NEURON, concentrating most recently on educational applications in collaboration with Ann Stuart (Moore and Stuart 2004). The list of those who have added in one way or another to the development of NEURON is far too long for this short preface. Zach Mainen, Alain Destexhe, Bill Lytton, Terry Sejnowski, and Gordon Shepherd deserve special mention for many contributions, both direct and indirect, that range from specific enhancements to the program, to fostering the wider acceptance of computational approaches in general, and NEURON in particular, by the neuroscience community at large. We also thank the countless NEURON users whose questions and suggestions continue to help guide the evolution of this software and its documentation. We hope that everyone else will forgive any omission and remind us, gently, in time for the second edition. Finally, we thank our wives and children for their encouragement and patience while we completed this book. References Moore, J.W. and Stuart, A.E. Neurons in Action: Computer Simulations with NeuroLab. Sunderland, MA: Sinauer Associates, 2004. Page 4 Copyright © 2001-2004 N.T. Carnevale and M.L. Hines, all rights reserved

November 29, 2004 Contents of The NEURON Book The NEURON Book Table of contents Note: page numbers in each chapter start from 1, and correspond to double-spaced format. Preface Chapter 1. A tour of the NEURON simulation environment Modeling and understanding Introducing NEURON 1. State the question 2. Formulate a conceptual model 3. Implement the model in NEURON Starting and stopping NEURON Bringing up a CellBuilder Enter the specifications of the model cell Topology Subsets Geometry Biophysics Copyright © 2001-2004 N.T. Carnevale and M.L. Hines, all rights reserved 1 2 3 4 7 8 10 11 11 15 16 20

Contents of The NEURON Book November 29, 2004 Save the model cell Execute the model specification 4. Instrument the model Signal sources Signal monitors 5. Set up controls for running the simulation 6. Save model with instrumentation and run control 7. Run the simulation experiment 8. Analyze results References Index Chapter 2. Principles of neural modeling Why model? From physical system to computational model Conceptual model: a simplified representation of a physical system Computational model: an accurate representation of a conceptual model An example Index 22 24 25 25 27 30 31 33 37 40 42 1 2 2 3 4 6 Page 2 Copyright © 2001-2004 N.T. Carnevale and M.L. Hines, all rights reserved

November 29, 2004 Contents of The NEURON Book Chapter 3. Expressing conceptual models in mathematical terms Chemical reactions Flux and conservation in kinetic schemes Stoichiometry, flux, and mole equivalents Compartment size Scale factors Electrical circuits Cables References Index Chapter 4. Essentials of numerical methods for neural modeling Spatial and temporal error in discretized cable equations Analytic solutions: continuous in time and space Spatial discretization Adding temporal discretization 2 3 5 7 11 13 14 28 29 2 3 6 9 Copyright © 2001-2004 N.T. Carnevale and M.L. Hines, all rights reserved Page 3

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