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Theoretical Neuroscience
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Neural Codes and Distributed Representations
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. The present volume focuses on neural codes and representations, topics of broad interest to neuroscientists and modelers. The topics addressed are: how neurons encode information through action potential firing patterns, how populations of neurons represent information, and how individual neurons use dendritic processing and biophysical properties of synapses to decode spike trains. The papers encompass a wide range of levels of investigation, from dendrites and neurons to networks and systems.
An Introductory Course in Computational Neuroscience
A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and...
23 Problems in Systems Neuroscience
The complexity of the brain and the protean nature of behavior remain the most elusive area of science, but also the most important. van Hemmen and Sejnowski invited 23 experts from the many areas--from evolution to qualia--of systems neuroscience to formulate one problem each. Although each chapter was written independently and can be read separately, together they provide a useful roadmap to the field of systems neuroscience and will serve as a source of inspirations for future explorers of the brain.
Methods and Models in Neurophysics
1. E. Marder, Experimenting with theory -- 2. A. Borysuk and J. Rinzel, Understanding neuronal dynamics by geometrical dissection of minimal models -- 3. D. Terman, Geometry singular perturbation analysis of neuronal dynamics -- 4. G. Mato, Theory of neural synchrony -- 5. M. Shelley, Some useful numerical techniques for simulating integrate-and-fire networks -- 6. D. Golomb, Propagation of pulses in cortical networks: the single-spike approximation -- 7. M. Tsodyks, Activity-dependent transmission in neocortical synapses -- 8. H. Sompolinsky and J. White, Theory of large recurrent networks: from spikes to behavior -- 9. C. van Vreeswijk, Irregular activity in large networks of neurons -- 10. N. Brunel, Network models of memory -- 11. P. Bressloff, Pattern formation in visual cortex -- 12. F. Wolf, Symmetry breaking and pattern selection in visual cortical development -- 13. A. Treves and Y. Roudi, On the evolution of the brain -- 14. E. Brown, Theory of point processes for neural syst ...
Models of the Mind
Explores the value of describing the machinery of neuroscience using the elegant language of mathematics and reveals in full the remarkable fruits of this endeavour.
Drawing the Boundaries of Meaning
One of the most lively and contentious issues in contemporary linguistic theory concerns the elusive boundary between semantics and pragmatics, and Professor Laurence R. Horn of Yale University has been at the center of that debate ever since his groundbreaking 1972 UCLA dissertation. This festvolume in honor of Horn brings together the best of current work at the semantics/pragmatics boundary from a neo-Gricean perspective. Featuring the contributions of 22 leading researchers, it includes papers on implicature (Kent Bach), inference (Betty Birner), presupposition (Barbara Abbott), lexical semantics (Georgia Green, Sally McConnell-Ginet, Steve Kleinedler & Randall Eggert), negation (Pauline Jacobson, Frederick Newmeyer, Scott Schwenter), polarity (Donka Farkas, Anastasia Giannakidou, Michael Israel), implicit variables (Greg Carlson & Gianluca Storto), definiteness (Barbara Partee), reference (Ellen Prince, Andrew Kehler & Gregory Ward), and logic (Jerrold Sadock, Francis Jeffry Pelletier & Andrew Hartline). These original papers represent not only a fitting homage to Larry Horn, but also an important contribution to semantic and pragmatic theory.
Methods in Neuronal Modeling
Kinetic Models of Synaptic Transmission / Alain Destexhe, Zachary F. Mainen, Terrence J. Sejnowski / - Cable Theory for Dendritic Neurons / Wilfrid Rall, Hagai Agmon-Snir / - Compartmental Models of Complex Neurons / Idan Segev, Robert E. Burke / - Multiple Channels and Calcium Dynamics / Walter M. Yamada, Christof Koch, Paul R. Adams / - Modeling Active Dendritic Processes in Pyramidal Neurons / Zachary F. Mainen, Terrence J. Sejnowski / - Calcium Dynamics in Large Neuronal Models / Erik De Schutter, Paul Smolen / - Analysis of Neural Excitability and Oscillations / John Rinzel, Bard Ermentrout / - Design and Fabrication of Analog VLSI Neurons / Rodney Douglas, Misha Mahowald / - Principles of Spike Train Analysis / Fabrizio Gabbiani, Christof Koch / - Modeling Small Networks / Larry Abbott, Eve Marder / - Spatial and Temporal Processing in Central Auditory Networks / Shihab Shamma / - Simulating Large Networks of Neurons / Alexander D. Protopapas, Michael Vanier, James M. Bower / ...
Theoretical Neuroscience
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Fundamentals of Computational Neuroscience
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.
