Computational Neuroscience: A Comprehensive Approach

How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding. Computational Neuroscience: A Comprehensive Approach provides a unified treatment of the mathematical theory of the nervous system and presents concrete examples demonstrating how computational techniques can illuminate difficult neuroscience problems. In chapters contributed by top researchers, the book introduces the basic mathematical concepts, then examines modeling at all levels, from single-channel and single neuron modeling to neuronal networks and system-level modeling. The emphasis is on models with close ties to experimental observations and data, and the authors review application of the models to systems such as olfactory bulbs, fly vision, and sensorymotor systems. Understanding the nature and limits of the strategies neural systems employ to process and transmit sensory information stands among the most exciting and difficult challenges faced by modern science. This book clearly shows how computational neuroscience has and will continue to help meet that challenge.

Table of Contents:
A THEORETICAL OVERVIEW Introduction Deterministic Dynamical Systems Stochastic Dynamical Systems Information Theory Optimal Control ATOMISTIC SIMULATIONS OF ION CHANNELS Introduction Simulation Methods Selected Applications Outlook MODELING NEURONAL CALCIUM DYNAMICS Introduction Basic Principles Special Calcium Signaling for Neurons Conclusions STRUCTURE BASED MODELS OF NO DIFFUSION IN THE NERVOUS SYSTEM Introduction Methods Results Exploring Functional Roles with More Abstract Models Conclusions STOCHASTIC MODELING OF SINGLE ION CHANNELS Introduction Some Basic Probability Single Channel Models Transition Probabilities, Macroscopic Currents and Noise Macroscopic Currents and Noise Behaviour of Single Channels under Equilibrium Conditions Time Interval Omission Some Miscellaneous Topics THE BIOPHYSICAL BASIS OF FIRING VARIABILITY IN CORTICAL NEURONS Introduction Typical Input is Correlated and Irregular Synaptic Unreliability Postsynaptic Ion Channel Noise Integration of a Transient Input by Cortical Neurons Noisy Spike Generation Dynamics Dynamics of NMDA Receptors Class 1 and Class 2 Neurons Show Different Noise Sensitivities Cortical Cell Dynamical Classes Implications for Synchronous Firing Conclusions Generating Models of Single Neurons Introduction The Hypothalamo-Hypophysial System Statistical Methods to Investigate The Intrinsic Mechanisms Underlying Spike Patterning Summary and Conclusions GENERATING QUANTITATIVELY ACCURATE, BUT COMPUTATIONALLY CONCISE, MODELS OF SINGLE NEURONS Introduction The Hypothalamo-hypophysial System Statistical Methods to Investigate the Intrinsic Mechanisms Underlying Spike Patterning Summary and Conclusions BURSTING ACTIVITY IN WEAKLY ELECTRIC FISH Introduction Overview of the Electrosensory System Feature Extraction by Spike Bursts Factors Shaping Burst Firing In Vivo Conditional Action Potential Back Propagation Controls Burst Firing In Vitro Comparison with Other Bursting Neurons Conclusions LIKELIHOOD METHODS FOR NEURAL SPIKE TRAIN DATA ANALYSIS Introduction Theory Applications Conclusion Appendix BIOLOGICALLY-DETAILED NETWORK MODELING Introduction Cells Synapses Connections Inputs Implementation Validation Conclusions HEBBIAN LEARNING AND SPIKE-TIMING-DEPENDENT PLASTICITY Hebbian Models of Plasticity Spike-Timing Dependent Plasticity Role of Constraints in Hebbian Learning Competitive Hebbian Learning Through STDP Temporal Aspects of STDP STDP in a Network Conclusion CORRELATED NEURONAL ACTIVITY: HIGH-AND LOW-LEVEL VIEWS Introduction: the Timing Game Functional Roles for Spike Timing Correlations Arising from Common input Correlations Arising from Local Network Interactions When Are Neurons Sensitive to Correlated Input? A Simple, Quantitative Model Correlations and Neuronal Variability Conclusion Appendix A CASE STUDY OF POPULATION CODING: STIMULUS LOCALIZATION IN THE BARREL CORTEX Introduction Series Expansion Method The Whisker System Coding in the Whisker System Discussion Conclusions MODELING FLY MOTION VISION The Fly Motion Vision System: An Overview Mechanisms of Local Motion Detection: The Correlation Detector Spatial Processing of Local Motion Signals BY Lobula Plate Tangential Cells Conclusions MEAN-FIELD THEORY OF IRREGULARLY SPIKING NEURONAL POPULATIONS AND WORKING MEMORY IN RECURRENT CORTICAL NETWORKS Introduction Firing-Rate and Variability of a Spiking Neuron with Noisy input Self-Consistent Theory of Recurrent Cortical Circuits THE OPERATION OF MEMORY SYSTEMS IN THE BRAIN Introduction Functions of the Hippocampus in Long-Term Memory Short Term Memory Systems Invariant Visual Object Recognition Visual Stimulus-Reward Association, Emotion, and Motivation Effects of Mood on Memory and Visual Processing MODELING MOTOR CONTROL PARADIGMS Introduction: The Ecological Nature of Motor Control The Robotic Perspective The Biological Perspective The Role of Cerebellum in the Coordination of Multiple Joints Controlling Unstable Plants Motor Learning Paradigms COMPUTATIONAL MODELS FOR GENERIC CORTICAL MICROCIRCUITS Introduction A Conceptual Framework for Real-Time Neural Computation The Generic Neural Microcircuit Model Towards a Non-Turing theory for Real-Time Neural Computation A Generic Neural Microcircuit on the Computational Test Stand Temporal integration and Kernel Function of Neural Microcircuit Models Software for Evaluating the Computational Capabilities of Neural Microcircuit Models Discussion MODELING PRIMATE VISUAL ATTENTION Introduction Brain Areas Bottom-Up Control Top-Down Modulation of Early Vision Top-Down Deployment of Attention Attention and Scene Understanding Discussion