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Theoretical analysis and computational modeling are important tools for characterizing what nervous systems do, determining how they function, and understanding why they operate in particular ways. Neuroscience encompasses approaches ranging from molecular and cellular studies to human psychophysics and psychology. Theoretical neuroscience encourages cross-talk among these sub-disciplines by constructing compact representations of what has been learned, building bridges between different levels of description, and identifying unifying concepts and principles. In this book, we present the basic methods used for these purposes and discuss examples in which theoretical approaches have yielded insight into nervous system function | THEORETICAL NEUROSCIENCE THEORETICAL NEUROSCIENCE Peter Dayan and L.F. Abbott Preface PART I - ANALYZING AND MODELING NEURAL RESPONSES Chapter 1 - Neural Encoding I Firing Rates and Spike Statistics Introduction Properties of Neurons Recording Neuronal Responses From Stimulus to Response Spike Trains and Firing Rates Measuring Firing Rates Tuning Curves Spike-Count Variability What Makes a Neuron Fire Describing the Stimulus The Spike-Triggered Average White-Noise Stimuli Multiple-Spike-Triggered Averages and Spike-Triggered Correlations Spike Train Statistics The Homogeneous Poisson Process The Spike-Train Autocorrelation Function The Inhomogeneous Poisson Process The Poisson Spike Generator Comparison with Data The Neural Code Independent-Spike Independent Neuron and Correlation Codes Temporal Codes Chapter Summary Appendices A The Power Spectrum of White Noise B Moments of the Poisson Distribution D Inhomogeneous Poisson Statistics Annotated Bibliography Chapter 2 - Neural Encoding II Reverse Correlation and Receptive Fields Introduction Estimating Firing Rates The Most Effective Stimulus Static Nonlinearities Introduction to the Early Visual System file E Media_folder Books books.pdox.net Physics Theoretical_Neuroscience TOC.htm 1 of 7 15-02-2002 0 32 12 THEORETICAL NEUROSCIENCE The Retinotopic Map Visual Stimuli The Nyquist Frequency Reverse Correlation Methods - Simple Cells Spatial Receptive Fields Temporal Receptive Fields Response of a Simple Cell to a Counterphase Grating Space-Time Receptive Fields Nonseparable Receptive Fields Static Nonlinearities - Simple Cells Static Nonlinearities - Complex Cells Receptive Fields in the Retina and LGN Constructing V1 Receptive Fields Chapter Summary Appendices A The Optimal Kernel B The Most Effective Stimulus C Bussgang s Theorem Annotated Bibliography Chapter 3 - Neural Decoding Encoding and Decoding Discrimination ROC Curves ROC Analysis of Motion Discrimination The Likelihood Ratio Test Population Decoding .
