文档介绍：THEORETICAL NEUROSCIENCE
THEORETICAL NEUROSCIENCE
Peter Dayan and . 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
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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
Encodi