Last update: 10/22/2007

Auditory System Characterization

Hiroki Asari

A dissertation presented to the Watson School of Biological Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Biological Sciences at Cold Spring Harbor Laboratory (July 2007).

Contents

  • Preface
  • Acknowledgements
  • List of Figures
  • List of Tables
  • 1 Introduction (140 KB, PDF)
      • 1.1 Auditory System Organization
        • 1.1.1 Hair Cells and Auditory Nerve Fibers
        • 1.1.2 Cochlear Nucleus
        • 1.1.3 Superior Olivary Nucleus
        • 1.1.4 Inferior Colliculus
        • 1.1.5 Medial Geniculate Nucleus
        • 1.1.6 Primary Auditory Cortex
      • 1.2 Characteristics of Auditory Signal Processing
  • 2 Top-down characterization: population level analysis (730 KB, PDF)
      • 2.1 Sparse Overcomplete Representations
        • 2.1.1 Blind Source Separation: Overview
        • 2.1.2 Head-Related Transfer Functions
      • 2.2 Source Separation Model: Problem Formulation
        • 2.2.1 Dictionary Method Approach
        • 2.2.2 HRTF-Based Approach
        • 2.2.3 Neural Representation for Source Separation
        • 2.2.4 Dictionary Learning
      • 2.3 Monaural Separation: Results
      • 2.4 Predictions on Neural Behaviors
        • 2.4.1 Optimal Feature Estimation
        • 2.4.2 Asymmetry of sparse representations
        • 2.4.3 Context-Dependence of Receptive Field
        • 2.4.4 Top-Down Receptive Field Modulation
        • 2.4.5 Sparse Activities
      • 2.5 Discussion
        • 2.5.1 Model Implications
        • 2.5.2 Model Extensions
      • 2.A Appendix: Feature Estimation
        • 2.A.1 Learning Algorithm
        • 2.A.2 Simulation Procedures
        • 2.A.3 Results
        • 2.A.4 Discussion
  • 3 Bottom-up characterization: single-cell level analysis (1223 KB, PDF)
      • 3.1 Introduction
        • 3.1.1 Sensory Coding Models
        • 3.1.2 Motivation and Background Studies
      • 3.2 Experimental Methods
        • 3.2.1 Surgery
        • 3.2.2 Whole-Cell Patch-Clamp Recordings
        • 3.2.3 Stimulus Design
        • 3.2.4 Stimuli
      • 3.3 Data Analysis Methods
        • 3.3.1 Context-Dependence
        • 3.3.2 Exponential Curve Fit
        • 3.3.3 Response Predictability
      • 3.4 Encoding Models
        • 3.4.1 Linear-Nonlinear Cascade Models
        • 3.4.2 Nonlinear Models
        • 3.4.3 Model Performance
      • 3.5 Results
        • 3.5.1 Context-Dependence
        • 3.5.2 Relation to Acoustic Properties
        • 3.5.3 Relation to Response Predictability
        • 3.5.4 Model Performance
      • 3.6 Discussion
        • 3.6.1 Context-Dependence
        • 3.6.2 Response Predictability
        • 3.6.3 Possible Mechanisms
        • 3.6.4 Plausible Encoding Model
  • 4 Coda (61 KB, PDF)
      • 4.1 Conclusions and Future Challenges
      • 4.2 General Discussion
  • A Technical Notes (673 KB, PDF)
      • A.1 Matrix Decomposition
        • A.1.1 Principal Component Analysis
        • A.1.2 Singular Value Decomposition
        • A.1.3 Independent Component Analysis
        • A.1.4 Non-negative Matrix Factorization
        • A.1.5 Characteristics of Each Factorization Method
      • A.2 Linear Regression
        • A.2.1 Regularization Methods
        • A.2.2 Probabilistic Interpretation
        • A.1.3 Independent Component Analysis
        • A.2.3 Dynamic Models
      • A.3 Nonlinear Regression
        • A.3.1 Perceptron
        • A.3.2 Artificial Neural Network
        • A.3.3 Support Vector Regression
      • A.4 Information Theory
        • A.4.1 Entropy
        • A.4.2 Mutual Information
        • A.4.3 File Compression Methods
  • Bibliography (175 KB, PDF)