Neural codes and distributed representations : foundations of neural computation /

Neural codes and distributed representations : foundations of neural computation /

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Bibliographic Details
Imprint:Cambridge, Mass. : MIT Press, c1999.
Description:xxiii, 345 p. : ill. ; 23 cm.
Language:English
Series:Computational neuroscience
Subject:
Neural networks (Neurobiology)
Coding theory.
Nerve Net.
Coding theory.
Neural networks (Neurobiology)
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/3966358
Hidden Bibliographic Details
Other authors / contributors:Abbott, L. F.
Sejnowski, Terrence J. (Terrence Joseph)
ISBN:0262511002 (pbk. : alk. paper)
Notes:"A Bradford book."
Includes bibliographical references and index.
Also available on the internet.
Table of Contents:
  • Introduction
  • Neural Coding
  • Neuronal Response Variability
  • The Nature of the Neural Code
  • Population Coding
  • Temporal Sequences
  • Sources
  • References
  • Deciphering the Brain's Codes
  • 1. Introduction
  • 2. Behavioral Analysis
  • 3. Successive Stages of Signal Processing
  • 3.1. The Top-Down Approach in the Owl.
  • 3.2. The Bottom-Up Approach in the Electric Fish.
  • 4. The Output Neurons
  • 5. Stimulus Selectivities and Neural Codes
  • 6. Similarities in Algorithms
  • 7. Concluding Remarks
  • Acknowledgments
  • References
  • A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors
  • 1. Introduction
  • 2. Model and Learning Procedure
  • 3. Results of Simulations
  • 4. Discussion
  • Acknowledgments
  • References
  • Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike...
  • 1. Introduction
  • 2. Spike Signal and Feature Extraction
  • 3. The Computation Model
  • 4. Trajectory Computation from Motor Cortical Discharge Rates
  • 4.1. Using Data from Spiral Tasks to Train the SOFM
  • 4.2. Using Data from Spiral and Center Out Tasks to Train the SOFM
  • 4.3. Average Testing Result Using the Leave-k-Out Method
  • 4.4. Trajectory Computation by the Population Vector Algorithm
  • 5. Discussion
  • Acknowledgments
  • References
  • Theoretical Considerations for the Analysis of Population Coding in Motor Cortex
  • 1. Introduction
  • 2. Single Unit Tuning Curves
  • 3. Population Vectors
  • 4. Coordinate-Free Representations
  • 5. Conclusion
  • Acknowledgments
  • References
  • Statistically Efficient Estimation Using Population Coding
  • 1. Introduction
  • 2. Model of Neuronal Responses
  • 3. Classical Decoding Methods
  • 3.1. Maximum Likelihood (ML)
  • 3.2. Optimum Linear Estimator (OLE)
  • 3.3. Center of Mass (COM)
  • 3.4. Complex Estimator (COMP)
  • 4. Recurrent Networks
  • 4.1. Linear Network
  • 4.2. Nonlinear Network
  • 5. Simulation Results
  • 6. Analysis
  • 6.1. Notation
  • 6.2. Linearization
  • 6.3. Characterizing the Transformation
  • 6.4. Properties of the Network Estimate
  • 6.5. Nonoptimal Cases
  • 6.5.1. Nonequal Variance
  • 6.5.2. Correlations
  • 6.5.3. Large Noise
  • 6.5.4. Nongaussian Distributions
  • 6.5.5. Different Input and Output Functions
  • 6.6. Relation to Linear ML Estimator
  • 7. Discussion
  • Acknowledgments
  • References
  • Parameter Extraction from Population Codes: A Critical Assessment
  • 1. Introduction
  • 2. Efficiency of CG Estimation Is Low for Sharply Tuned Sensors Perturbed by Background Noise
  • 3. Efficiency of CG Estimation Is High for Poisson Noise or Broadly Tuned Sensors
  • 3.1. Poisson Noise
  • 3.2. Broadly Tuned Sensors
  • 4. Sensor Position Irregularities: Another Noise Source for Center-of-Gravity Estimation
  • 5. System Nonlinearities: Consequences for the CG Estimate
  • 6. Conclusions
  • Appendix: Proof of Equation 4.5
  • Acknowledgments
  • References
  • Energy Efficient Neural Codes
  • 1. Introduction
  • 2. Case 1: Binary Neurons
  • 2.1. Representational Capacity.
  • 2.2. Energy Expenditure.
  • 2.3. Maximizing
  • 3. Case 2: Analog Neurons
  • 3.1. Representational Capacity.
  • 3.2. Energy Expenditure.
  • 3.3. Maximizing
  • Summary
  • Appendix
  • Acknowledgments
  • References
  • Seeing Beyond the Nyquist Limit
  • 1. Introduction
  • 2. The Receptor Array
  • 3. Stimulus Reconstruction
  • 4. Examples
  • 5. What's going on?
  • 6. Is Phase Preserved in Super-Nyquist Frequencies?
  • 7. Conclusions
  • Acknowledgments
  • References
  • A Model of Spatial Map Formation in the Hippocampus of the Rat
  • 1. Mathematical Results
  • Acknowledgments
  • References
  • Probabilistic Interpretation of Population Codes
  • 1. Introduction
  • 2. Population Code Interpretations
  • 2.1. The Encoding-Decoding Framework.
  • 2.2. The Poisson Model.
  • 2.3. The KDE Model.
  • 3. The Extended Poisson Model
  • 4. Comparing the Models
  • 4.1. Uncertainty in Target Location.
  • 4.2. Multiple Locations.
  • 4.3. Uncertainty in Object Presence.
  • 4.4. Noise Robustness.
  • 5. Discussion
  • Acknowledgments
  • References
  • Cortical Cells Should Fire Regularly, But Do Not
  • Acknowledgments
  • References
  • Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky ...
  • 1. Introduction
  • 2. Partial Reset and the Control of the Firing Irregularity
  • 3. Equivalence Between Partial Reset and Time-Varying Threshold
  • 4. Determinants of the Firing Time
  • 5. What Do Reverse Correlation Graphs Tell Us?
  • 6. Proving Coincidence Detection
  • 7. Temporally Clustered Firing and Neuronal Gain
  • 8. Summary
  • Appendix A. Equivalence Between a Model with Partial Reset and a Model with Time-Dependent Threshold...
  • Appendix B. Decay Time Constant for Fluctuations
  • Acknowledgments
  • References
  • Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking ...
  • 1. Introduction
  • 2. The High-Gain Model
  • 3. Simulation Results
  • 4. An Intuitive Picture
  • 5. Discussion
  • Acknowledgments
  • References
  • Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Thresho...
  • 1. Introduction
  • 2. Linear Estimation of Time-Varying Signals from Neuronal Spike Trains
  • 3. A Simplified Model of Motion Encoding in H1 Neurons
  • 4. Results
  • 5. Discussion
  • Acknowledgments
  • References
  • Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey
  • 1. Introduction
  • 2. Methods
  • 2.1. Experimental Procedures.
  • 2.2. Data Analysis.
  • 3. Results
  • 3.1. Precision and Reliability.
  • 3.2. Frequency Profile.
  • 3.3. Response to Coherent Motion.
  • 4. Discussion
  • Acknowledgments
  • References
  • Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors
  • 1. Introduction
  • 1.1. Temporal to Spatial Correlations in Monkey Cortex.
  • 1.2. Modeling Correlation Conversion.
  • 2. The Model with ±1 Neurons
  • 3. ANN with Discrete 01 Neurons
  • 4. Learning
  • 5. Experimental Predictions and Some Speculations
  • Acknowledgments
  • References
  • Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses...
  • 1. Introduction
  • 2. Structure of the Model
  • 2.1. Cerebellar Circuitry.
  • 2.2. Classic Cerebellar Theories.
  • 2.3. Hypothesis.
  • 2.4. Neural Network.
  • 3. Simulations
  • 3.1. Timing.
  • 3.2. Ability to Store Multiple Intervals.
  • 3.3. Sensitivity to Noise.
  • 3.4. Effects of the MF Go Connection on Timing and Sensitivity to Noise.
  • 4. Discussion
  • Acknowledgments
  • References
  • Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency
  • 1. Introduction
  • 2. Methods
  • 3. Results
  • 4. Discussion
  • Acknowledgments
  • References
  • Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model
  • 1. Introduction
  • 2. Methods
  • 3. Results
  • 3.1. Steady-State Activity.
  • 3.2. Time-Varying Inputs.
  • 3.3. Cross-Correlations.
  • 4. Discussion
  • Acknowledgments
  • References
  • NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron
  • 1. Introduction
  • 2. The Biophysical Model
  • 3. A Basis for Nonlinear Pattern Discrimination
  • 4. Conclusions
  • Acknowledgments
  • References
  • The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje C...
  • 1. Introduction
  • 2. Model
  • 3. Results
  • 4. Discussion
  • Acknowledgments
  • References
  • Index
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