Neural Codes and Distributed Representations
Laurence Abbott, Terrence J. Sejnowski, 1999

This book presents a number of attempts to combine neuron and network modeling with observations of the behavior of in vivo nervous systems to aid our understanding of the nature of large populations of neurons.

A central issue is neural coding. How is information encoded in the timing of neural spikes? What is the nature of neural response variability? What are the probability distribution models and how is information coded? Do neurons employ temporal or rate encoding? What is the nature of the neural code? How do large populations of neurons cooperate to exhibit population coding? Can temporal sequences of neural firing be interpreted as attractors?

Chapter 1. Deciphering the Brain's Codes
Masakazu Konishi

Chapter 2. A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors
Alexander V. Lukashin and Apostolos P. Georgopoulos

Chapter 3. Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike Signals
Siming Lin, Jennie Si, and A. B. Schwartz

Chapter 4. Theoretical Considerations for the ANalysis of Population Coding in Motor Cortex
Terence D. Sanger

Chapter 5. Statistically Efficient Estimation Using Population Coding
Alexander Pouget, Kechen Zhang, Sophie Deneve, and Peter E. Latham

Chapter 6. Parameter Extraction from Population Codes: A Critical Assessment
Herman P. Snippe

Chapter 7. Energy Efficient Neural Codes
William B. Levy and Robert A. Baxter

Chapter 8. Seeing Beyond the Nyquist Limit
Daniel L. Ruderman and William Bialek

Chapter 9. A Model of Spatial Map Formation in the Hippocampus of the Rat
Kenneth I. Blum and L. F. Abbott

Chapter 10. Probabalistic Interpretation of Population Codes
Richard S. Zemel, Peter Dayan, and Alexander Pouget

Chapter 11. Cortical Cells Should Fire Regularly, But Do Not
William R. Softky and Christof Koch

Chapter 12. Role of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reset
Guido Bugmann, Chris Christodoulou, and John G. Taylor

Chapter 13. Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell
Todd W. Troyer and Kenneth D. Miller

Chapter 14. Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Threshold
Fabrizio Gabbiani and Christof Koch

Chapter 15. Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey
Wyeth Bair and Christof Koch

Chapter 16. Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors
M. Griniasti, M. V. Tsodyks, and Daniel J. Amit

Chapter 17. Neural Network Model of the Cerebellum:Temporal Discrimination and the TIming of Motor Responses
Dean V. Buonomano and Michael D. Mauk

Chapter 18. Gamma Oscillation Model Predicts Intensity Coding by Phase Rather than Frequency
Roger D. Traub, Miles A. Whittington, and John G. R. Jefferys

Chapter 19. Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model
Venkatesh N. Murthy and Eberhard E. Fetz

Chapter 20. NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron
Bartlett W. Mel

Chapter 21. The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells
Moshe Rapp, Yosef Yarom, and Idan Segev

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