Introduction. 1. Deciphering the Brain's codes (Masakazu Konish). 2. A neural network for coding of trajectories by time series of neuronal population vectors (Alexander V. Lukashin and Apostolos P. Georgopolos). 3. Selforganization of firing activities in monkey's motor coretx: Trajectory computation from spike signals (Siming Lin, Jennie Si and A.B. Schwartz). 4. Theoritical considerations for the analysis of population coding in motor cortex (Terence D. Sanger). 5. Statistically efficient estimation using population coding (Alexandre Pouget, Kechen Zhang, Sophie Devene and Peter E. Latham). 6. Parameter extraction from population codes: A critical assessment (Herman P. Snippe). 7. Energy efficient neural codes (William B. Levy and Robert A. Baxter). 8. Seeing beyond the Nyquist limit (Daniel L. Ruderman and William Bialek). 9. A model of spatial map formation in the hippocampus of the rat (Kenneth I. Blum and L.F. Abbott). 10. Probabilistic interpretation of population codes (Richard S. Zemel, Peter Dayan and Alexandre Pouget). 11. Cortical cells should fire regularly, but do not (William R. Softky and Christof Koch). 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). 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). 14. Coding of time-varying signals in spike trains of integrateand-fire neurons with random threshold (Fabrizio Gabbiani and Christof Koch). 15. Temporal precision of spike trains in extrastriate cortex of the behaving Macaque monkey (Wyeth Bair and Christof Koch). 16. Conversion of temporal correlations between stimuli to spatial correlations between attractors (M. Griniasty, M.V. Tsodyks and Daniel J. Amit). 17. Neural network model of the cerebellum: Temporal discrimination and the timing of motor responses (Dean V. Buonomano and Michael D. Mauk). 18. Gamma oscillation model predicts intensity coding by phase rather than frequency (Roger D. Traub, Miles A. Whittington and John G.R. Jefferys). 19. Effects of input synchrony on the firing rate of a three-conductance cortical neuron model (Venkatesh N. Murthy and Eberhard E. Fetz). 20. NMDA-based pattern discrimination in a modeled cortical neuron (Bartlett W. Mel). 21. The impact of parallel fiber background activity on the cable properties of cerebellar Purkinje cells (Moshe Rapp, Yosef Yarom and Idan Segev). Index.