Basics

Humphrey et al., 1970: Predicting Measures of Motor Performance from Multiple Cortical Spike Trains. Science, `70. First population decoding ref?

Seung/Sompolinsky93: Simple models for reading Neuronal population codes [Lays out basic ML story, and discrimination (retold in Dayan/Abbott book)]

Salinas/Abbott94: Vector Reconstruction from Firing Rates [early decoding paper]

Sanger98: Probability Density Methods for Smooth Function Approximation and Learning in Populations of Tuned Spiking Neurons [another decoding paper, concentrates more on ML]

Gold/Shadlen01: Neural comptations that underlie decisions about sensory stimuli [Representing likelihood ratios with neurons]

Stanley-etal99: Reconstruction of Natural Scenes from Ensemble Responses in the LGN


Effects of conditional dependence

Abbott/Dayan99: Effect of Correlated Variability on the Accuracy of a Population Code

Wu, Nakahara, Amari `01 Population Coding with Correlation and an Unfaithful Model [What happens when your encoding model is wrong?]

Pouget-etal99: Narrow vs. Wide Tuning Curves: What's best for a population code? [Answer depends critically on noise covariance]

Sompolinsky01: Population coding in Neuronal Systems with Correlated Noise

Wilke-Eurich02: On the functional role of noise correlations in the nervous system, by S. D. Wilke and C. W. Eurich, Neurocomputing (in press).


Effects of shape of tuning curve

Zhang/Sejnowski99: Neuronal Tuning: To Sharpen or Broaden? [Accuracy (Fisher info) as a function of dimension of encoded variable]

Eurich/Wilke00: Multidimensional Encoding Strategy of Spiking Neurons [optimal tuning widths for D features]

Wilke/Eurich02: Representational Accuracy of Stochastic Neural Populations [Fisher Info analysis for encoding of D features, with correlations]


Other

Bialek01: Complexity Through Nonextensivity [analysis of I(parameter; data)]

Zemel-etal98: Probabilistic Interpretation of Population Codes