Probabilistic interpretation of population codes
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Distributional population codes and multiple motion models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Unifying Perspectives on Neuronal Codes and Processing
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Spatial transformations in the parietal cortex using basis functions
Journal of Cognitive Neuroscience
Integration of form and motion within a generative model of visual cortex
Neural Networks - 2004 Special issue Vision and brain
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Most theoretical and empirical studies of cortical population codes make the assumption that underlying neuronal activities is a unique and unambiguous value of an encoded quantity. We propose an alternative hypothesis, that neural populations represent, and effectively compute, probabilities. Under this hypothesis, population activities can contain additional information about such things as multiple values of, or uncertainty about, the quantity. We discuss methods for recovering this extra information, and show how this approach bears on psychophysical and neurophysiological studies. A natural extension of this probabilistic interpretation hypothesis casts interacting populations as a belief network, a structure which permits the analysis of information propagation from one population to another. This novel framework for population codes opens up new avenues for studying a diverse set of problems, including cue combination, decision-making, and visual attention.