Starting from minimum description length, the paper derives and reviews the minimax entropy principle: the optimal features are those that yield the maximum entropy model with the lowest entropy.
Extended mean-field theories for networks of real neurons
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abstract
If the behavior of a system with many degrees of freedom can be captured by a small number of collective variables, then plausibly there is an underlying mean-field theory. We show that simple versions of this idea fail to describe the patterns of activity in networks of real neurons. An extended mean-field theory that matches the distribution of collective variables is at least consistent, though shows signs that these networks are poised near a critical point, in agreement with other observations. These results suggest a path to analysis of emerging data on ever larger numbers of neurons.
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Minimax entropy: The statistical physics of optimal models
Starting from minimum description length, the paper derives and reviews the minimax entropy principle: the optimal features are those that yield the maximum entropy model with the lowest entropy.