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Extended Mean-Field Theories for Networks of Real Neurons
Luca Di Carlo1, Francesca Mignacco1,2, Christopher W Lynn3
1Princeton University, Joseph Henry Laboratories of Physics and Lewis-Sigler Institute, Princeton, New Jersey 08544, USA.
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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