  Understanding the activity of large populations of neurons is difficult due
to the combinatorial complexity of possible cell-cell interactions. To reduce
the complexity, coarse-graining had been previously applied to experimental
neural recordings, which showed over two decades of scaling in free energy,
activity variance, eigenvalue spectra, and correlation time, hinting that the
mouse hippocampus operates in a critical regime. We model the experiment by
simulating conditionally independent binary neurons coupled to a small number
of long-timescale stochastic fields and then replicating the coarse-graining
procedure and analysis. This reproduces the experimentally-observed scalings,
suggesting that they may arise from coupling the neural population activity to
latent dynamic stimuli. Further, parameter sweeps for our model suggest that
emergence of scaling requires most of the cells in a population to couple to
the latent stimuli, predicting that even the celebrated place cells must also
respond to non-place stimuli.
