  Discussions of the hippocampus often focus on place cells, but many neurons
are not place cells in any given environment. Here we describe the collective
activity in such mixed populations, treating place and non-place cells on the
same footing. We start with optical imaging experiments on CA1 in mice as they
run along a virtual linear track, and use maximum entropy methods to
approximate the distribution of patterns of activity in the population,
matching the correlations between pairs of cells but otherwise assuming as
little structure as possible. We find that these simple models accurately
predict the activity of each neuron from the state of all the other neurons in
the network, regardless of how well that neuron codes for position. These and
other results suggest that place cells are not a distinct sub-network, but part
of a larger system that encodes, collectively, more than just place
information.
