Data from: Stability of spontaneous, correlated activity in mouse auditory cortex

Neural systems can be modeled as complex networks in which neural elements
are represented as nodes linked to one another through structural or
functional connections. The resulting network can be analyzed using
mathematical tools from network science and graph theory to quantify the
system's topological organization and to better understand its
function. Here, we used two-photon calcium imaging to record spontaneous
activity from the same set of cells in mouse auditory cortex over the
course of several weeks. We reconstruct functional networks in which cells
are linked to one another by edges weighted according to the correlation
of their fluorescence traces. We show that the networks exhibit modular
structure across multiple topological scales and that these multi-scale
modules unfold as part of a hierarchy. We also show that, on average,
network architecture becomes increasingly dissimilar over time, with
similarity decaying monotonically with the distance (in time) between
sessions. Finally, we show that a small fraction of cells maintain
strongly-correlated activity over multiple days, forming a stable temporal
core surrounded by a fluctuating and variable periphery. Our work
indicates a framework for studying spontaneous activity measured by
two-photon calcium imaging using computational methods and graphical
models from network science. The methods are flexible and easily extended
to additional datasets, opening the possibility of studying cellular level
network organization of neural systems and how that organization is
modulated by stimuli or altered in models of disease.