Stability of spontaneous, correlated activity in mouse auditory cortex

Abstract

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. While the network-based approach has become common in the analysis of large-scale neural systems probed by non-invasive neuroimaging, few studies have used network science to study the organization of biological neuronal networks reconstructed at the cellular level, and thus many very basic and fundamental questions remain unanswered. 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 maximum lagged 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 provides a careful methodological blueprint for future studies of spontaneous activity measured by two-photon calcium imaging using cutting-edge computational methods and machine learning algorithms informed by explicit 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.

Strong and weak principles of neural dimension reduction

“…Finding that such correlations are stable over long periods of time is consistent with the existence of some kind of low-dimensional system realised by that population. Such long-lasting correlations have been reported for populations of grid cells [47], and of head-direction cells across waking and sleep [48], populations of the Aplysia motor system across an hour or more [17], for noise correlations in layer 2/3 of primary visual cortex [49], and for spontaneous activity in primary auditory cortex over days [50,51].…”

Autonomous emergence of connectivity assemblies via spike triplet interactions

“…The resulting graphs can be described by standard metrics, whose dependence on the shape of the learning rule and the EPSC function might yield insight into the emergent structures during circuit organization driven by spontaneous activity. We focused on common quantities for describing graph structure, including the clustering coefficient, the global efficiency and the modularity [92,93], used previously in experimental systems like the zebrafish tectum and the mammalian cortex [25,94].…”

Neuronal activity in sensory cortex predicts the specificity of learning

“…We would not expect this result were the stimuli not relevant for AC. Future studies will dissect to what extent the differences in neuronal codes in AC shape differential fear learning of more complex and natural sounds and its role in other forms of learning 13,34,48,49 .…”