Scalable covariance-based connectivity inference for synchronous neuronal networks

We present a novel method for inferring connectivity from large-scale neuronal networks with synchronous activity. Our approach leverages Dynamic Differential Covariance to address the associated computational challenges. First, we analyze spike trains generated from Leaky Integrate-and-Fire network simulations and evaluate the performance of several off-the-shelf multivariate connectivity inference methods. Next, we introduce a new approach, Fractional Dynamic Differential Covariance (FDDC), and demonstrate that it consistently outperforms the other methods. Finally, we apply FDDC to experimental data to assess the topological organization of inferred graphs of in vitro neural network recordings obtained using high-density microelectrode arrays (HD-MEAs). Our results indicate that FDDC-derived graphs exhibit a significant negative correlation between small-worldness and measures of network synchrony. In contrast, graphs inferred through the well-established pairwise correlation method do not show such a correlation. This finding implies that the graphs obtained through FDDC provide stronger evidence in support of the theoretical notion that networks with clustered connections tend to exhibit higher levels of synchronizability. We hypothesize that our findings have implications for the development of scalable connectivity inference methods for large-scale neural network data.