Fast Algorithm for Neural Network Reconstruction

We propose an efficient and accurate way of predicting the connectivity of neural networks in the brain represented by simulated calcium fluorescence data. Classical methods to neural network reconstruction compute a connectivity matrix whose entries are pairwise likelihoods of directed excitatory connections based on time-series signals of each pair of neurons. Our method uses only a fraction of this computation to achieve equal or better performance. The proposed method is based on matrix completion and a local thresholding technique. By computing a subset of the total entries in the connectivity matrix, we use matrix completion to determine the rest of the connection likelihoods, and apply a local threshold to identify which directed connections exist in the underlying network. We validate the proposed method on a simulated calcium fluorescence dataset. The proposed method outperforms the classical one with 20% of the computation.