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FARCI: Fast and Robust Connectome Inference

By Saber Meamardoost, Mahasweta Bhattacharya, EunJung Hwang, Takaki Komiyama, Claudia Mewes, Linbing Wang, Ying Zhang, R Gunawan

Posted 08 Oct 2020
bioRxiv DOI: 10.1101/2020.10.07.330175

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using gold standard datasets from the Neural Connectomics Challenge (NCC) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison to the best performing algorithm in the NCC, FARCI produces more accurate networks over different network sizes and subsampling, while providing over two orders of magnitude faster computational speed. ### Competing Interest Statement The authors have declared no competing interest.

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