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Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks

Timothy W. Dunn, Peter K. Koo
doi: https://doi.org/10.1101/141010
Timothy W. Dunn
Harvard University
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Peter K. Koo
Harvard University
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Abstract

Measuring synaptic connectivity in large neuronal populations remains a major goal of modern neuroscience. While this connectivity is traditionally revealed by anatomical methods such as electron microscopy, an efficient alternative is to computationally infer functional connectivity from recordings of neural activity. However, these statistical techniques still require further refinement before they can be reliably applied to real data. Here, we report significant improvements to a deep learning method for functional connectomics, as assayed on synthetic ChaLearn Connectomics data. The method, which integrates recent advances in convolutional neural network architecture and model-free partial correlation coefficients, outperforms published methods on competition data and can achieve over 90% precision at 1% recall on validation datasets. This suggests that future application of the model to in vivo whole-brain imaging data in larval zebrafish could reliably recover on the order of 106 synaptic connections with a 10% false discovery rate. The model also generalizes to networks with different underlying connection probabilities and should scale well when parallelized across multiple GPUs. The method offers real potential as a statistical complement to existing experiments and circuit hypotheses in neuroscience.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 25, 2017.
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Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks
Timothy W. Dunn, Peter K. Koo
bioRxiv 141010; doi: https://doi.org/10.1101/141010
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Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks
Timothy W. Dunn, Peter K. Koo
bioRxiv 141010; doi: https://doi.org/10.1101/141010

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