TY - JOUR T1 - Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks JF - bioRxiv DO - 10.1101/141010 SP - 141010 AU - Timothy W. Dunn AU - Peter K. Koo Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/05/22/141010.abstract N2 - Measuring synaptic connectivity in large neuronal populations remains a major goal of modern neuroscience. While anatomical methods are advancing, computational techniques for inferring functional connectivity from recordings of neural activity represent a promising avenue of analysis. 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. ER -