Online Neural Connectivity Estimation with Noisy Group Testing
Published in Advances in Neural Information Processing Systems (NeurIPS), 2020
Recommended citation: Draelos, A., Pearson, J. M. (2020). "Online Neural Connectivity Estimation with Noisy Group Testing." Advances in Neural Information Processing Systems 33.' https://proceedings.neurips.cc/paper/2020/file/531d29a813ef9471aad0a5558d449a73-Paper.pdf
We describe a method based on noisy group testing to infer functional connections in large networks of neurons. By stimulating small ensembles of neurons, we show that it is possible to recover binarized network connectivity with a number of tests that grows only logarithmically with population size under minimal statistical assumptions. We extend our method to the streaming setting, where continuously updated posteriors allow for optional stopping, and demonstrate the feasibility of inferring connectivity for networks of up to tens of thousands of neurons online.