Stimulation-mediated reverse engineering of silent neural networks

Abstract

Reconstructing connectivity of neuronal networks from single cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of realistic silent neuronal networks using stimulation combined with a supervised learning algorithm, that enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell level with high accuracy. These testable predictions about the number and protocol of the required stimulations is expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.