Carey E. Priebe

TL;DR: In this paper, the authors describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many subcellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database.

TL;DR: It is found that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons, which maximizes performance of a model output neuron on a stimulus discrimination task.

TL;DR: A theory of scan statistics on graphs is introduced and the ideas applied to the problem of anomaly detection in a time series of Enron email graphs are applied.

TL;DR: It is proved that this method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel is consistent for assigning nodes to blocks, as only a negligible number of nodes will be misassigned.

TL;DR: A fundamentally different approach to neuron-behavior mapping is presented, which optogenetically activated 1054 identified neuron lines in Drosophila larvae and tracked the behavioral responses from 37,780 animals, and enabled us to identify 29 discrete, statistically distinguishable, observer-unbiased behavioral phenotypes.