@article {48720, title = {The complete connectome of a learning and memory centre in an insect brain.}, journal = {Nature}, volume = {548}, year = {2017}, month = {2017 Aug 09}, pages = {175-182}, abstract = {

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the Drosophila larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.

}, issn = {1476-4687}, doi = {10.1038/nature23455}, author = {Eichler, Katharina and Li, Feng and Litwin-Kumar, Ashok and Park, Youngser and Andrade, Ingrid and Schneider-Mizell, Casey M and Saumweber, Timo and Huser, Annina and Eschbach, Claire and Gerber, Bertram and Fetter, Richard D and Truman, James W and Priebe, Carey E and Abbott, L F and Thum, Andreas S and Zlatic, Marta and Cardona, Albert} } @article {48495, title = {Semiparametric spectral modeling of the Drosophila connectome.}, journal = {arXiv}, year = {2017}, month = {2017 May 9}, pages = {1705.03297}, abstract = {

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.

}, url = {https://arxiv.org/abs/1705.03297}, author = {Priebe, Carey E. and Park, Youngser and Tang, Minh and Athreya, Avanti and Lyzinski, Vince and Vogelstein, Joshua T and Qin, Yichen and Cocanougher, Benjamin and Eichler, Katharina and Zlatic, Marta and Cardona, Albert} } @article {6911, title = {Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning}, journal = {Science}, volume = {344}, year = {2014}, month = {2014 Mar 27}, pages = {386-92}, chapter = {386}, abstract = {

A single nervous system can generate many distinct motor patterns. Identifying which neurons and circuits control which behaviors has been a laborious piecemeal process, usually for one observer-defined behavior at a time. We present a fundamentally different approach to neuron-behavior mapping. We optogenetically activated 1,054 identified neuron lines in Drosophila larva and tracked the behavioral responses from 37,780 animals. Applying multiscale unsupervised structure learning methods to the behavioral data identified 29 discrete statistically distinguishable and observer-unbiased behavioral phenotypes. Mapping the neural lines to the behavior(s) they evoke provides a behavioral reference atlas for neuron subsets covering a large fraction of larval neurons. This atlas is a starting point for connectivity- and activity-mapping studies to further investigate the mechanisms by which neurons mediate diverse behaviors.

}, issn = {1095-9203}, doi = {10.1126/science.1250298}, author = {Vogelstein, Joshua T and Park, Youngser and Ohyama, Tomoko and Kerr, Rex and Truman, James W and Priebe, Carey E. and Zlatic, Marta} }