@article {65832, title = {Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jul 1}, pages = {771-4}, abstract = {

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and requires only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: 96\% of edges between connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

}, doi = {10.1038/s41592-021-01183-7}, author = {Buhmann, Julia and Sheridan, Arlo and Gerhard, Stephan and Krause, Renate and Nguyen, Tri and Heinrich, Larissa and Schlegel, Philipp and Lee, Wei-Chung Allen and Wilson, Rachel and Saalfeld, Stephan and Jefferis, Gregory and Bock, Davi and Turaga, Srinivas and Cook, Matthew and Funke, Jan} } @article {47992, title = {A mechanosensory circuit that mixes opponent channels to produce selectivity for complex stimulus features.}, journal = {Neuron}, volume = {92}, year = {2016}, month = {2016 Oct 27}, pages = {888-901}, abstract = {

Johnston{\textquoteright}s organ is the largest mechanosensory organ in\ Drosophila; it analyzes movements of the antenna due to sound, wind, gravity, and touch. Different Johnston{\textquoteright}s organ neurons (JONs) encode distinct stimulus features. Certain JONs respond in a sustained manner to steady displacements, and these JONs subdivide into opponent populations that prefer push or pull displacements. Here, we describe neurons in the brain (aPN3 neurons) that combine excitation and inhibition from push/pull JONs in different ratios. Consequently, different aPN3 neurons are sensitive to movement in different parts of the antenna{\textquoteright}s range, at different frequencies, or at different amplitude modulation rates. We use a model to show how the tuning of aPN3 neurons can arise from rectification and temporal filtering in JONs, followed by mixing of JON signals in different proportions. These results illustrate how several canonical neural circuit components{\textemdash}rectification, opponency, and filtering{\textemdash}can combine to produce selectivity for complex stimulus features.

}, doi = {10.1016/j.neuron.2016.09.059}, author = {Chang, Allison EB and Vaughan, Alexander G and Wilson, Rachel} } @article {6508, title = {Vertebrate versus invertebrate neural circuits.}, journal = {Current Biology}, volume = {23}, year = {2013}, month = {2013 Jun 17}, pages = {R504-6}, issn = {1879-0445}, author = {Katz, Paul and Grillner, Sten and Wilson, Rachel and Borst, Alexander and Greenspan, Ralph and Buzs{\'a}ki, Gy{\"o}rgy and Martin, Kevan and Marder, Eve and Kristan, William and Friedrich, Rainer and Chklovskii, Dmitri Mitya} }