@article {6100,
	title = {A distance-field based automatic neuron tracing method.},
	journal = {BMC Bioinformatics},
	volume = {14},
	year = {2013},
	month = {2013 Mar 12},
	pages = {93},
	abstract = {<p>Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.</p>
},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-14-93},
	author = {Yang, Jinzhu and Gonzalez-Bellido, Paloma T and Peng, Hanchuan}
}
@article {5523,
	title = {Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction.},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	volume = {110},
	year = {2013},
	month = {2013 Jan 8},
	pages = {696-701},
	chapter = {696},
	abstract = {<p>Intercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95\% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a population vector that reflects the direction of the target with high accuracy and reliability across 360{\textdegree}. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in population vector form.</p>
},
	issn = {1091-6490},
	doi = {10.1073/pnas.1210489109},
	author = {Gonzalez-Bellido, Paloma T and Peng, Hanchuan and Yang, Jinzhu and Georgopoulos, Apostolos P and Olberg, Robert M}
}
