@article {5162,
	title = {Activity in motor-sensory projections reveals distributed coding in somatosensation.},
	journal = {Nature},
	volume = {489},
	year = {2012},
	month = {2012 Sep 13},
	pages = {299-303},
	abstract = {<p>Cortical-feedback projections to primary sensory areas terminate most heavily in layer 1 (L1) of the neocortex, where they make synapses with tuft dendrites of pyramidal neurons. L1 input is thought to provide {\textquoteleft}contextual{\textquoteright} information, but the signals transmitted by L1 feedback remain uncharacterized. In the rodent somatosensory system, the spatially diffuse feedback projection from vibrissal motor cortex (vM1) to vibrissal somatosensory cortex (vS1, also known as the barrel cortex) may allow whisker touch to be interpreted in the context of whisker position to compute object location. When mice palpate objects with their whiskers to localize object features, whisker touch excites vS1 and later vM1 in a somatotopic manner. Here we use axonal calcium imaging to track activity in vM1{\textendash}\&gt;vS1 afferents in L1 of the barrel cortex while mice performed whisker-dependent object localization. Spatially intermingled individual axons represent whisker movements, touch and other behavioural features. In a subpopulation of axons, activity depends on object location and persists for seconds after touch. Neurons in the barrel cortex thus have information to integrate movements and touches of multiple whiskers over time, key components of object identification and navigation by active touch.</p>
},
	issn = {1476-4687},
	doi = {10.1038/nature11321},
	author = {Petreanu, Leopoldo and Gutnisky, Diego A and Huber, Daniel and Xu, Ning-long and O{\textquoteright}Connor, Dan H and Tian, Lin and Looger, Loren and Svoboda, Karel}
}
@article {5200,
	title = {Automated tracking of whiskers in videos of head fixed rodents.},
	journal = {PLoS Computational Biology},
	volume = {8},
	year = {2012},
	month = {2012 Jul},
	pages = {e1002591},
	abstract = {<p>We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (\&gt;500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998\% and identifies individual whiskers with 99.997\% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px{\texttimes}352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.</p>
},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1002591},
	author = {Clack, Nathan G and O{\textquoteright}Connor, Daniel H and Huber, Daniel and Petreanu, Leopoldo and Hires, Andrew and Peron, Simon and Svoboda, Karel and Myers, Eugene W}
}
