@article {47812,
	title = {Improving data quality in neuronal population recordings.},
	journal = {Nature Neuroscience},
	volume = {19},
	year = {2016},
	month = {2016 Aug 26},
	pages = {1165-74},
	abstract = {<p>Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.</p>
},
	issn = {1546-1726},
	doi = {10.1038/nn.4365},
	author = {Harris, Kenneth D and Quiroga, Rodrigo Quian and Freeman, Jeremy and Smith, Spencer L}
}
@article {47813,
	title = {Technologies for imaging neural activity in large volumes.},
	journal = {Nature Neuroscience},
	volume = {19},
	year = {2016},
	month = {2016 Aug 26},
	pages = {1154-64},
	abstract = {<p>Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.</p>
},
	issn = {1546-1726},
	doi = {10.1038/nn.4358},
	author = {Ji, Na and Freeman, Jeremy and Smith, Spencer L}
}
