@article {49518,
	title = {Visual physiology of the layer 4 cortical circuit in silico.},
	journal = {PLoS Computational Biology},
	volume = {14},
	year = {2018},
	month = {2018 Nov 12},
	pages = {e1006535},
	abstract = {<p>Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.</p>
},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1006535},
	author = {Arkhipov, Anton and Gouwens, Nathan W and Billeh, Yazan N and Gratiy, Sergey and Iyer, Ramakrishnan and Wei, Ziqiang and Xu, Zihao and Abbasi-Asl, Reza and Berg, Jim and Buice, Michael and Cain, Nicholas and da Costa, Nuno and de Vries, Saskia and Denman, Daniel and Durand, Severine and Feng, David and Jarsky, Tim and Lecoq, Jerome and Lee, Brian and Li, Lu and Mihalas, Stefan and Ocker, Gabriel K and Olsen, Shawn R and Reid, R Clay and Soler-Llavina, Gilberto and Sorensen, Staci A and Wang, Quanxin and Waters, Jack and Scanziani, Massimo and Koch, Christof}
}
@article {48220,
	title = {Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction.},
	journal = {bioRxiv},
	year = {2017},
	month = {2017 Jan 19},
	abstract = {<p>Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating a channel-covariance feature and a clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes a 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.</p>
},
	doi = {10.1101/101030},
	author = {Jun, James Jaeyoon and Mitelut, Catalin and Lai, Chongxi and Gratiy, Sergey and Anastassiou, Costas and Harris, Timothy D}
}
