@article {67859,
	title = {Toroidal topology of population activity in grid cells.},
	journal = {Nature},
	volume = {602},
	year = {2022},
	month = {2022 Jan 12},
	pages = {123-128},
	abstract = {<p>The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations, and are organized in modules that collectively form a population code for the animal{\textquoteright}s allocentric position. The invariance of the correlation structure of this population code across environments and behavioural states, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.</p>
},
	issn = {1476-4687},
	doi = {10.1038/s41586-021-04268-7},
	author = {Gardner, Richard J and Hermansen, Erik and Pachitariu, Marius and Burak, Yoram and Baas, Nils A and Dunn, Benjamin A and Moser, May-Britt and Moser, Edvard I}
}
@article {67232,
	title = {Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings.},
	journal = {Science},
	volume = {372},
	year = {2021},
	month = {2021 Apr 16},
	abstract = {<p>Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.</p>
},
	issn = {1095-9203},
	doi = {10.1126/science.abf4588},
	author = {Steinmetz, Nicholas A and Ayd{\i}n, {\c C}a{\u g}atay and Lebedeva, Anna and Okun, Michael and Pachitariu, Marius and Bauza, Marius and Beau, Maxime and Bhagat, Jai and B{\"o}hm, Claudia and Broux, Martijn and Chen, Susu and Colonell, Jennifer and Gardner, Richard J and Karsh, Bill and Kloosterman, Fabian and Kostadinov, Dimitar and Mora-Lopez, Carolina and O{\textquoteright}Callaghan, John and Park, Junchol and Putzeys, Jan and Sauerbrei, Britton and van Daal, Rik J J and Vollan, Abraham Z and Wang, Shiwei and Welkenhuysen, Marleen and Ye, Zhiwen and Dudman, Joshua T and Dutta, Barundeb and Hantman, Adam W and Harris, Kenneth D and Lee, Albert K and Moser, Edvard I and O{\textquoteright}Keefe, John and Renart, Alfonso and Svoboda, Karel and H{\"a}usser, Michael and Haesler, Sebastian and Carandini, Matteo and Harris, Timothy D}
}
