Reproducibility of in-vivo 1 electrophysiological measurements 2 in mice 3

20 Understanding whole-brain-scale electrophysiological recordings will rely on the collective work 21 of multiple labs. Because two labs recording from the same brain area often reach different 22 conclusions, it is critical to quantify and control for features that decrease reproducibility. To 23 address these issues, we formed a multi-lab collaboration using a shared, open-source 24 behavioral task and experimental apparatus. We repeatedly inserted Neuropixels multi-electrode 25

[1]  Anne E. Urai,et al.  Large-scale neural recordings call for new insights to link brain and behavior , 2022, Nature Neuroscience.

[2]  Richard J. Gardner,et al.  Grid-cell modules remain coordinated when neural activity is dissociated from external sensory cues , 2021, Neuron.

[3]  Edward A. B. Horrocks,et al.  Creating and controlling visual environments using BonVision , 2021, eLife.

[4]  Nicholas A. Roy,et al.  Mice alternate between discrete strategies during perceptual decision-making , 2020, bioRxiv.

[5]  N. Altman,et al.  Reproducibility of animal research in light of biological variation , 2020, Nature Reviews Neuroscience.

[6]  L. Ng,et al.  The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas , 2020, Cell.

[7]  Fanny Cazettes,et al.  Standardized and reproducible measurement of decision-making in mice , 2020, bioRxiv.

[8]  Eric Shea-Brown,et al.  A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex , 2019, Nature Neuroscience.

[9]  Timothy E. J. Behrens,et al.  Human Replay Spontaneously Reorganizes Experience , 2019, Cell.

[10]  Leon A. Gatys,et al.  Deep convolutional models improve predictions of macaque V1 responses to natural images , 2017, bioRxiv.

[11]  Liam Paninski,et al.  Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses , 2016, ICLR.

[12]  Thomas J. Wills,et al.  Absence of Visual Input Results in the Disruption of Grid Cell Firing in the Mouse , 2016, Current Biology.

[13]  Thomas E. Nichols,et al.  Best practices in data analysis and sharing in neuroimaging using MRI , 2017, Nature Neuroscience.

[14]  M. Baker.  1,500 scientists lift the lid on reproducibility , 2016, Nature.

[15]  Naoshige Uchida,et al.  Demixed principal component analysis of neural population data , 2014, eLife.

[16]  Karel Svoboda,et al.  A platform for brain-wide imaging and reconstruction of individual neurons , 2016, eLife.

[17]  J. O’Keefe,et al.  Grid cell firing patterns signal environmental novelty by expansion , 2012, Proceedings of the National Academy of Sciences.

[18]  Y. Saalmann,et al.  Cognitive and Perceptual Functions of the Visual Thalamus , 2011, Neuron.

[19]  A. Pouget,et al.  Variance as a Signature of Neural Computations during Decision Making , 2011, Neuron.

[20]  G. Dragoi,et al.  Preplay of future place cell sequences by hippocampal cellular assemblies , 2011, Nature.

[21]  Andrew M. Clark,et al.  Stimulus onset quenches neural variability: a widespread cortical phenomenon , 2010, Nature Neuroscience.

[22]  Suramya Tomar,et al.  Converting video formats with FFmpeg , 2006 .

[23]  Li I. Zhang,et al.  A critical window for cooperation and competition among developing retinotectal synapses , 1998, Nature.

[24]  G Buzsáki,et al.  Dentate EEG spikes and associated interneuronal population bursts in the hippocampal hilar region of the rat. , 1995, Journal of neurophysiology.

[25]  J. Movshon,et al.  The statistical reliability of signals in single neurons in cat and monkey visual cortex , 1983, Vision Research.

[26]  A. Izenman.  Reduced-rank regression for the multivariate linear model , 1975 .

[27]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .