@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 = {

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.

}, 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} } @article {48935, title = {Fully integrated silicon probes for high-density recording of neural activity.}, journal = {Nature}, volume = {551}, year = {2017}, month = {2017 Nov 08}, pages = {232-236}, abstract = {

Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca(2+) imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 {\texttimes} 20-μm cross-section shank. The 6 {\texttimes} 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.

}, issn = {1476-4687}, doi = {10.1038/nature24636}, author = {Jun, James J and Steinmetz, Nicholas A and Siegle, Joshua H and Denman, Daniel J and Bauza, Marius and Barbarits, Brian and Lee, Albert K and Anastassiou, Costas A and Andrei, Alexandru and Ayd{\i}n, {\c C}a{\u g}atay and Barbic, Mladen and Blanche, Timothy J and Bonin, Vincent and Couto, Jo{\~a}o and Dutta, Barundeb and Gratiy, Sergey L and Gutnisky, Diego A and H{\"a}usser, Michael and Karsh, Bill and Ledochowitsch, Peter and Lopez, Carolina Mora and Mitelut, Catalin and Musa, Silke and Okun, Michael and Pachitariu, Marius and Putzeys, Jan and Rich, P Dylan and Rossant, Cyrille and Sun, Wei-Lung and Svoboda, Karel and Carandini, Matteo and Harris, Kenneth D and Koch, Christof and O{\textquoteright}Keefe, John and Harris, Timothy D} } @article {48842, title = {Inhibitory control of correlated intrinsic variability in cortical networks.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Dec 07}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, issn = {2050-084X}, doi = {10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} } @article {65751, title = {Inhibitory control of correlated intrinsic variability in cortical networks}, journal = {Elife}, volume = {5}, year = {2016}, month = {12/2016}, pages = {e19695}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, doi = {https://doi.org/10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} }