@article {69774, title = {Neuropixels Opto: Combining high-resolution electrophysiology and optogenetics}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 06}, abstract = {

High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations, and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 1 cm shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent an unprecedented tool for recording, identifying, and manipulating neuronal populations.

}, doi = {10.1101/2025.02.04.636286}, url = {https://www.biorxiv.org/content/early/2025/02/06/2025.02.04.636286}, author = {Lakunina, Anna and Socha, Karolina Z and Ladd, Alexander and Bowen, Anna J and Chen, Susu and Colonell, Jennifer and Doshi, Anjal and Karsh, Bill and Krumin, Michael and Kulik, Pavel and Li, Anna and Neutens, Pieter and O{\textquoteright}Callaghan, John and Olsen, Meghan and Putzeys, Jan and Tilmans, Harrie AC and Ye, Zhiwen and Welkenhuysen, Marleen and H{\"a}usser, Michael and Koch, Christof and Ting, Jonathan T. and Neuropixels Opto Consortium and Dutta, Barun and Harris, Timothy D and Steinmetz, Nicholas A and Svoboda, Karel and Siegle, Joshua H and Carandini, Matteo} } @article {68968, title = {Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings.}, journal = {bioRxiv}, year = {2023}, month = {2023 Aug 25}, abstract = {

To study the neural basis of behavior, we require methods to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology is a principal method for achieving this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To overcome these limitations, we developed a silicon probe with significantly smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device measures neuronal activity at ultra-high densities (\>1300 sites per mm, 10 times higher than previous probes), with 6 {\textmu}m center-to-center spacing and low noise. This device effectively comprises an implantable voltage-sensing camera that captures a planar image of a neuron{\textquoteright}s electrical field. We introduce a new spike sorting algorithm optimized for these probes and use it to find that the yield of visually-responsive neurons in recordings from mouse visual cortex improves \~{}3-fold. Recordings across multiple brain regions and four species revealed a subset of unexpectedly small extracellular action potentials not previously reported. Further experiments determined that, in visual cortex, these do not correspond to major subclasses of interneurons and instead likely reflect recordings from axons. Finally, using ground-truth identification of cortical inhibitory cell types with optotagging, we found that cell type was discriminable with approximately 75\% success among three types, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, sampling bias, and cell type classification.

}, doi = {10.1101/2023.08.23.554527}, author = {Ye, Zhiwen and Shelton, Andrew M and Shaker, Jordan R and Boussard, Julien and Colonell, Jennifer and Manavi, Sahar and Chen, Susu and Windolf, Charlie and Hurwitz, Cole and Namima, Tomoyuki and Pedraja, Federico and Weiss, Shahaf and Raducanu, Bogdan and Ness, Torbj{\o}rn V and Einevoll, Gaute T and Laurent, Gilles and Sawtell, Nathaniel B and Bair, Wyeth and Pasupathy, Anitha and Mora Lopez, Carolina and Dutta, Barun and Paninski, Liam and Siegle, Joshua H and Koch, Christof and Olsen, Shawn R and Harris, Timothy D and Steinmetz, Nicholas A} } @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 {65615, title = {Arousal modulates retinal output.}, journal = {Neuron}, volume = {107}, year = {2020}, month = {2020 Aug 01}, pages = {487}, abstract = {

At various stages of the visual system, visual responses are modulated by arousal. Here, we find that in mice this modulation operates as early as in the first synapse from the retina and even in retinal axons. To measure retinal activity in the awake, intact brain, we imaged the synaptic boutons of retinal axons in the superior colliculus. Their activity depended not only on vision but also on running speed and pupil size, regardless of retinal illumination. Arousal typically reduced their visual responses and selectivity for direction and orientation. Recordings from retinal axons in the optic tract revealed that arousal modulates the firing of some retinal ganglion cells. Arousal had similar effects postsynaptically in colliculus neurons, independent of activity in the other main source of visual inputs to the colliculus, the primary visual cortex. These results indicate that arousal modulates activity at every stage of the mouse visual system.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2020.04.026}, author = {Schr{\"o}der, Sylvia and Steinmetz, Nicholas A and Krumin, Michael and Pachitariu, Marius and Rizzi, Matteo and Lagnado, Leon and Harris, Kenneth D and Carandini, Matteo} } @article {49534, title = {High-dimensional geometry of population responses in visual cortex.}, journal = {Nature}, volume = {571}, year = {2019}, month = {2019 Jun 26}, pages = {361-65}, abstract = {

A neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the n-th principal component variance scaled as 1/n. This was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness represents a fundamental constraint governing correlations in neural population codes.

}, doi = {10.1038/s41586-019-1346-5}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Carandini, Matteo and Harris, Kenneth D} } @article {49187, title = {Spontaneous behaviors drive multidimensional, brain-wide population activity.}, journal = {Science}, volume = {364}, year = {2019}, month = {2019 Apr 18}, pages = {255}, abstract = {

Sensory cortices are active in the absence of external sensory stimuli. To understand the nature of this ongoing activity, we used two-photon calcium imaging to record from over 10,000 neurons in the visual cortex of mice awake in darkness while monitoring their behavior videographically. Ongoing population activity was multidimensional, exhibiting at least 100 significant dimensions, some of which were related to the spontaneous behaviors of the mice. The largest single dimension was correlated with the running speed and pupil area, while a 16-dimensional summary of orofacial behaviors could predict ~45\% of the explainable neural variance. Electrophysiological recordings with 8 simultaneous Neuropixels probes revealed a similar encoding of high-dimensional orofacial behaviors across multiple forebrain regions. Representation of motor variables continued uninterrupted during visual stimulus presentation, occupying dimensions nearly orthogonal to the stimulus responses. Our results show that a multidimensional representation of motor state is encoded across the forebrain, and is integrated with visual input by neuronal populations in primary visual cortex.

}, doi = {10.1101/306019}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Reddy, Charu Bai and Carandini, Matteo and Harris, Kenneth 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} } @conference {48832, title = {Fast and accurate spike sorting of high-channel count probes with KiloSort.}, booktitle = {Neural Information Processing Systems (NIPS 2016)}, year = {2016}, month = {2016 Dec 05}, address = {Barcelona, Spain}, abstract = {

New silicon technology is enabling large-scale electrophysiological recordings in vivo from hundreds to thousands of channels. Interpreting these recordings requires scalable and accurate automated methods for spike sorting, which should minimize the time required for manual curation of the results. Here we introduce KiloSort, a new integrated spike sorting framework that uses template matching both during spike detection and during spike clustering. KiloSort models the electrical voltage as a sum of template waveforms triggered on the spike times, which allows overlapping spikes to be identified and resolved. Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms. Processing times are faster than in previous algorithms thanks to batch-based optimization on GPUs. We compare KiloSort to an established algorithm and show favorable performance, at much reduced processing times. A novel post-clustering merging step based on the continuity of the templates further reduced substantially the number of manual operations required on this data, for the neurons with near-zero error rates, paving the way for fully automated spike sorting of multichannel electrode recordings.

}, url = {http://papers.nips.cc/paper/6326-fast-and-accurate-spike-sorting-of-high-channel-count-probes-with-kilosort}, author = {Pachitariu, Marius and Steinmetz, Nicholas A and Kadir, Shabnam N and Carandini, Matteo and Harris, Kenneth 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} }