@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 {70133, title = {Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings.}, journal = {Neuron}, year = {2025}, month = {2025 Sep 30}, abstract = {

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single-neuron and single-spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). Using NP Ultra, neuronal yield in mouse visual cortex recordings increased by more than 2-fold. With ultra-high spatial resolution, we discovered that a feature of extracellular waveforms, the spatial extent or "footprint," distinguished axonal from somatic recordings. In addition, three genetically identified cortical cell types could be discriminated from one another with \~{}80\% accuracy and from other neurons with \~{}85\% accuracy. NP Ultra improves yield, detection of subcellular compartments, and cell type identification to enable a more powerful dissection of neural circuit activity during behavior.

bioRxiv preprint: doi.org/10.1101/2023.08.23.554527

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2025.08.030}, url = {https://doi.org/10.1016/j.neuron.2025.08.030}, author = {Ye, Zhiwen and Shelton, Andrew M and Shaker, Jordan R and Boussard, Julien and Colonell, Jennifer and Birman, Daniel and Manavi, Sahar and Chen, Susu and Windolf, Charlie and Hurwitz, Cole and Yu, Han and Namima, Tomoyuki and Pedraja, Federico and Weiss, Shahaf and Raducanu, Bogdan C and Ness, Torbj{\o}rn V and Jia, Xiaoxuan and Mastroberardino, Giulia and Rossi, L Federico and Carandini, Matteo and H{\"a}usser, Michael and Einevoll, Gaute T and Laurent, Gilles and Sawtell, Nathaniel B and Bair, Wyeth and Pasupathy, Anitha and Mora Lopez, Carolina and Dutta, Barundeb 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 {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} } @proceedings {48942, title = {Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit.}, year = {2017}, month = {2017 Dec 04}, abstract = {

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Our joint model includes at least two sets of discrete random variables; to avoid the dramatic slowdown typically caused by being unable to differentiate such variables, we introduce two strategies that have not, to our knowledge, been used with variational autoencoders. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

}, url = {https://papers.nips.cc/paper/6940-model-based-bayesian-inference-of-neural-activity-and-connectivity-from-all-optical-interrogation-of-a-neural-circuit}, author = {Aitchison, Laurence and Russell, Lloyd and Packer, Adam M and Yan, Jinyao and Castonguaye, Philippe and H{\"a}usser, Michael and Turaga, Srinivas C} } @conference {67221, title = {Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit}, booktitle = {Advances in Neural Information Processing Systems}, year = {2017}, publisher = {Curran Associates, Inc.}, organization = {Curran Associates, Inc.}, abstract = {

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

}, url = {https://discovery.ucl.ac.uk/id/eprint/10072143/}, author = {Aitchison, Laurence and Russell, Lloyd and Packer, Adam M and Yan, Jinyao and Castonguay, Philippe and H{\"a}usser, Michael and Turaga, Srinivas C}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett} } @article {47017, title = {Optogenetics: 10 years after ChR2 in neurons-views from the community.}, journal = {Nature Neuroscience}, volume = {18}, year = {2015}, month = {2015 Aug 26}, pages = {1202-12}, issn = {1546-1726}, doi = {10.1038/nn.4106}, author = {Adamantidis, Antoine and Arber, Silvia and Bains, Jaideep S and Bamberg, Ernst and Bonci, Antonello and Buzs{\'a}ki, Gy{\"o}rgy and Cardin, Jessica A and Costa, Rui M and Dan, Yang and Goda, Yukiko and Graybiel, Ann M and H{\"a}usser, Michael and Hegemann, Peter and Huguenard, John R and Insel, Thomas R and Janak, Patricia H and Johnston, Daniel and Josselyn, Sheena A and Koch, Christof and Kreitzer, Anatol C and L{\"u}scher, Christian and Malenka, Robert C and Miesenb{\"o}ck, Gero and Nagel, Georg and Roska, Botond and Schnitzer, Mark J and Shenoy, Krishna V and Soltesz, Ivan and Sternson, Scott M and Tsien, Richard W and Tsien, Roger Y and Turrigiano, Gina G and Tye, Kay M and Wilson, Rachel I} } @conference {48833, title = {Extracting regions of interest from biological images with convolutional sparse block coding.}, booktitle = {Neural Information Processing Systems (NIPS 2013)}, year = {2013}, month = {2013 Dec 05}, address = {Lake Tahoe}, abstract = {

Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.

}, url = {http://papers.nips.cc/paper/5167-extracting-regions-of-interest-from-biological-images-with-convolutional-sparse-block-coding}, author = {Pachitariu, Marius and Packer, Adam M and Pettit, Noah and Dalgleish, Henry and H{\"a}usser, Michael and Sahani, Maneesh} } @article {46575, title = {Inferring neural population dynamics from multiple partial recordings of the same neural circuit.}, journal = {Advances in Neural Information Processing Systems (NIPS) }, volume = {26}, year = {2013}, month = {2013}, abstract = {

Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons using 2-photon calcium imaging. However, many computations are thought to involve circuits consisting of thousands of neurons, such as cortical barrels in rodent somatosensory cortex. Here we contribute a statistical method for stitching" together sequentially imaged sets of neurons into one model by phrasing the problem as fitting a latent dynamical system with missing observations. This method allows us to substantially expand the population-sizes for which population dynamics can be characterized---beyond the number of simultaneously imaged neurons. In particular, we demonstrate using recordings in mouse somatosensory cortex that this method makes it possible to predict noise correlations between non-simultaneously recorded neuron pairs.

}, url = {http://papers.nips.cc/paper/4874-inferring-neural-population-dynamics-from-multiple-partial-recordings-of-the-same-neural-circuit}, author = {Turaga, Srinivas C and Buesing, Lars and Packer, Adam M. and Dalgleish, Henry and Pettit, Noah and H{\"a}usser, Michael and Macke, Jakob H.} } @article {45891, title = {Dendritic patch-clamp recording.}, journal = {Nature Protocols}, volume = {1}, year = {2006}, month = {2006}, pages = {1235-47}, abstract = {

The patch-clamp technique allows investigation of the electrical excitability of neurons and the functional properties and densities of ion channels. Most patch-clamp recordings from neurons have been made from the soma, the largest structure of individual neurons, while their dendrites, which form the majority of the surface area and receive most of the synaptic input, have been relatively neglected. This protocol describes techniques for recording from the dendrites of neurons in brain slices under direct visual control. Although the basic technique is similar to that used for somatic patching, we describe refinements and optimizations of slice quality, microscope optics, setup stability and electrode approach that are required for maximizing the success rate for dendritic recordings. Using this approach, all configurations of the patch-clamp technique (cell-attached, inside-out, whole-cell, outside-out and perforated patch) can be achieved, even for relatively distal dendrites, and simultaneous multiple-electrode dendritic recordings are also possible. The protocol--from the beginning of slice preparation to the end of the first successful recording--can be completed in 3 h.

}, keywords = {Dendrites, Patch-Clamp Techniques}, issn = {1750-2799}, doi = {10.1038/nprot.2006.164}, author = {Davie, Jenny T and Kole, Maarten H P and Letzkus, Johannes J and Rancz, Ede A and Spruston, Nelson and Stuart, Greg J and H{\"a}usser, Michael} }