@article {69460, title = {Dendritic excitations govern back-propagation via a spike-rate accelerometer}, journal = {Nature Communications}, volume = {16}, year = {2025}, month = {2025 Feb 04}, abstract = {

Dendrites on neurons support nonlinear electrical excitations, but the computational significance of these events is not well understood. We developed molecular, optical, and analytical tools to map sub-millisecond voltage dynamics throughout the dendritic trees of CA1 pyramidal neurons under diverse optogenetic and synaptic stimulus patterns, in acute brain slices. We observed history-dependent spike back-propagation in distal dendrites, driven by locally generated Na+ spikes (dSpikes). Dendritic depolarization created a transient window for dSpike propagation, opened by A-type KV channel inactivation, and closed by slow NaV inactivation. Collisions of dSpikes with synaptic inputs triggered calcium channel and N-methyl-D-aspartate receptor (NMDAR)-dependent plateau potentials, with accompanying complex spikes at the soma. This hierarchical ion channel network acts as a spike-rate accelerometer, providing an intuitive picture of how dendritic excitations shape associative plasticity rules.

}, doi = {10.1038/s41467-025-55819-9}, url = {https://www.biorxiv.org/content/early/2024/05/18/2023.06.02.543490}, author = {Park, Pojeong and Wong-Campos, David and Itkis, Daniel G. and Lee, Byung Hun and Qi, Yitong and Davis, Hunter and Antin, Benjamin and Pasarkar, Amol and Grimm, Jonathan B. and Plutkis, Sarah E. and Holland, Katie L. and Paninski, Liam and Lavis, Luke D. and Cohen, Adam E.} } @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 {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 {67211, title = {High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in~vivo.}, journal = {Cell Reports}, volume = {35}, year = {2021}, month = {2021 Apr 06}, pages = {108954}, abstract = {

The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio in\ vivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra- and subthreshold voltages in\ vivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 in\ vivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2021.108954}, author = {Xie, Michael E and Adam, Yoav and Fan, Linlin Z and B{\"o}hm, Urs L and Kinsella, Ian and Zhou, Ding and Rozsa, Marton and Singh, Amrita and Svoboda, Karel and Paninski, Liam and Cohen, Adam E} } @article {65321, title = {Rapid mesoscale volumetric imaging of neural activity with synaptic resolution.}, journal = {Nature Methods}, volume = {17}, year = {2020}, month = {2020 Mar 02}, pages = {291-4}, abstract = {

Imaging neurons and neural circuits over large volumes at high speed and subcellular resolution is a difficult task. Incorporating a Bessel focus module into a two-photon fluorescence mesoscope, we achieved rapid volumetric imaging of neural activity over the mesoscale with synaptic resolution. We applied the technology to calcium imaging of entire dendritic spans of neurons as well as neural ensembles within multiple cortical regions over two hemispheres of the awake mouse brain.

}, issn = {1548-7105}, doi = {10.1038/s41592-020-0760-9}, author = {Lu, Rongwen and Liang, Yajie and Meng, Guanghan and Zhou, Pengcheng and Svoboda, Karel and Paninski, Liam and Ji, Na} } @article {65081, title = {Bright and photostable chemigenetic indicators for extended in vivo voltage imaging.}, journal = {Science}, volume = {365}, year = {2019}, month = {2019 Aug 13}, pages = {699-704}, abstract = {

Imaging changes in membrane potential using genetically encoded fluorescent voltage indicators (GEVIs) has great potential for monitoring neuronal activity with high spatial and temporal resolution. Brightness and photostability of fluorescent proteins and rhodopsins have limited the utility of existing GEVIs. We engineered a novel GEVI, "Voltron", that utilizes bright and photostable synthetic dyes instead of protein-based fluorophores, extending the combined duration of imaging and number of neurons imaged simultaneously by more than tenfold relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously, over 15 min of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

}, issn = {1095-9203}, doi = {10.1126/science.aav6416}, author = {Abdelfattah, Ahmed S and Kawashima, Takashi and Singh, Amrita and Novak, Ondrej and Liu, Hui and Shuai, Yichun and Huang, Yi-Chieh and Campagnola, Luke and Seeman, Stephanie C and Yu, Jianing and Zheng, Jihong and Grimm, Jonathan B and Patel, Ronak and Friedrich, Johannes and Mensh, Brett D and Paninski, Liam and Macklin, John J and Murphy, Gabe J and Podgorski, Kaspar and Lin, Bei-Jung and Chen, Tsai-Wen and Turner, Glenn C and Liu, Zhe and Koyama, Minoru and Svoboda, Karel and Ahrens, Misha B and Lavis, Luke D and Schreiter, Eric R} } @article {67219, title = {Community-based benchmarking improves spike rate inference from two-photon calcium imaging data}, journal = {PLOS Computational Biology}, volume = {14}, year = {2018}, month = {Sep-05-2019}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the\ spikefinder\ challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, url = {https://journals.plos.org/ploscompbiol/article?rev=2\&id=10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nikolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H. and Turaga, Srinivas C. and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W. and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D. and Bolte, Ben and Machado, Timothy A. and Ringach, Dario and Stone, Jasmine and Rogerson, Luke E. and Sofroniew, Nicolas J. and Reimer, Jacob and Froudarakis, Emmanouil and Euler, Thomas and Rom{\'a}n Ros{\'o}n, Miroslav and Theis, Lucas and Tolias, Andreas S. and Bethge, Matthias}, editor = {Bush, Daniel} } @article {48836, title = {Community-based benchmarking improves spike inference from two-photon calcium imaging data.}, journal = {PLoS Computational Biology}, volume = {14}, year = {2017}, month = {2018 May 21}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nicolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H and Turaga, Srinivas C and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D and Bolte, Ben and Machado, Timothy A and Ringach, Dario and etal} } @article {48716, title = {Multi-scale approaches for high-speed imaging and analysis of large neural populations.}, journal = {PLoS Computational Biology}, volume = {13}, year = {2017}, month = {2017 Aug 03}, pages = {e1005685}, abstract = {

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to "zoom out" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1005685}, author = {Friedrich, Johannes and Yang, Weijian and Soudry, Daniel and Mu, Yu and Ahrens, Misha B and Yuste, Rafael and Peterka, Darcy S and Paninski, Liam} } @article {47262, title = {Simultaneous denoising, deconvolution, and demixing of calcium imaging data.}, journal = {Neuron}, volume = {89}, year = {2016}, month = {2016 Jan 20}, pages = {285-99}, abstract = {

We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in\ vitro and in\ vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2015.11.037}, author = {Pnevmatikakis, Eftychios A and Soudry, Daniel and Gao, Yuanjun and Machado, Timothy A and Merel, Josh and Pfau, David and Reardon, Thomas and Mu, Yu and Lacefield, Clay and Yang, Weijian and Ahrens, Misha and Bruno, Randy and Jessell, Thomas M and Peterka, Darcy S and Yuste, Rafael and Paninski, Liam} } @article {47143, title = {Mapping nonlinear receptive field structure in primate retina at single cone resolution.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Oct 30}, abstract = {

The function of a neural circuit is shaped by the computations performed by its interneurons, which in many cases are not easily accessible to experimental investigation. Here, we elucidate the transformation of visual signals flowing from the input to the output of the primate retina, using a combination of large-scale multi-electrode recordings from an identified ganglion cell type, visual stimulation targeted at individual cone photoreceptors, and a hierarchical computational model. The results reveal nonlinear subunits in the circuity of OFF midget ganglion cells, which subserve high-resolution vision. The model explains light responses to a variety of stimuli more accurately than a linear model, including stimuli targeted to cones within and across subunits. The recovered model components are consistent with known anatomical organization of midget bipolar interneurons. These results reveal the spatial structure of linear and nonlinear encoding, at the resolution of single cells and at the scale of complete circuits.

}, issn = {2050-084X}, doi = {10.7554/eLife.05241}, author = {Freeman, Jeremy and Field, Greg D and Li, Peter H and Greschner, Martin and Gunning, Deborah E and Mathieson, Keith and Sher, Alexander and Litke, Alan M and Paninski, Liam and Simoncelli, Eero P and Chichilnisky, E J} } @article {5673, title = {Inferring input nonlinearities in neural encoding models.}, journal = {Network}, volume = {19}, year = {2008}, month = {2008}, pages = {35-67}, abstract = {

We describe a class of models that predict how the instantaneous firing rate of a neuron depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of the stimulus, followed by a linear filter that acts on the sequence of transformed inputs. In one case, the nonlinear transform is the same at all filter lag-times. Thus, this "input nonlinearity" converts the initial numerical representation of stimulus value to a new representation that provides optimal input to the subsequent linear model. We describe algorithms that estimate both the input nonlinearity and the linear weights simultaneously; and present techniques to regularise and quantify uncertainty in the estimates. In a second approach, the model is generalized to allow a different nonlinear transform of the stimulus value at each lag-time. Although more general, this model is algorithmically more straightforward to fit. However, it has many more degrees of freedom than the first approach, thus requiring more data for accurate estimation. We test the feasibility of these methods on synthetic data, and on responses from a neuron in rodent barrel cortex. The models are shown to predict responses to novel data accurately, and to recover several important neuronal response properties.

}, keywords = {Models, Neural Networks (Computer), Neurological, Neurons, Nonlinear Dynamics}, issn = {1361-6536}, doi = {10.1080/09548980701813936}, author = {Ahrens, Misha B and Paninski, Liam and Sahani, Maneesh} } @article {5530, title = {Efficient estimation of detailed single-neuron models.}, journal = {Journal of Neurophysiology}, volume = {96}, year = {2006}, month = {2006 Aug}, pages = {872-90}, abstract = {

Biophysically accurate multicompartmental models of individual neurons have significantly advanced our understanding of the input-output function of single cells. These models depend on a large number of parameters that are difficult to estimate. In practice, they are often hand-tuned to match measured physiological behaviors, thus raising questions of identifiability and interpretability. We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. Recent experimental advances, notably in voltage-sensitive imaging, motivate us to assume access to: i) the spatiotemporal voltage signal in the dendrite and ii) an approximate description of the channel kinetics of interest. We show here that, given i and ii, parameters 1-3 can be inferred simultaneously by nonnegative linear regression; that this optimization problem possesses a unique solution and is guaranteed to converge despite the large number of parameters and their complex nonlinear interaction; and that standard optimization algorithms efficiently reach this optimum with modest computational and data requirements. We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10(4) parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.

}, keywords = {Algorithms, Biophysical Phenomena, Biophysics, Cell Membrane, Data Interpretation, Dendrites, Electrophysiology, Ion Channel Gating, Ion Channels, Kinetics, Ligands, Likelihood Functions, Models, Monte Carlo Method, N-Methyl-D-Aspartate, Neurological, Neurons, Patch-Clamp Techniques, Receptors, Statistical, Synapses}, issn = {0022-3077}, author = {Huys, Quentin J M and Ahrens, Misha B and Paninski, Liam} }