144 results on '"Paninski L"'
Search Results
2. Brain-wide representations of prior information in mouse decision-making
- Author
-
Findling, C., Hubert, F., International Brain Lab, Acerbi, L., Benson, B., Benson, J., Birman, D., Bonacchi, N., Carandini, M., Catarino, J., Chapuis, G., Churchland, A., Dan, Y., Dayan, P., https://orcid.org/0000-0003-3476-1839, DeWitt, E., Engel, T., Fabbri, M., Faulkner, M., Fiete, I., Freitas-Silva, L., Gercek, B., Harris, K., Häusser, M., Hofer, S., Hu, F., Huntenburg, J., https://orcid.org/0000-0003-0579-9811, Khanal, A., Krasniak, C., Langdon, C., Latham, P., Lau, P., Mainen, Z., Meijer, G., Miska, N., Mrsic-Flogel, T., Noel, J., Nylund, K., Pan-Vazquez, A., Paninski, L., Pillow, J., Rossant, C., Roth, N., Schaeffer, R., Schartner, M., Shi, Y., Socha, K., Steinmetz, N., Svoboda, K., Tessereau, C., https://orcid.org/0000-0002-0385-2802, Urai, A., Wells, M., West, S., Whiteway, M., Winter, O., Witten, I., Zador, A., and Pouget, A.
- Abstract
The neural representations of prior information about the state of the world are poorly understood. To investigate this issue, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACCd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.
- Published
- 2023
3. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools
- Author
-
Biderman, D., Whiteway, M., Hurwitz, C., Greenspan, N., Lee, R., Vishnubhotla, A., Warren, R., Pedraja, F., Noone, D., Schartner, M., Huntenburg, J., Khanal, A., Meijer, G., Noel, J., Pan-Vazquez, A., Socha, K., Urai, A., The International Brain Laboratory, Cunningham, J., Sawtell, N., and Paninski, L.
- Abstract
Pose estimation algorithms are shedding new light on animal behavior and intelligence. Most existing models are only trained with labeled frames (supervised learning). Although effective in many cases, the fully supervised approach requires extensive image labeling, struggles to generalize to new videos, and produces noisy outputs that hinder downstream analyses. We address each of these limitations with a semi-supervised approach that leverages the spatiotemporal statistics of unlabeled videos in two different ways. First, we introduce unsupervised training objectives that penalize the network whenever its predictions violate smoothness of physical motion, multiple-view geometry, or depart from a low-dimensional subspace of plausible body configurations. Second, we design a new network architecture that predicts pose for a given frame using temporal context from surrounding unlabeled frames. These context frames help resolve brief occlusions or ambiguities between nearby and similar-looking body parts. The resulting pose estimation networks achieve better performance with fewer labels, generalize better to unseen videos, and provide smoother and more reliable pose trajectories for downstream analysis; for example, these improved pose trajectories exhibit stronger correlations with neural activity. We also propose a Bayesian post-processing approach based on deep ensembling and Kalman smoothing that further improves tracking accuracy and robustness. We release a deep learning package that adheres to industry best practices, supporting easy model development and accelerated training and prediction. Our package is accompanied by a cloud application that allows users to annotate data, train networks, and predict new videos at scale, directly from the browser.
- Published
- 2023
4. Reproducibility of in-vivo electrophysiological measurements in mice
- Author
-
International Brain Laboratory, Banga, K., Benson, J., Bonacchi, N., Bruijns, S., Campbell, R., Chapuis, G., Churchland, A., Davatolhagh, M., Lee, H., Faulkner, M., Hu, F., Huntenburg, J., Khanal, A., Krasniak, C., Meijer, G., Miska, N., Mohammadi, Z., Noel, J., Paninski, L., Pan-Vazquez, A., Roth, N., Schartner, M., Socha, K., Steinmetz, N., Taheri, M., Urai, A., Wells, M., West, S., Whiteway, M., and Winter, O.
- Abstract
Understanding whole-brain-scale electrophysiological recordings will rely on the collective work of multiple labs. Because two labs recording from the same brain area often reach different conclusions, it is critical to quantify and control for features that decrease reproducibility. To address these issues, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. We repeatedly inserted Neuropixels multi-electrode probes targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus) in mice performing the behavioral task. We gathered data across 9 labs and developed a common histological and data processing pipeline to analyze the resulting large datasets. After applying stringent behavioral, histological, and electrophysiological quality-control criteria, we found that neuronal yield, firing rates, spike amplitudes, and task-modulated neuronal activity were reproducible across laboratories. To quantify variance in neural activity explained by task variables (e.g., stimulus onset time), behavioral variables (timing of licks/paw movements), and other variables (e.g., spatial location in the brain or the lab ID), we developed a multi-task neural network encoding model that extends common, simpler regression approaches by allowing nonlinear interactions between variables. We found that within-lab random effects captured by this model were comparable to between-lab random effects. Taken together, these results demonstrate that across-lab standardization of electrophysiological procedures can lead to reproducible results across labs. Moreover, our protocols to achieve reproducibility, along with our analyses to evaluate it are openly accessible to the scientific community, along with our extensive electrophysiological dataset with corresponding behavior and open-source analysis code.
- Published
- 2022
5. Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
- Author
-
Linderman, S, Mena, G, Cooper, H, Paninski, L, and Cunningham, J
- Subjects
FOS: Computer and information sciences ,Statistics - Machine Learning ,MathematicsofComputing_NUMERICALANALYSIS ,Machine Learning (stat.ML) ,MathematicsofComputing_DISCRETEMATHEMATICS - Abstract
Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optimization algorithms may enable efficient point estimation, but fully Bayesian inference poses a severe challenge in this high-dimensional, discrete space. To surmount this challenge, we start with the usual step of relaxing a discrete set (here, of permutation matrices) to its convex hull, which here is the Birkhoff polytope: the set of all doubly-stochastic matrices. We then introduce two novel transformations: first, an invertible and differentiable stick-breaking procedure that maps unconstrained space to the Birkhoff polytope; second, a map that rounds points toward the vertices of the polytope. Both transformations include a temperature parameter that, in the limit, concentrates the densities on permutation matrices. We then exploit these transformations and reparameterization gradients to introduce variational inference over permutation matrices, and we demonstrate its utility in a series of experiments.
- Published
- 2017
- Full Text
- View/download PDF
6. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience
- Author
-
.Paninski, L, primary and Cunningham, JP, additional
- Published
- 2018
- Full Text
- View/download PDF
7. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience
- Author
-
Paninski, L, primary and Cunningham, J.P, additional
- Published
- 2017
- Full Text
- View/download PDF
8. Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
- Author
-
Ari Pakman and Paninski, L.
- Subjects
FOS: Computer and information sciences ,Statistical Mechanics (cond-mat.stat-mech) ,Statistics::Methodology ,FOS: Physical sciences ,Statistics - Computation ,Condensed Matter - Statistical Mechanics ,Computation (stat.CO) ,Statistics::Computation - Abstract
We present a new approach to sample from generic binary distributions, based on an exact Hamiltonian Monte Carlo algorithm applied to a piecewise continuous augmentation of the binary distribution of interest. An extension of this idea to distributions over mixtures of binary and possibly-truncated Gaussian or exponential variables allows us to sample from posteriors of linear and probit regression models with spike-and-slab priors and truncated parameters. We illustrate the advantages of these algorithms in several examples in which they outperform the Metropolis or Gibbs samplers., Comment: 11 pages, 4 figures. Proceedings of the 27th Annual Conference Neural Information Processing Systems (NIPS), 2013
- Published
- 2013
- Full Text
- View/download PDF
9. Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings
- Author
-
Sadeghi, K., primary, Gauthier, J.L., additional, Field, G.D., additional, Greschner, M., additional, Agne, M., additional, Chichilnisky, E.J., additional, and Paninski, L., additional
- Published
- 2012
- Full Text
- View/download PDF
10. Efficient Coding of Spatial Information in the Primate Retina
- Author
-
Doi, E., primary, Gauthier, J. L., additional, Field, G. D., additional, Shlens, J., additional, Sher, A., additional, Greschner, M., additional, Machado, T. A., additional, Jepson, L. H., additional, Mathieson, K., additional, Gunning, D. E., additional, Litke, A. M., additional, Paninski, L., additional, Chichilnisky, E. J., additional, and Simoncelli, E. P., additional
- Published
- 2012
- Full Text
- View/download PDF
11. Decoding arm and hand movements across layers of the macaque frontal cortices
- Author
-
Wong, Y. T., primary, Vigeral, M., additional, Putrino, D., additional, Pfau, D., additional, Merel, J., additional, Paninski, L., additional, and Pesaran, B., additional
- Published
- 2012
- Full Text
- View/download PDF
12. EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter
- Author
-
Nazarpour, K., primary, Ethier, C., additional, Paninski, L., additional, Rebesco, J. M., additional, Miall, R. C., additional, and Miller, L. E., additional
- Published
- 2012
- Full Text
- View/download PDF
13. Temporal Precision in the Visual Pathway through the Interplay of Excitation and Stimulus-Driven Suppression
- Author
-
Butts, D. A., primary, Weng, C., additional, Jin, J., additional, Alonso, J.-M., additional, and Paninski, L., additional
- Published
- 2011
- Full Text
- View/download PDF
14. Incorporating Naturalistic Correlation Structure Improves Spectrogram Reconstruction from Neuronal Activity in the Songbird Auditory Midbrain
- Author
-
Ramirez, A. D., primary, Ahmadian, Y., additional, Schumacher, J., additional, Schneider, D., additional, Woolley, S. M. N., additional, and Paninski, L., additional
- Published
- 2011
- Full Text
- View/download PDF
15. A generalized linear model of the impact of direct and indirect inputs to the lateral geniculate nucleus
- Author
-
Babadi, B., primary, Casti, A., additional, Xiao, Y., additional, Kaplan, E., additional, and Paninski, L., additional
- Published
- 2010
- Full Text
- View/download PDF
16. Toward characterizion of the complete visual signal in a patch of retina
- Author
-
Simoncelli, E. P., primary, Pillow, J. W., additional, Shlens, J., additional, Paninski, L., additional, and Chichilnisky, E. J., additional
- Published
- 2010
- Full Text
- View/download PDF
17. Bayesian Image Recovery for Dendritic Structures Under Low Signal-to-Noise Conditions
- Author
-
Fudenberg, G., primary and Paninski, L., additional
- Published
- 2009
- Full Text
- View/download PDF
18. Estimating Entropy on<tex>$m$</tex>Bins Given Fewer Than<tex>$m$</tex>Samples
- Author
-
Paninski, L., primary
- Published
- 2004
- Full Text
- View/download PDF
19. Efficient model-based design of neurophysiological experiments.
- Author
-
Lewi, J., Butera, R., and Paninski, L.
- Published
- 2006
- Full Text
- View/download PDF
20. Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings.
- Author
-
Sadeghi, K., Gauthier, J.L., Field, G.D., Greschner, M., Agne, M., Chichilnisky, E.J., and Paninski, L.
- Abstract
It has recently become possible to identify cone photoreceptors in primate retina from multi-electrode recordings of ganglion cell spiking driven by visual stimuli of sufficiently high spatial resolution. In this paper we present a statistical approach to the problem of identifying the number, locations, and color types of the cones observed in this type of experiment. We develop an adaptive Markov Chain Monte Carlo (MCMC) method that explores the space of cone configurations, using a Linear-Nonlinear-Poisson (LNP) encoding model of ganglion cell spiking output, while analytically integrating out the functional weights between cones and ganglion cells. This method provides information about our posterior certainty about the inferred cone properties, and additionally leads to improvements in both the speed and quality of the inferred cone maps, compared to earlier 'greedy' computational approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
21. State-Space Decoding of Goal-Directed Movements.
- Author
-
Kulkarni, J.E. and Paninski, L.
- Abstract
This article reviews an earlier recursive approach for computing such reach trajectories and presents a new nonrecursive approach, with computations that may be performed analytically for the most part, leading to a significant gain in the accuracy of the inferred trajectory while imposing a very small computational burden. Extensions of the approach are discussed including the incorporation of multiple target observations at different times, and multiple possible target locations. [ABSTRACT FROM PUBLISHER]
- Published
- 2008
- Full Text
- View/download PDF
22. Stochastic optimal control and the human oculomotor system
- Author
-
Paninski, L. and Hawken, M. J.
- Published
- 2001
- Full Text
- View/download PDF
23. Partition functions from rao-blackwellized tempered sampling
- Author
-
Carlson, D. E., Stinson, P., Ari Pakman, and Paninski, L.
- Subjects
FOS: Computer and information sciences ,Statistics - Machine Learning ,Machine Learning (stat.ML) - Abstract
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex and multimodal distributions. Such distributions are often sampled using simulated tempering, which augments the target space with an auxiliary inverse temperature variable. Our method exploits the multinomial probability law of the inverse temperatures, and provides estimates of the partition function in terms of a simple quotient of Rao-Blackwellized marginal inverse temperature probability estimates, which are updated while sampling. We show that the method has interesting connections with several alternative popular methods, and offers some significant advantages. In particular, we empirically find that the new method provides more accurate estimates than Annealed Importance Sampling when calculating partition functions of large Restricted Boltzmann Machines (RBM); moreover, the method is sufficiently accurate to track training and validation log-likelihoods during learning of RBMs, at minimal computational cost., Comment: 15 pages, 8 figures; Appearing at International Conference on Machine Learning 2016
24. BehaveNet: Nonlinear embedding and Bayesian neural decoding of behavioral videos
- Author
-
Batty, E., Whiteway, M. R., Saxena, S., Biderman, D., Abe, T., Musall, S., Winthrop Gillis, Markowitz, J. E., Churchland, A., Cunningham, J., Datta, S. R., Linderman, S. W., and Paninski, L.
25. Stochastic bouncy particle sampler
- Author
-
Pakman, A., Gilboa, D., David Carlson, and Paninski, L.
- Subjects
FOS: Computer and information sciences ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Statistics - Computation ,Computation (stat.CO) - Abstract
We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear. The algorithm is based on simulating first arrival times in a doubly stochastic Poisson process using the thinning method, and allows efficient sampling of Bayesian posteriors in big datasets. We prove that in the BPS no bias is introduced by noisy evaluations of the log-likelihood gradient. On the other hand, we argue that efficiency considerations favor a small, controllable bias in the construction of the thinning proposals, in exchange for faster mixing. We introduce a simple regression-based proposal intensity for the thinning method that controls this trade-off. We illustrate the algorithm in several examples in which it outperforms both unbiased, but slowly mixing stochastic versions of BPS, as well as biased stochastic gradient-based samplers., Comment: ICML Camera ready version
26. A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms.
- Author
-
Blau A, Schaffer ES, Mishra N, Miska NJ, Paninski L, and Whiteway MR
- Abstract
Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms - which include tree-based models, deep neural networks, and graphical models - differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species - fly, mouse, and human - we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.
- Published
- 2024
27. Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution.
- Author
-
Zhang Y, Wang Y, Benetó DJ, Wang Z, Azabou M, Richards B, Winter O, Dyer E, Paninski L, and Hurwitz C
- Abstract
Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas. We introduce a novel self-supervised modeling approach for population activity in which the model alternates between masking out and reconstructing neural activity across different time steps, neurons, and brain regions. To evaluate our approach, we design unsupervised and supervised prediction tasks using the International Brain Laboratory repeated site dataset, which is comprised of Neuropixels recordings targeting the same brain locations across 48 animals and experimental sessions. The prediction tasks include single-neuron and region-level activity prediction, forward prediction, and behavior decoding. We demonstrate that our multi-task-masking (MtM) approach significantly improves the performance of current state-of-the-art population models and enables multi-task learning. We also show that by training on multiple animals, we can improve the generalization ability of the model to unseen animals, paving the way for a foundation model of the brain at single-cell, single-spike resolution. Project page and code: https://ibl-mtm.github.io/.
- Published
- 2024
28. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.
- Author
-
Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner MM, Huntenburg JM, Khanal A, Meijer GT, Noel JP, Pan-Vazquez A, Socha KZ, Urai AE, Cunningham JP, Sawtell NB, and Paninski L
- Subjects
- Animals, Supervised Machine Learning, Cloud Computing, Software, Posture physiology, Deep Learning, Image Processing, Computer-Assisted methods, Behavior, Animal, Bayes Theorem, Video Recording methods, Algorithms
- Abstract
Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce 'Lightning Pose', an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We released a cloud application that allows users to label data, train networks and process new videos directly from the browser., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
- Published
- 2024
- Full Text
- View/download PDF
29. Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration.
- Author
-
Ryu J, Nejatbakhsh A, Torkashvand M, Gangadharan S, Seyedolmohadesin M, Kim J, Paninski L, and Venkatachalam V
- Subjects
- Animals, Mice, Caenorhabditis elegans physiology, Imaging, Three-Dimensional methods, Image Processing, Computer-Assisted methods, Algorithms, Deep Learning, Computational Biology methods
- Abstract
Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed for specific datasets and hence do not generalize well to other datasets, or require large amounts of training data that are not readily available. This is further exacerbated when tracking fluorescent sources in moving and deforming tissues, where the lack of unique features and sparsely populated images create a challenging environment, especially for modern deep learning techniques. By leveraging technology recently developed for spatial transformer networks, we propose ZephIR, an image registration framework for semi-supervised MOT in 2D and 3D videos. ZephIR can generalize to a wide range of biological systems by incorporating adjustable parameters that encode spatial (sparsity, texture, rigidity) and temporal priors of a given data class. We demonstrate the accuracy and versatility of our approach in a variety of applications, including tracking the body parts of a behaving mouse and neurons in the brain of a freely moving C. elegans. We provide an open-source package along with a web-based graphical user interface that allows users to provide small numbers of annotations to interactively improve tracking results., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ryu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Published
- 2024
- Full Text
- View/download PDF
30. Dendritic excitations govern back-propagation via a spike-rate accelerometer.
- Author
-
Park P, Wong-Campos D, Itkis DG, Lee BH, Qi Y, Davis H, Antin B, Pasarkar A, Grimm JB, Plutkis SE, Holland KL, Paninski L, Lavis LD, and Cohen AE
- 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 K V channel inactivation, and closed by slow N a V 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., Competing Interests: Competing interests The authors declare no competing interests.- Published
- 2024
- Full Text
- View/download PDF
31. Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization.
- Author
-
Antin B, Sadahiro M, Gajowa M, Triplett MA, Adesnik H, and Paninski L
- Subjects
- Animals, Synapses physiology, Mice, Neurons physiology, Software, Computer Simulation, Algorithms, Patch-Clamp Techniques methods, Humans, Optogenetics methods, Artifacts, Models, Neurological, Computational Biology methods
- Abstract
Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical excitation of nearby cells can induce direct photocurrents in the postsynaptic cell. These photocurrent artifacts contaminate synaptic currents, making it difficult or impossible to probe connectivity for nearby cells. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are less variable than those of synaptic currents. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available as open source software., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Antin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Published
- 2024
- Full Text
- View/download PDF
32. Coordination and persistence of aggressive visual communication in Siamese fighting fish.
- Author
-
Everett CP, Norovich AL, Burke JE, Whiteway MR, Shih PY, Zhu Y, Paninski L, and Bendesky A
- Abstract
Animals coordinate their behavior with each other during both cooperative and agonistic social interactions. Such coordination often adopts the form of "turn taking", in which the interactive partners alternate the performance of a behavior. Apart from acoustic communication, how turn taking between animals is coordinated is not well understood. Furthermore, the neural substrates that regulate persistence in engaging in social interactions are poorly studied. Here, we use Siamese fighting fish ( Betta splendens ), to study visually-driven turn-taking aggressive behavior. Using encounters with conspecifics and with animations, we characterize the dynamic visual features of an opponent and the behavioral sequences that drive turn taking. Through a brain-wide screen of neuronal activity during coordinated and persistent aggressive behavior, followed by targeted brain lesions, we find that the caudal portion of the dorsomedial telencephalon, an amygdala-like region, promotes persistent participation in aggressive interactions, yet is not necessary for coordination. Our work highlights how dynamic visual cues shape the rhythm of social interactions at multiple timescales, and points to the pallial amygdala as a region controlling engagement in such interactions. These results suggest an evolutionarily conserved role of the vertebrate pallial amygdala in regulating the persistence of emotional states.
- Published
- 2024
- Full Text
- View/download PDF
33. Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings.
- Author
-
Ye Z, Shelton AM, Shaker JR, Boussard J, Colonell J, Birman D, Manavi S, Chen S, Windolf C, Hurwitz C, Namima T, Pedraja F, Weiss S, Raducanu B, Ness TV, Jia X, Mastroberardino G, Rossi LF, Carandini M, Häusser M, Einevoll GT, Laurent G, Sawtell NB, Bair W, Pasupathy A, Lopez CM, Dutta B, Paninski L, Siegle JH, Koch C, Olsen SR, Harris TD, and Steinmetz NA
- 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 ). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior., Competing Interests: Declaration of interests CK holds an executive position, and has a financial interest, in Intrinsic Powers, Inc., a company whose purpose is to develop a device that can be used in the clinic to assess the presence and absence of consciousness in patients. This does not pose any conflict of interest with regard to the work undertaken for this publication. BR, CML, and BD are employees of IMEC vzw, a nonprofit research institute that manufactures, sells, and distributes the Neuropixels probes, at cost, to the research community. IMEC vzw holds patents US10811542B2, US10044325B2, and US9384990B2 related to the Neuropixels 1.0 technology that is built upon in this work. All other authors have no competing interests.
- Published
- 2024
- Full Text
- View/download PDF
34. Identifying Interpretable Latent Factors with Sparse Component Analysis.
- Author
-
Zimnik AJ, Cora Ames K, An X, Driscoll L, Lara AH, Russo AA, Susoy V, Cunningham JP, Paninski L, Churchland MM, and Glaser JI
- Abstract
In many neural populations, the computationally relevant signals are posited to be a set of 'latent factors' - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans , and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations., Competing Interests: Declaration of Interests The authors declare no competing interests.
- Published
- 2024
- Full Text
- View/download PDF
35. DREDge: robust motion correction for high-density extracellular recordings across species.
- Author
-
Windolf C, Yu H, Paulk AC, Meszéna D, Muñoz W, Boussard J, Hardstone R, Caprara I, Jamali M, Kfir Y, Xu D, Chung JE, Sellers KK, Ye Z, Shaker J, Lebedeva A, Raghavan M, Trautmann E, Melin M, Couto J, Garcia S, Coughlin B, Horváth C, Fiáth R, Ulbert I, Movshon JA, Shadlen MN, Churchland MM, Churchland AK, Steinmetz NA, Chang EF, Schweitzer JS, Williams ZM, Cash SS, Paninski L, and Varol E
- Abstract
High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.
- Published
- 2023
- Full Text
- View/download PDF
36. Bayesian target optimisation for high-precision holographic optogenetics.
- Author
-
Triplett MA, Gajowa M, Adesnik H, and Paninski L
- Abstract
Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision.
- Published
- 2023
- Full Text
- View/download PDF
37. Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.
- Author
-
Zhang Y, He T, Boussard J, Windolf C, Winter O, Trautmann E, Roth N, Barrell H, Churchland M, Steinmetz NA, Varol E, Hurwitz C, and Paninski L
- Abstract
Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting , the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.
- Published
- 2023
- Full Text
- View/download PDF
38. maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data.
- Author
-
Pasarkar A, Kinsella I, Zhou P, Wu M, Pan D, Fan JL, Wang Z, Abdeladim L, Peterka DS, Adesnik H, Ji N, and Paninski L
- Abstract
A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells' shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.
- Published
- 2023
- Full Text
- View/download PDF
39. The spatial and temporal structure of neural activity across the fly brain.
- Author
-
Schaffer ES, Mishra N, Whiteway MR, Li W, Vancura MB, Freedman J, Patel KB, Voleti V, Paninski L, Hillman EMC, Abbott LF, and Axel R
- Subjects
- Animals, Drosophila, Grooming, Knowledge, Brain, Neurons
- Abstract
What are the spatial and temporal scales of brainwide neuronal activity? We used swept, confocally-aligned planar excitation (SCAPE) microscopy to image all cells in a large volume of the brain of adult Drosophila with high spatiotemporal resolution while flies engaged in a variety of spontaneous behaviors. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons correlated (or anticorrelated) with running and flailing over timescales that ranged from seconds to a minute. Grooming elicited a weaker global response. Significant residual activity not directly correlated with behavior was high dimensional and reflected the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate., (© 2023. Springer Nature Limited.)
- Published
- 2023
- Full Text
- View/download PDF
40. Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks.
- Author
-
Nejatbakhsh A, Dey N, Venkatachalam V, Yemini E, Paninski L, and Varol E
- Abstract
Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans , and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.
- Published
- 2023
- Full Text
- View/download PDF
41. MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM.
- Author
-
Chen S, Rao BY, Herrlinger S, Losonczy A, Paninski L, and Varol E
- Abstract
Multimodal microscopy experiments that image the same population of cells under different experimental conditions have become a widely used approach in systems and molecular neuroscience. The main obstacle is to align the different imaging modalities to obtain complementary information about the observed cell population (e.g., gene expression and calcium signal). Traditional image registration methods perform poorly when only a small subset of cells are present in both images, as is common in multimodal experiments. We cast multimodal microscopy alignment as a cell subset matching problem. To solve this non-convex problem, we introduce an efficient and globally optimal branch-and-bound algorithm to find subsets of point clouds that are in rotational alignment with each other. In addition, we use complementary information about cell shape and location to compute the matching likelihood of cell pairs in two imaging modalities to further prune the optimization search tree. Finally, we use the maximal set of cells in rigid rotational alignment to seed image deformation fields to obtain a final registration result. Our framework performs better than the state-of-the-art histology alignment approaches regarding matching quality and is faster than manual alignment, providing a viable solution to improve the throughput of multimodal microscopy experiments.
- Published
- 2023
- Full Text
- View/download PDF
42. ROBUST ONLINE MULTIBAND DRIFT ESTIMATION IN ELECTROPHYSIOLOGY DATA.
- Author
-
Windolf C, Paulk AC, Kfir Y, Trautmann E, Meszéna D, Muñoz W, Caprara I, Jamali M, Boussard J, Williams ZM, Cash SS, Paninski L, and Varol E
- Abstract
High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion poses a challenge for downstream analyses, particularly in human recordings. We improve on the state of the art for tracking this motion with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) in addition to spikes. Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, enabling the method to scale up to longer and higher-resolution recordings, and possibly facilitating real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and simple methods for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from human and mouse.
- Published
- 2023
- Full Text
- View/download PDF
43. Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis.
- Author
-
Abe T, Kinsella I, Saxena S, Buchanan EK, Couto J, Briggs J, Kitt SL, Glassman R, Zhou J, Paninski L, and Cunningham JP
- Subjects
- Cloud Computing, Reproducibility of Results, Software, Data Analysis, Neurosciences
- Abstract
A key aspect of neuroscience research is the development of powerful, general-purpose data analyses that process large datasets. Unfortunately, modern data analyses have a hidden dependence upon complex computing infrastructure (e.g., software and hardware), which acts as an unaddressed deterrent to analysis users. Although existing analyses are increasingly shared as open-source software, the infrastructure and knowledge needed to deploy these analyses efficiently still pose significant barriers to use. In this work, we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated open-source analysis platform offering automatic infrastructure reproducibility for any data analysis. We show how NeuroCAAS supports the design of simpler, more powerful data analyses and that many popular data analysis tools offered through NeuroCAAS outperform counterparts on typical infrastructure. Pairing rigorous infrastructure management with cloud resources, NeuroCAAS dramatically accelerates the dissemination and use of new data analyses for neuroscientific discovery., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)
- Published
- 2022
- Full Text
- View/download PDF
44. Blind demixing methods for recovering dense neuronal morphology from barcode imaging data.
- Author
-
Chen S, Loper J, Zhou P, and Paninski L
- Subjects
- Neurons, DNA Barcoding, Taxonomic methods, Optical Imaging
- Abstract
Cellular barcoding methods offer the exciting possibility of 'infinite-pseudocolor' anatomical reconstruction-i.e., assigning each neuron its own random unique barcoded 'pseudocolor,' and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron, and validate this method on real data with known barcodes. We also develop a neural network which uses the recovered barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, 'connecting the dots' between discontiguous barcode amplicon signals. We find that accurate recovery should be feasible, provided that the barcode signal density is sufficiently high. This study suggests the possibility of mapping the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2022
- Full Text
- View/download PDF
45. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity.
- Author
-
Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, and Seung HS
- Subjects
- Animals, Mice, Microscopy, Electron, Organelles, Pyramidal Cells physiology, Synapses physiology, Neocortex physiology
- Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm
3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis., Competing Interests: Declaration of interests T.M. and H.S.S. disclose financial interests in Zetta Ai LLC. J.R. and A.S.T. disclose financial interests in Vathes LLC., (Copyright © 2022 Elsevier Inc. All rights reserved.)- Published
- 2022
- Full Text
- View/download PDF
46. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.
- Author
-
Whiteway MR, Biderman D, Friedman Y, Dipoppa M, Buchanan EK, Wu A, Zhou J, Bonacchi N, Miska NJ, Noel JP, Rodriguez E, Schartner M, Socha K, Urai AE, Salzman CD, Cunningham JP, and Paninski L
- Subjects
- Animals, Computational Biology, Computer Simulation, Markov Chains, Mice, Models, Statistical, Neural Networks, Computer, Supervised Machine Learning statistics & numerical data, Unsupervised Machine Learning statistics & numerical data, Algorithms, Artificial Intelligence statistics & numerical data, Behavior, Animal, Video Recording statistics & numerical data
- Abstract
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2021
- Full Text
- View/download PDF
47. Visualizing the organization and differentiation of the male-specific nervous system of C. elegans.
- Author
-
Tekieli T, Yemini E, Nejatbakhsh A, Wang C, Varol E, Fernandez RW, Masoudi N, Paninski L, and Hobert O
- Subjects
- Animals, Brain physiology, Caenorhabditis elegans genetics, Cell Differentiation genetics, Gene Expression Regulation, Developmental genetics, Genes, Reporter genetics, Male, Neurogenesis genetics, Neurons physiology, Transgenes genetics, Caenorhabditis elegans physiology, Cell Differentiation physiology, Nervous System physiopathology
- Abstract
Sex differences in the brain are prevalent throughout the animal kingdom and particularly well appreciated in the nematode Caenorhabditis elegans, where male animals contain a little-studied set of 93 male-specific neurons. To make these neurons amenable for future study, we describe here how a multicolor reporter transgene, NeuroPAL, is capable of visualizing the distinct identities of all male-specific neurons. We used NeuroPAL to visualize and characterize a number of features of the male-specific nervous system. We provide several proofs of concept for using NeuroPAL to identify the sites of expression of gfp-tagged reporter genes and for cellular fate analysis by analyzing the effect of removal of several developmental patterning genes on neuronal identity acquisition. We use NeuroPAL and its intrinsic cohort of more than 40 distinct differentiation markers to show that, even though male-specific neurons are generated throughout all four larval stages, they execute their terminal differentiation program in a coordinated manner in the fourth larval stage. This coordinated wave of differentiation, which we call 'just-in-time' differentiation, couples neuronal maturation programs with the appearance of sexual organs., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2021. Published by The Company of Biologists Ltd.)
- Published
- 2021
- Full Text
- View/download PDF
48. Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images.
- Author
-
Rao BY, Peterson AM, Kandror EK, Herrlinger S, Losonczy A, Paninski L, Rizvi AH, and Varol E
- Abstract
Spatial transcriptomics techniques such as STARmap [15] enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st.
- Published
- 2021
- Full Text
- View/download PDF
49. Chronic, cortex-wide imaging of specific cell populations during behavior.
- Author
-
Couto J, Musall S, Sun XR, Khanal A, Gluf S, Saxena S, Kinsella I, Abe T, Cunningham JP, Paninski L, and Churchland AK
- Subjects
- Animals, Artifacts, Hemodynamics physiology, Mice, Transgenic, Skull surgery, Behavior, Animal physiology, Cerebral Cortex cytology, Cerebral Cortex diagnostic imaging, Imaging, Three-Dimensional methods
- Abstract
Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes such as attention, decision-making and action selection. However, techniques that allow cellular resolution measurements are expensive and require a high degree of technical expertise, which limits their broad use. Wide-field imaging of genetically encoded indicators is a high-throughput, cost-effective and flexible approach to measure activity of specific cell populations with high temporal resolution and a cortex-wide field of view. Here we outline our protocol for assembling a wide-field macroscope setup, performing surgery to prepare the intact skull and imaging neural activity chronically in behaving, transgenic mice. Further, we highlight a processing pipeline that leverages novel, cloud-based methods to analyze large-scale imaging datasets. The protocol targets laboratories that are seeking to build macroscopes, optimize surgical procedures for long-term chronic imaging and/or analyze cortex-wide neuronal recordings. The entire protocol, including steps for assembly and calibration of the macroscope, surgical preparation, imaging and data analysis, requires a total of 8 h. It is designed to be accessible to laboratories with limited expertise in imaging methods or interest in high-throughput imaging during behavior.
- Published
- 2021
- Full Text
- View/download PDF
50. Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings.
- Author
-
Kim YJ, Brackbill N, Batty E, Lee J, Mitelut C, Tong W, Chichilnisky EJ, and Paninski L
- Subjects
- Animals, Macaca, Neural Networks, Computer, Retina, Brain-Computer Interfaces, Retinal Ganglion Cells
- Abstract
Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons., (© 2021 Massachusetts Institute of Technology.)
- Published
- 2021
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.