scholarly journals On the use of calcium deconvolution algorithms in practical contexts

2019 ◽  
Author(s):  
Mathew H. Evans ◽  
Rasmus S. Petersen ◽  
Mark D. Humphries
Keyword(s):  
Time Series ◽  
Calcium Imaging ◽  
Barrel Cortex ◽  
Ground Truth ◽  
Neural Population ◽  
Imaging Data ◽  
Population Activity ◽  
Ground Truth Data ◽  
Electrophysiological Recordings ◽  
Order Of Magnitude

AbstractCalcium imaging is a powerful tool for capturing the simultaneous activity of large populations of neurons. Here we determine the extent to which our inferences of neural population activity, correlations, and coding depend on our choice of whether and how we deconvolve the calcium time-series into spike-driven events. To this end, we use a range of deconvolution algorithms to create nine versions of the same calcium imaging data obtained from barrel cortex during a pole-detection task. Seeking suitable values for the deconvolution algorithms’ parameters, we optimise them against ground-truth data, and find those parameters both vary by up to two orders of magnitude between neurons and are sensitive to small changes in their values. Applied to the barrel cortex data, we show that a substantial fraction of the processing methods fail to recover simple features of population activity in barrel cortex already established by electrophysiological recordings. Raw calcium time-series contain an order of magnitude more neurons tuned to features of the pole task; yet there is also qualitative disagreement between deconvolution methods on which neurons are tuned to the task. Finally, we show that raw and processed calcium time-series qualitatively disagree on the structure of correlations within the population and the dimensionality of its joint activity. Collectively, our results show that properties of neural activity, correlations, and coding inferred from calcium imaging are sensitive to the choice of if and how spike-evoked events are recovered. We suggest that quantitative results obtained from population calcium-imaging be verified across multiple processed forms of the calcium time-series.

scholarly journals A deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data

2020 ◽  
Author(s):  
Peter Rupprecht ◽  
Stefano Carta ◽  
Adrian Hoffmann ◽  
Mayumi Echizen ◽  
Kazuo Kitamura ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Action Potentials ◽  
Sampling Rate ◽  
Ground Truth ◽  
Cell Types ◽  
Imaging Data ◽  
Calcium Signals ◽  
Ground Truth Data ◽  
Unseen Data ◽  
User Friendly

ABSTRACTCalcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials (‘spikes’) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >20 hours from 225 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

Olfactory modulation of barrel cortex activity during active whisking and passive whisker stimulation

2021 ◽  
Author(s):  
Anthony Renard ◽  
Evan Harrell ◽  
Brice Bathallier
Keyword(s):  
Facial Nerve ◽  
Calcium Imaging ◽  
Tactile Stimulation ◽  
Barrel Cortex ◽  
Neuronal Responses ◽  
Population Activity ◽  
Tactile Information ◽  
Large Populations ◽  
Whisker Stimulation ◽  
The Impact

Abstract Rodents depend on olfaction and touch to meet many of their fundamental needs. The joint significance of these sensory systems is underscored by an intricate coupling between sniffing and whisking. However, the impact of simultaneous olfactory and tactile inputs on sensory representations in the cortex remains elusive. To study these interactions, we recorded large populations of barrel cortex neurons using 2-photon calcium imaging in head-fixed mice during olfactory and tactile stimulation. We find that odors alter barrel cortex activity in at least two ways, first by enhancing whisking, and second by central cross-talk that persists after whisking is abolished by facial nerve sectioning. Odors can either enhance or suppress barrel cortex neuronal responses, and while odor identity can be decoded from population activity, it does not interfere with the tactile representation. Thus, barrel cortex represents olfactory information which, in the absence of learned associations, is coded independently of tactile information.

scholarly journals CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging

2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
P. Jesper Sjöström ◽  
Simon R. Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Spike Train ◽  
Success Rate ◽  
Calcium Imaging ◽  
Ground Truth ◽  
Spike Trains ◽  
Imaging Data ◽  
Temporal Precision ◽  
Pulse Trains ◽  
Two Photon ◽  
Average Size

AbstractIn recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.

scholarly journals Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction

2017 ◽  
Author(s):  
James J. Jun ◽  
Catalin Mitelut ◽  
Chongxi Lai ◽  
Sergey L. Gratiy ◽  
Costas A. Anastassiou ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Neural Population ◽  
Spike Sorting ◽  
Population Activity ◽  
Density Peak ◽  
Precise Control ◽  
Peak Finding ◽  
Single Spike ◽  
Loop Feedback

AbstractElectrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating covariance-based features and unsupervised clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes up to 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.

scholarly journals COMBINING AIRBORNE OBLIQUE CAMERA AND LIDAR SENSORS: INVESTIGATION AND NEW PERSPECTIVES

2018 ◽  
Vol XLII-1 ◽  
pp. 437-444 ◽  
Author(s):  
I. Toschi ◽  
F. Remondino ◽  
R. Rothe ◽  
K. Klimek
Keyword(s):  
Ground Truth ◽  
Point Clouds ◽  
Image Sensors ◽  
Lidar Data ◽  
Imaging Data ◽  
Ground Truth Data ◽  
Dense Image ◽  
Integrated Processing ◽  
Urban Mapping ◽  
Oblique Images

<p><strong>Abstract.</strong> Hybrid sensor solutions, that feature active laser and passive image sensors on the same platform, are rapidly entering the airborne market of topographic and urban mapping, offering new opportunities for an improved quality of geo-spatial products. In this perspective, a concurrent acquisition of LiDAR data and oblique imagery, seems to have all the potential to lead the airborne (urban) mapping sector a step forward. This contribution focuses on the first commercial example of such an integrated, all-in-one mapping solution, namely the Leica CityMapper hybrid sensor. By analysing two CityMapper datasets acquired over the city of Heilbronn (Germany) and Bordeaux (France), the paper investigates potential and challenges, w.r.t. (i) number and distribution of tie points between nadir and oblique images, (ii) strategy for image aerial triangulation (AT) and accuracy achievable w.r.t ground truth data, (iii) local noise level and completeness of dense image matching (DIM) point clouds w.r.t LiDAR data. Solutions for an integrated processing of the concurrently acquired ranging and imaging data are proposed, that open new opportunities for exploiting the real potential of both data sources.</p>

scholarly journals Simultaneous Two-Photon Voltage or Calcium Imaging and Multi-Channel Local Field Potential Recordings in Barrel Cortex of Awake and Anesthetized Mice

2021 ◽  
Vol 15 ◽  
Author(s):  
Claudia Cecchetto ◽  
Stefano Vassanelli ◽  
Bernd Kuhn
Keyword(s):  
Local Field ◽  
Calcium Imaging ◽  
Local Field Potential ◽  
Barrel Cortex ◽  
Field Potential ◽  
Imaging Data ◽  
Spatiotemporal Resolution ◽  
Two Photon ◽  
Cranial Window ◽  
Sensory Evoked

Neuronal population activity, both spontaneous and sensory-evoked, generates propagating waves in cortex. However, high spatiotemporal-resolution mapping of these waves is difficult as calcium imaging, the work horse of current imaging, does not reveal subthreshold activity. Here, we present a platform combining voltage or calcium two-photon imaging with multi-channel local field potential (LFP) recordings in different layers of the barrel cortex from anesthetized and awake head-restrained mice. A chronic cranial window with access port allows injecting a viral vector expressing GCaMP6f or the voltage-sensitive dye (VSD) ANNINE-6plus, as well as entering the brain with a multi-channel neural probe. We present both average spontaneous activity and average evoked signals in response to multi-whisker air-puff stimulations. Time domain analysis shows the dependence of the evoked responses on the cortical layer and on the state of the animal, here separated into anesthetized, awake but resting, and running. The simultaneous data acquisition allows to compare the average membrane depolarization measured with ANNINE-6plus with the amplitude and shape of the LFP recordings. The calcium imaging data connects these data sets to the large existing database of this important second messenger. Interestingly, in the calcium imaging data, we found a few cells which showed a decrease in calcium concentration in response to vibrissa stimulation in awake mice. This system offers a multimodal technique to study the spatiotemporal dynamics of neuronal signals through a 3D architecture in vivo. It will provide novel insights on sensory coding, closing the gap between electrical and optical recordings.

scholarly journals CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging

2018 ◽  
Vol 30 (10) ◽  
pp. 2726-2756 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
Per Jesper Sjöström ◽  
Simon R. Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Spike Train ◽  
Success Rate ◽  
Calcium Imaging ◽  
Ground Truth ◽  
Spike Trains ◽  
Imaging Data ◽  
Temporal Precision ◽  
Pulse Trains ◽  
Two Photon ◽  
Average Size

In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention, yet few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient—an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximized when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.

scholarly journals Synthetic single cell RNA sequencing data from small pilot studies using deep generative models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Treppner ◽  
Adrián Salas-Bastos ◽  
Moritz Hess ◽  
Stefan Lenz ◽  
Tanja Vogel ◽  
...  
Keyword(s):  
Single Cell ◽  
Synthetic Data ◽  
Ground Truth ◽  
Cell Types ◽  
Generative Models ◽  
Imaging Data ◽  
Ground Truth Data ◽  
Boltzmann Machines ◽  
Deep Boltzmann Machines ◽  
Univariate Distribution

AbstractDeep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the $$scVI_{posterior}$$ s c V I posterior variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10$$\times$$ × Genomics and Smart-seq2 technologies, we could show that for 10$$\times$$ × datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

scholarly journals Correlations enhance the behavioral readout of neural population activity in association cortex

2020 ◽  
Author(s):  
Martina Valente ◽  
Giuseppe Pica ◽  
Caroline A. Runyan ◽  
Ari S. Morcos ◽  
Christopher D. Harvey ◽  
...  
Keyword(s):  
Neural Activity ◽  
Posterior Parietal Cortex ◽  
Sensory Information ◽  
Neural Population ◽  
Association Cortex ◽  
Perceptual Discrimination ◽  
Imaging Data ◽  
Stimulus Information ◽  
Population Activity ◽  
Behavioral Choice

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on “noise” correlations – trial-to-trial covariations in neural activity for a given stimulus – as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a population’s total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouse’s behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

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