scholarly journals Synthetic Data Resource and Benchmarks for Time Cell Analysis and Detection Algorithms

2022 ◽  
Author(s):  
Kambadur Gundu Ananthamurthy ◽  
Upinder S Bhalla
Keyword(s):  
Synthetic Data ◽  
Ground Truth ◽  
Synthetic Activity ◽  
Cell Detection ◽  
Data Resource ◽  
Detection Algorithms ◽  
Hippocampal Ca1 ◽  
Mouse Hippocampus ◽  
Good Concordance

Hippocampal CA1 cells take part in reliable, time-locked activity sequences in tasks that involve an association between stimuli, in a manner that tiles the interval between the stimuli. Such cells have been termed time cells. Here we adopt a first-principles approach to comparing diverse analysis and detection algorithms for identifying time cells. We developed a resource for generating synthetic activity datasets using calcium signals recorded in vivo from mouse hippocampus using 2-photon imaging, for template response waveforms. We assigned known, ground truth values for properties of time cells in this synthetic dataset, including noise, timing imprecision, hit-trial ratio and calcium event width. These datasets were the input to a pipeline for testing multiple algorithms for time cell detection to determine the conditions for which they were best suited, and evaluate their effective operating ranges. We find that most algorithms are sensitive to noise. Only a few methods benefit from larger event widths. Reassuringly, most methods are insensitive to timing imprecision, and exhibit successful time cell detection even at low hit trial ratios. Importantly, all methods show good concordance in identifying cells as time cells.

scholarly journals Inferring and validating mechanistic models of neural microcircuits based on spike-train data

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Josef Ladenbauer ◽  
Sam McKenzie ◽  
Daniel Fine English ◽  
Olivier Hagens ◽  
Srdjan Ostojic
Keyword(s):  
Parameter Estimation ◽  
Spike Train ◽  
Synthetic Data ◽  
Ground Truth ◽  
Neuronal Population ◽  
Mechanistic Models ◽  
Population Activity ◽  
Comprehensive Evaluations

Abstract The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity.

scholarly journals Inferring and validating mechanistic models of neural microcircuits based on spike-train data

2018 ◽  
Author(s):  
Josef Ladenbauer ◽  
Sam McKenzie ◽  
Daniel Fine English ◽  
Olivier Hagens ◽  
Srdjan Ostojic
Keyword(s):  
Parameter Estimation ◽  
Spike Train ◽  
Synthetic Data ◽  
Ground Truth ◽  
Neuronal Population ◽  
Mechanistic Models ◽  
Population Activity ◽  
Comprehensive Evaluations

AbstractThe interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity.

Monocytes as Carriers of Magnetic Nanoparticles for Tracking Inflammation in the Epileptic Rat Brain

2019 ◽  
Vol 16 (7) ◽  
pp. 637-644 ◽  
Author(s):  
Hadas Han ◽  
Sara Eyal ◽  
Emma Portnoy ◽  
Aniv Mann ◽  
Miriam Shmuel ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Ex Vivo ◽  
In Vivo Studies ◽  
Nanoparticle Distribution ◽  
Spontaneous Seizures ◽  
Systemic Distribution ◽  
Hippocampal Ca1 ◽  
Pilocarpine Model

Background: Inflammation is a hallmark of epileptogenic brain tissue. Previously, we have shown that inflammation in epilepsy can be delineated using systemically-injected fluorescent and magnetite- laden nanoparticles. Suggested mechanisms included distribution of free nanoparticles across a compromised blood-brain barrier or their transfer by monocytes that infiltrate the epileptic brain. Objective: In the current study, we evaluated monocytes as vehicles that deliver nanoparticles into the epileptic brain. We also assessed the effect of epilepsy on the systemic distribution of nanoparticleloaded monocytes. Methods: The in vitro uptake of 300-nm nanoparticles labeled with magnetite and BODIPY (for optical imaging) was evaluated using rat monocytes and fluorescence detection. For in vivo studies we used the rat lithium-pilocarpine model of temporal lobe epilepsy. In vivo nanoparticle distribution was evaluated using immunohistochemistry. Results: 89% of nanoparticle loading into rat monocytes was accomplished within 8 hours, enabling overnight nanoparticle loading ex vivo. The dose-normalized distribution of nanoparticle-loaded monocytes into the hippocampal CA1 and dentate gyrus of rats with spontaneous seizures was 176-fold and 380-fold higher compared to the free nanoparticles (p<0.05). Seizures were associated with greater nanoparticle accumulation within the liver and the spleen (p<0.05). Conclusion: Nanoparticle-loaded monocytes are attracted to epileptogenic brain tissue and may be used for labeling or targeting it, while significantly reducing the systemic dose of potentially toxic compounds. The effect of seizures on monocyte biodistribution should be further explored to better understand the systemic effects of epilepsy.

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

2021 ◽  
Vol 13 (7) ◽  
pp. 1238
Author(s):  
Jere Kaivosoja ◽  
Juho Hautsalo ◽  
Jaakko Heikkinen ◽  
Lea Hiltunen ◽  
Pentti Ruuttunen ◽  
...  
Keyword(s):  
Reference Data ◽  
Measurement Techniques ◽  
Synthetic Data ◽  
Ground Truth ◽  
Precision Farming ◽  
Aerial Imaging ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

scholarly journals Simulating a Ground Truth for Transit Time Analysis of Indicator Dilution Curves

2020 ◽  
Vol 6 (3) ◽  
pp. 268-271
Author(s):  
Michael Reiß ◽  
Ady Naber ◽  
Werner Nahm
Keyword(s):  
Transit Time ◽  
Sampling Rate ◽  
Ground Truth ◽  
Indicator Dilution ◽  
Cross Sectional ◽  
In Vivo Measurements ◽  
Multiple Indicator Dilution ◽  
Transit Times ◽  
Dilution Curves

AbstractTransit times of a bolus through an organ can provide valuable information for researchers, technicians and clinicians. Therefore, an indicator is injected and the temporal propagation is monitored at two distinct locations. The transit time extracted from two indicator dilution curves can be used to calculate for example blood flow and thus provide the surgeon with important diagnostic information. However, the performance of methods to determine the transit time Δt cannot be assessed quantitatively due to the lack of a sufficient and trustworthy ground truth derived from in vivo measurements. Therefore, we propose a method to obtain an in silico generated dataset of differently subsampled indicator dilution curves with a ground truth of the transit time. This method allows variations on shape, sampling rate and noise while being accurate and easily configurable. COMSOL Multiphysics is used to simulate a laminar flow through a pipe containing blood analogue. The indicator is modelled as a rectangular function of concentration in a segment of the pipe. Afterwards, a flow is applied and the rectangular function will be diluted. Shape varying dilution curves are obtained by discrete-time measurement of the average dye concentration over different cross-sectional areas of the pipe. One dataset is obtained by duplicating one curve followed by subsampling, delaying and applying noise. Multiple indicator dilution curves were simulated, which are qualitatively matching in vivo measurements. The curves temporal resolution, delay and noise level can be chosen according to the requirements of the field of research. Various datasets, each containing two corresponding dilution curves with an existing ground truth transit time, are now available. With additional knowledge or assumptions regarding the detection-specific transfer function, realistic signal characteristics can be simulated. The accuracy of methods for the assessment of Δt can now be quantitatively compared and their sensitivity to noise evaluated.

scholarly journals Experimental Evaluation of Computer Vision and Machine Learning-Based UAV Detection and Ranging

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 37
Author(s):  
Bingsheng Wei ◽  
Martin Barczyk
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Tracking System ◽  
Ground Truth ◽  
White Background ◽  
Cascade Classifier ◽  
Detection Algorithms ◽  
Squared Error ◽  
Test Conditions ◽  
Video Feed

We consider the problem of vision-based detection and ranging of a target UAV using the video feed from a monocular camera onboard a pursuer UAV. Our previously published work in this area employed a cascade classifier algorithm to locate the target UAV, which was found to perform poorly in complex background scenes. We thus study the replacement of the cascade classifier algorithm with newer machine learning-based object detection algorithms. Five candidate algorithms are implemented and quantitatively tested in terms of their efficiency (measured as frames per second processing rate), accuracy (measured as the root mean squared error between ground truth and detected location), and consistency (measured as mean average precision) in a variety of flight patterns, backgrounds, and test conditions. Assigning relative weights of 20%, 40% and 40% to these three criteria, we find that when flying over a white background, the top three performers are YOLO v2 (76.73 out of 100), Faster RCNN v2 (63.65 out of 100), and Tiny YOLO (59.50 out of 100), while over a realistic background, the top three performers are Faster RCNN v2 (54.35 out of 100, SSD MobileNet v1 (51.68 out of 100) and SSD Inception v2 (50.72 out of 100), leading us to recommend Faster RCNN v2 as the recommended solution. We then provide a roadmap for further work in integrating the object detector into our vision-based UAV tracking system.

scholarly journals Community Detection in Multiplex Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Matteo Magnani ◽  
Obaida Hanteer ◽  
Roberto Interdonato ◽  
Luca Rossi ◽  
Andrea Tagarelli
Keyword(s):  
Community Detection ◽  
Experimental Evaluation ◽  
Network Models ◽  
Ground Truth ◽  
Community Structures ◽  
Multiplex Networks ◽  
Detection Algorithms ◽  
Multiplex Network ◽  
The Right ◽  
Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data

2017 ◽  
Vol 223 (1) ◽  
pp. 519-533 ◽  
Author(s):  
Jiangheng Guan ◽  
Jingcheng Li ◽  
Shanshan Liang ◽  
Ruijie Li ◽  
Xingyi Li ◽  
...  
Keyword(s):  
Cell Detection ◽  
Imaging Data ◽  
Two Photon

scholarly journals Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3415 ◽  
Author(s):  
Jinpeng Zhang ◽  
Jinming Zhang ◽  
Shan Yu
Keyword(s):  
Object Detection ◽  
Sampling Method ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Two Stage ◽  
Baseline Model ◽  
Detection Algorithms ◽  
Imbalance Problem ◽  
Image Object Detection ◽  
Image Object

In the image object detection task, a huge number of candidate boxes are generated to match with a relatively very small amount of ground-truth boxes, and through this method the learning samples can be created. But in fact the vast majority of the candidate boxes do not contain valid object instances and should be recognized and rejected during the training and evaluation of the network. This leads to extra high computation burden and a serious imbalance problem between object and none-object samples, thereby impeding the algorithm’s performance. Here we propose a new heuristic sampling method to generate candidate boxes for two-stage detection algorithms. It is generally applicable to the current two-stage detection algorithms to improve their detection performance. Experiments on COCO dataset showed that, relative to the baseline model, this new method could significantly increase the detection accuracy and efficiency.

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