scholarly journals Automated classification of signal sources in mesoscale calcium imaging

2021 ◽  
Author(s):  
Brian R. Mullen ◽  
Sydney C. Weiser ◽  
Desiderio Ascencio ◽  
James B. Ackman
Keyword(s):  
Calcium Imaging ◽  
Data Driven ◽  
Data Sets ◽  
Automated Classification ◽  
Regional Network ◽  
Control Data ◽  
Learning Classifier ◽  
Neuronal Signal ◽  
Signal Sources

Functional imaging of neural cell populations is critical for mapping intra− and inter−regional network dynamics across the neocortex. Recently we showed that an unsupervised machine learning decomposition of densely sampled recordings of cortical calcium dynamics results in a collection of components comprised of neuronal signal sources distinct from optical, movement, and vascular artifacts. Here we build a supervised learning classifier that automatically separates neural activity and artifact components, using a set of extracted spatial and temporal metrics that characterize the respective components. We demonstrate that the performance of the machine classifier matches human identification of signal components in novel data sets. Further, we analyze control data recorded in glial cell reporter and non−fluorescent mouse lines that validates human and machine identification of functional component class. This combined workflow of data−driven video decomposition and machine classification of signal sources will aid robust and scalable mapping of complex cerebral dynamics.

scholarly journals CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS OF URBAN SCENES EXPLOITING CYLINDRICAL NEIGHBOURHOODS

2018 ◽  
Vol XLII-2 ◽  
pp. 1225-1228 ◽  
Author(s):  
M. Zheng ◽  
M. Lemmens ◽  
P. van Oosterom
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Bench Mark ◽  
Data Sets ◽  
Automated Classification ◽  
Height Difference ◽  
Urban Scenes ◽  
The Core ◽  
Selection Of

This paper presents our work on automated classification of Mobile Laser Scanning (MLS) point clouds of urban scenes with features derived from cylinders around points of consideration. The core of our method consists of spanning up a cylinder around points and deriving features, such as reflectance, height difference, from the points present within the cylindrical neighbourhood. Crucial in the approach is the selection of features from the points within the cylinder. An overall accuracy could be achieved, exploiting two bench mark data sets (Paris-rue-Madame and IQmulus & TerraMobilita) of 83 % and 87 % respectively.

scholarly journals Automated Classification of Asteroids into Families at Work

2014 ◽  
Vol 9 (S310) ◽  
pp. 130-133 ◽  
Author(s):  
Zoran Knežević ◽  
Andrea Milani ◽  
Alberto Cellino ◽  
Bojan Novaković ◽  
Federica Spoto ◽  
...  
Keyword(s):  
Data Sets ◽  
Automated Classification ◽  
New Approach ◽  
General Terms ◽  
The Individual ◽  
Family Classification

AbstractWe have recently proposed a new approach to the asteroid family classification by combining the classical HCM method with an automated procedure to add newly discovered members to existing families. This approach is specifically intended to cope with ever increasing asteroid data sets, and consists of several steps to segment the problem and handle the very large amount of data in an efficient and accurate manner. We briefly present all these steps and show the results from three subsequent updates making use of only the automated step of attributing the newly numbered asteroids to the known families. We describe the changes of the individual families membership, as well as the evolution of the classification due to the newly added intersections between the families, resolved candidate family mergers, and emergence of the new candidates for the mergers. We thus demonstrate how by the new approach the asteroid family classification becomes stable in general terms (converging towards a permanent list of confirmed families), and in the same time evolving in details (to account for the newly discovered asteroids) at each update.

scholarly journals Correlation-based spike sorting of multivariate data

2019 ◽  
Vol 5 (1) ◽  
pp. 113-116
Author(s):  
Pavel Larionov ◽  
Tom Juergens ◽  
Thomas Schanze
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Biomedical Engineering ◽  
Data Driven ◽  
Automated Classification ◽  
Electrode Arrays ◽  
Multichannel Recording ◽  
Important Method ◽  
Multichannel Electrode

AbstractAutomated classification of waveforms is an important method of data processing used in various fields of science, such as neuroscience, biomedical engineering, etc. This work shows the possibility of sorting special waveforms i.e. spikes recorded with multichannel electrode arrays by using principles of correlation and data-driven reference. A new method to estimate the number of k-means clusters by using a Monte Carlo method is introduced. To demonstrate the performance of the algorithm, generated signals were used, which are created to mimic multichannel recording of the extra-cellular neuronal signals.

scholarly journals Automated Classification of Evidence of Respect in the Communication through Twitter

2021 ◽  
Vol 11 (3) ◽  
pp. 1294
Author(s):  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Edgar Gutierrez ◽  
Tameika Liciaga ◽  
Alessandro Belmonte ◽  
...  
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Hate Speech ◽  
Automatic Detection ◽  
Data Sets ◽  
Automated Classification ◽  
Data Set ◽  
Analysis Methods ◽  
Textual Data

Volcanoes of hate and disrespect erupt in societies often not without fatal consequences. To address this negative phenomenon scientists struggled to understand and analyze its roots and language expressions described as hate speech. As a result, it is now possible to automatically detect and counter hate speech in textual data spreading rapidly, for example, in social media. However, recently another approach to tackling the roots of disrespect was proposed, it is based on the concept of promoting positive behavior instead of only penalizing hate and disrespect. In our study, we followed this approach and discovered that it is hard to find any textual data sets or studies discussing automatic detection regarding respectful behaviors and their textual expressions. Therefore, we decided to contribute probably one of the first human-annotated data sets which allows for supervised training of text analysis methods for automatic detection of respectful messages. By choosing a data set of tweets which already possessed sentiment annotations we were also able to discuss the correlation of sentiment and respect. Finally, we provide a comparison of recent machine and deep learning text analysis methods and their performance which allowed us to demonstrate that automatic detection of respectful messages in social media is feasible.

scholarly journals Classification of Autism Spectrum Disorder Data using Machine Learning Techniques

2019 ◽  
Vol 8 (6S) ◽  
pp. 565-569
Keyword(s):  
Machine Learning ◽  
Autism Spectrum ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Human Communication ◽  
Data Set ◽  
Complex Disorder ◽  
Learning Classifier ◽  
Learning Techniques

Autism is a neuro-developmental disability that affects human communication and behaviour. It is a condition that is associated with the complex disorder of the brain which can lead to significant changes in social interaction and behaviour of a human being.Machine learning techniques are being applied to autism data sets to discover useful hidden patterns and to construct predictive models for detecting its risk.This paper focuses on finding the best machine learning classifier on the UCI autism disorder data set for identifying the main factors associated with autism. The results obtained using Multilayer Perceptron, Naive Bayes Classifier and Bayesian Networkwere compared with J48 Decision tree algorithm. The superiority of MultilayerPerceptron over the well known classification algorithms in predicting the autism risk is established in this paper.

scholarly journals Automated classification of demographics from face images: A tutorial and validation

2018 ◽  
Author(s):  
Bastian Jaeger ◽  
Willem Sleegers ◽  
Anthony M Evans
Keyword(s):  
Statistical Power ◽  
Large Data ◽  
Demographic Characteristics ◽  
Data Sets ◽  
Automated Classification ◽  
Real World Data ◽  
Black And White ◽  
Face Classification ◽  
Face Images

Examining disparities in social outcomes as a function of gender, age, or race has a long tradition in psychology and other social sciences. With an increasing availability of large naturalistic data sets, researchers are afforded the opportunity to study the effects of demographic characteristics with real-world data and high statistical power. However, since traditional studies rely on human raters to asses demographic characteristics, limits in participant pools can hinder researchers from analyzing large data sets. Automated procedures offer a new solution to the classification of face images. Here, we present a tutorial on how to use two face classification algorithms, Face++ and Kairos. We also test and compare their accuracy under varying conditions and provide practical recommendations for their use. Drawing on two face databases (n = 2,805 images), we find that classification accuracy is (a) relatively high, with Kairos generally outperforming Face++ (b) similar for standardized and more variable images, and (c) dependent on target demographics. For example, accuracy was lower for Hispanic and Asian (vs. Black and White) targets. In sum, we propose that automated face classification can be a useful tool for researchers interested in studying the effects of demographic characteristics in large naturalistic data sets.

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