scholarly journals Action detection using a neural network elucidates the genetics of mouse grooming behavior

2020 ◽  
Author(s):  
Brian Q. Geuther ◽  
Asaf Peer ◽  
Hao He ◽  
Gautam Sabnis ◽  
Vivek M. Philip ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Stereotyped Behavior ◽  
General Purpose ◽  
Training Data ◽  
Mouse Strains ◽  
Human Observer ◽  
Grooming Behavior ◽  
Action Detection ◽  
Visual Diversity

AbstractAutomated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming, a prototypical stereotyped behavior, is often used as an endophenotype in psychiatric genetics. Using mouse grooming behavior as an example, we develop a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operate across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We survey grooming behavior in the open field in 2500 mice across 62 strains, determine its heritable components, conduct GWAS to outline its genetic architecture, and perform PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of mechanisms underlying these behaviors.

scholarly journals Action detection using a neural network elucidates the genetics of mouse grooming behavior

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Brian Q Geuther ◽  
Asaf Peer ◽  
Hao He ◽  
Gautam Sabnis ◽  
Vivek M Philip ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Stereotyped Behavior ◽  
General Purpose ◽  
Training Data ◽  
Mouse Strains ◽  
Human Observer ◽  
Grooming Behavior ◽  
Action Detection ◽  
Visual Diversity

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

scholarly journals A Novel Convolutional Neural Network for Classifying Indian Coins by Denomination

2021 ◽  
Author(s):  
Yash Chauhan ◽  
Prateek Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Academic Research ◽  
High Volume ◽  
Classification Problem ◽  
General Purpose ◽  
Training Data ◽  
Slot Machines ◽  
Validation Data ◽  
Starting Point

Coins recognition systems have humungous applications from vending and slot machines to banking and management firms which directly translate to a high volume of research regarding the development of methods for such classification. In recent years, academic research has shifted towards a computer vision approach for sorting coins due to the advancement in the field of deep learning. However, most of the documented work utilizes what is known as ‘Transfer Learning’ in which we reuse a pre-trained model of a fixed architecture as a starting point for our training. While such an approach saves us a lot of time and effort, the generic nature of the pre-trained model can often become a bottleneck for performance on a specialized problem such as coin classification. This study develops a convolutional neural network (CNN) model from scratch and tests it against a widely-used general-purpose architecture known as Googlenet. We have shown in this study by comparing the performance of our model with that of Googlenet (documented in various previous studies) that a more straightforward and specialized architecture is more optimal than a more complex general architecture for the coin classification problem. The model developed in this study is trained and tested on 720 and 180 images of Indian coins of different denominations, respectively. The final accuracy gained by the model is 91.62% on the training data, while the accuracy is 90.55% on the validation data.

Segmentation Neural Network Incorporating Scale-Space in the Application of Cardiac MRI

2020 ◽  
Vol 10 (7) ◽  
pp. 1494-1505
Author(s):  
Hyo-Hun Kim ◽  
Byung-Woo Hong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cardiac Mri ◽  
Network Architecture ◽  
Data Augmentation ◽  
Scale Space ◽  
Segmentation Algorithm ◽  
Training Data ◽  
Training Procedure ◽  
Whole Heart

In this work, we present an image segmentation algorithm based on the convolutional neural network framework where the scale space theory is incorporated in the course of training procedure. The construction of data augmentation is designed to apply the scale space to the training data in order to effectively deal with the variability of regions of interest in geometry and appearance such as shape and contrast. The proposed data augmentation algorithm via scale space is aimed to improve invariant features with respect to both geometry and appearance by taking into consideration of their diffusion process. We develop a segmentation algorithm based on the convolutional neural network framework where the network architecture consists of encoding and decoding substructures in combination with the data augmentation scheme via the scale space induced by the heat equation. The quantitative analysis using the cardiac MRI dataset indicates that the proposed algorithm achieves better accuracy in the delineation of the left ventricles, which demonstrates the potential of the algorithm in the application of the whole heart segmentation as a compute-aided diagnosis system for the cardiac diseases.

scholarly journals Batik pattern recognition using convolutional neural network

2020 ◽  
Vol 9 (4) ◽  
pp. 1430-1437
Author(s):  
Mohammad Arif Rasyidi ◽  
Taufiqotul Bariyah
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Network Architecture ◽  
Experimental Results ◽  
Training Data ◽  
Excellent Performance ◽  
Artistic Value

Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.

scholarly journals TweetyNet: A neural network that enables high-throughput, automated annotation of birdsong

2020 ◽  
Author(s):  
Yarden Cohen ◽  
David Nicholson ◽  
Alexa Sanchioni ◽  
Emily K. Mallaber ◽  
Viktoriya Skidanova ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Error Rates ◽  
Training Data ◽  
Manual Annotation ◽  
Require Time ◽  
Automated Annotation ◽  
Complex Song ◽  
Wide Range ◽  
The Individual

AbstractSongbirds have long been studied as a model system of sensory-motor learning. Many analyses of birdsong require time-consuming manual annotation of the individual elements of song, known as syllables or notes. Here we describe the first automated algorithm for birdsong annotation that is applicable to complex song such as canary song. We developed a neural network architecture, “TweetyNet”, that is trained with a small amount of hand-labeled data using supervised learning methods. We first show TweetyNet achieves significantly lower error on Bengalese finch song than a similar method, using less training data, and maintains low error rates across days. Applied to canary song, TweetyNet achieves fully automated annotation of canary song, accurately capturing the complex statistical structure previously discovered in a manually annotated dataset. We conclude that TweetyNet will make it possible to ask a wide range of new questions focused on complex songs where manual annotation was impractical.

scholarly journals Robust Mouse Tracking in Complex Environments using Neural Networks

2018 ◽  
Author(s):  
Brian Q. Geuther ◽  
Sean P. Deats ◽  
Kai J. Fox ◽  
Steve A. Murray ◽  
Robert E. Braun ◽  
...  
Keyword(s):  
Neural Network ◽  
Environmental Conditions ◽  
State Of The Art ◽  
High Accuracy ◽  
General Purpose ◽  
Training Data ◽  
Network Architectures ◽  
Complex Environments ◽  
Behavioral Experiments ◽  
Modern Machine

AbstractThe ability to track animals accurately is critical for behavioral experiments. For video-based assays, this is often accomplished by manipulating environmental conditions to increase contrast between the animal and the background, in order to achieve proper foreground/background detection (segmentation). However, as behavioral paradigms become more sophisticated with ethologically relevant environments, the approach of modifying environmental conditions offers diminishing returns, particularly for scalable experiments. Currently, there is a need for methods to monitor behaviors over long periods of time, under dynamic environmental conditions, and in animals that are genetically and behaviorally heterogeneous. To address this need, we developed a state-of-the-art neural network-based tracker for mice, using modern machine vision techniques. We test three different neural network architectures to determine their performance on genetically diverse mice under varying environmental conditions. We find that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data. Furthermore, we provide a labeling interface, labeled training data, tuned hyperparameters, and a pre-trained network for the mouse behavior and neuroscience communities. This general-purpose neural network tracker can be easily extended to other experimental paradigms and even to other animals, through transfer learning, thus providing a robust, generalizable solution for biobehavioral research.

scholarly journals Capturing the Style of Fake News

2020 ◽  
Vol 34 (01) ◽  
pp. 490-497
Author(s):  
Piotr Przybyla
Keyword(s):  
Neural Network ◽  
High Accuracy ◽  
General Purpose ◽  
Training Data ◽  
Fake News ◽  
Realistic Evaluation ◽  
News Websites ◽  
Text Classifiers ◽  
Automatic Methods ◽  
Online Sources

In this study we aim to explore automatic methods that can detect online documents of low credibility, especially fake news, based on the style they are written in. We show that general-purpose text classifiers, despite seemingly good performance when evaluated simplistically, in fact overfit to sources of documents in training data. In order to achieve a truly style-based prediction, we gather a corpus of 103,219 documents from 223 online sources labelled by media experts, devise realistic evaluation scenarios and design two new classifiers: a neural network and a model based on stylometric features. The evaluation shows that the proposed classifiers maintain high accuracy in case of documents on previously unseen topics (e.g. new events) and from previously unseen sources (e.g. emerging news websites). An analysis of the stylometric model indicates it indeed focuses on sensational and affective vocabulary, known to be typical for fake news.

scholarly journals Neural Network Analysis of Dynamic Fracture in a Layered Material

MRS Advances ◽  
2019 ◽  
Vol 4 (19) ◽  
pp. 1109-1117 ◽  
Author(s):  
Pankaj Rajak ◽  
Rajiv K. Kalia ◽  
Aiichiro Nakano ◽  
Priya Vashishta
Keyword(s):  
Neural Network ◽  
Dynamic Fracture ◽  
Network Architecture ◽  
Md Simulation ◽  
Structural Phase ◽  
Feature Learning ◽  
Training Data ◽  
Model Complexity ◽  
Feed Forward Neural Network ◽  
Data Set

AbstractDynamic fracture of a two-dimensional MoWSe2 membrane is studied with molecular dynamics (MD) simulation. The system consists of a random distribution of WSe2 patches in a pre-cracked matrix of MoSe2. Under strain, the system shows toughening due to crack branching, crack closure and strain-induced structural phase transformation from 2H to 1T crystal structures. Different structures generated during MD simulation are analyzed using a three-layer, feed-forward neural network (NN) model. A training data set of 36,000 atoms is created where each atom is represented by a 50-dimension feature vector consisting of radial and angular symmetry functions. Hyper parameters of the symmetry functions and network architecture are tuned to minimize model complexity with high predictive power using feature learning, which shows an increase in model accuracy from 67% to 95%. The NN model classifies each atom in one of the six phases which are either as transition metal or chalcogen atoms in 2H phase, 1T phase and defects. Further t-SNE analyses of learned representation of these phases in the hidden layers of the NN model show that separation of all phases become clearer in the third layer than in layers 1 and 2.

Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

2014 ◽  
Vol 574 ◽  
pp. 342-346
Author(s):  
Hong Yan Duan ◽  
Huan Rong Zhang ◽  
Ming Zheng ◽  
Xiao Hong Wang
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Approximate Methods ◽  
The Neural Network

The fracture problems of medium carbon steel under extra-low cycle bend torsion fatigue loading were studied using artificial neural networks (ANN) in this paper. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and notch open angle, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

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