Decoding speech information from EEG data with 4, 7 and 11 month-old infants: Contrasting convolutional neural network, mutual information-based and backward linear models

2021 ◽  
Author(s):  
Mahmoud Keshavarzi ◽  
Áine Ní Choisdealbha ◽  
Adam Attaheri ◽  
Sinead Rocha ◽  
Perrine Brusini ◽  
...  
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Neural Activity ◽  
Computational Models ◽  
Delta Band ◽  
Infant Brain ◽  
Speech Envelope ◽  
Speech Information

Computational models that successfully translate neural activity into speech are multiplying in the adult literature, with non-linear convolutional neural network (CNN) approaches joining the more frequently-employed linear and mutual information (MI) models. Despite the promise of these methods for uncovering the neural basis of language acquisition by the human brain, similar studies with infants are rare. Existing infant studies rely on simpler cross-correlation and other linear techniques and aim only to establish neural tracking of the broadband speech envelope. Here, three novel computational models were applied to measure whether low-frequency speech envelope information was encoded in infant neural activity. Backward linear and CNN models were applied to estimate speech information from neural activity using linear versus nonlinear approaches, and a MI model measured how well the acoustic stimuli were encoded in infant neural responses. Fifty infants provided EEG recordings when aged 4, 7, and 11 months, while listening passively to natural speech (sung nursery rhymes) presented by video with a female singer. Each model computed speech information for these nursery rhymes in two different frequency bands, delta (1 – 4 Hz) and theta (4 – 8 Hz), thought to provide different types of linguistic information. All three models demonstrated significant levels of performance for delta-band and theta-band neural activity from 4 months of age. All models also demonstrated higher accuracy for the delta-band neural response in the infant brain. However, only the linear and MI models showed developmental (age-related) effects, and these developmental effects differed by model. Accordingly, the choice of algorithm used to decode speech envelope information from neural activity in the infant brain may determine the developmental conclusions that can be drawn. Better understanding of the strengths and weaknesses of each modelling approach will be fundamental to improving our understanding of how the human brain builds a language system.

Deep Convolutional Neural Network for Object Classification

2021 ◽  
pp. 317-343
Author(s):  
Amira Ahmad Al-Sharkawy ◽  
Gehan A. Bahgat ◽  
Elsayed E. Hemayed ◽  
Samia Abdel-Razik Mashali
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computational Models ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Object Classification ◽  
Classification Problem ◽  
Deep Convolutional Neural Network ◽  
Object Appearance

Object classification problem is essential in many applications nowadays. Human can easily classify objects in unconstrained environments easily. Classical classification techniques were far away from human performance. Thus, researchers try to mimic the human visual system till they reached the deep neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural network usage in object classification under constrained and unconstrained environment. The chapter gives a brief review on the classical techniques of object classification and the development of bio-inspired computational models from neuroscience till the creation of deep neural networks. A review is given on the constrained environment issues: the hardware computing resources and memory, the object appearance and background, and the training and processing time. Datasets that are used to test the performance are analyzed according to the images environmental conditions, besides the dataset biasing is discussed.

scholarly journals IAV-CNN: a 2D convolutional neural network model to predict antigenic variants of influenza A virus

2020 ◽  
Author(s):  
Rui Yin ◽  
Nyi Nyi Thwin ◽  
Pei Zhuang ◽  
Yu Zhang ◽  
Zhuoyi Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Influenza A ◽  
Computational Models ◽  
Rapid Evolution ◽  
Predictive Performance ◽  
Influenza Viruses ◽  
Distributed Representation ◽  
Similar Distribution ◽  
Feature Maps

The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we introduce a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combing the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.

scholarly journals Analysis of Brain Tumor Disease Detection Using Convolutional Neural Network

2021 ◽  
Author(s):  
Yogesh Deshmukh ◽  
Samiksha Dahe ◽  
Tanmayeeta Belote ◽  
Aishwarya Gawali ◽  
Sunnykumar Choudhary
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumor ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Vital Role ◽  
Machine Learning Algorithm ◽  
Different Types ◽  
The Brain

Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.

Deep Learning in Computational Neuroscience

2020 ◽  
pp. 43-63
Author(s):  
Sanjay Saxena ◽  
Sudip Paul ◽  
Adhesh Garg ◽  
Angana Saikia ◽  
Amitava Datta
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Human Brain ◽  
Convolutional Neural Network ◽  
Computational Neuroscience ◽  
Deep Model ◽  
The World ◽  
Current Scenario

Computational neuroscience is inspired by the mechanism of the human brain. Neural networks have reformed machine learning and artificial intelligence. Deep learning is a type of machine learning that teaches computers to do what comes naturally to individuals: acquire by example. It is inspired by biological brains and became the essential class of models in the field of machine learning. Deep learning involves several layers of computation. In the current scenario, researchers and scientists around the world are focusing on the implementation of different deep models and architectures. This chapter consists the information about major architectures of deep network. That will give the information about convolutional neural network, recurrent neural network, multilayer perceptron, and many more. Further, it discusses CNN (convolutional neural network) and its different pretrained models due to its major requirements in visual imaginary. This chapter also deliberates about the similarity of deep model and architectures with the human brain.

scholarly journals VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

2021 ◽  
Author(s):  
Ilia Igashov ◽  
liment Olechnovič ◽  
Maria Kadukova ◽  
Česlovas Venclovas ◽  
Sergei Grudinin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Computational Models ◽  
Voronoi Tessellation ◽  
Protein Structures ◽  
Data Representation ◽  
Deep Convolutional Neural Network ◽  
Molecular Structures ◽  
Supplementary Information ◽  
Evolutionary Information

Abstract Motivation Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. Results For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows us to efficiently introduce both convolution and pooling operations and train the network in an end-to-end fashion without precomputed descriptors. The resultant model, VoroCNN, predicts local qualities of 3D protein folds. The prediction results are competitive to state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in recognition of protein binding interfaces. Availability The model, data, and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Language Recognition Method of Convolutional Neural Network Based on Spectrogram

2019 ◽  
Vol 1 (2) ◽  
pp. p113 ◽  
Author(s):  
Wu Min ◽  
Zhu Shanshan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Model ◽  
Language Recognition ◽  
Speech Technology ◽  
Traditional Language ◽  
Low Dimensional ◽  
Speech Information ◽  
Spectrogram Feature ◽  
Important Branch

Language recognition is an important branch of speech technology. As a front-end technology of speech information processing, higher recognition accuracy is required. It is found through research that there are obvious differences between the language maps of different languages, which can be used for language identification. This paper uses a convolutional neural network as a classification model, and compares the language recognition effects of traditional language recognition features and spectrogram features on the five language recognition tasks of Chinese, Japanese, Vietnamese, Russian, and Spanish through experiments. The best effect is the ivector feature, and the spectrogram feature has a higher F value than the low-dimensional ivector feature.

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