scholarly journals Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mark R. Saddler ◽  
Ray Gonzalez ◽  
Josh H. McDermott
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neural Coding ◽  
Network Models ◽  
Pitch Perception ◽  
Natural Environments ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Perceptual Systems ◽  
Biological Organisms

AbstractPerception is thought to be shaped by the environments for which organisms are optimized. These influences are difficult to test in biological organisms but may be revealed by machine perceptual systems optimized under different conditions. We investigated environmental and physiological influences on pitch perception, whose properties are commonly linked to peripheral neural coding limits. We first trained artificial neural networks to estimate fundamental frequency from biologically faithful cochlear representations of natural sounds. The best-performing networks replicated many characteristics of human pitch judgments. To probe the origins of these characteristics, we then optimized networks given altered cochleae or sound statistics. Human-like behavior emerged only when cochleae had high temporal fidelity and when models were optimized for naturalistic sounds. The results suggest pitch perception is critically shaped by the constraints of natural environments in addition to those of the cochlea, illustrating the use of artificial neural networks to reveal underpinnings of behavior.

scholarly journals Deep neural network models reveal interplay of peripheral coding and stimulus statistics in pitch perception

2020 ◽  
Author(s):  
Mark R. Saddler ◽  
Ray Gonzalez ◽  
Josh H. McDermott
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Neural Coding ◽  
Deep Neural Network ◽  
Network Models ◽  
Pitch Perception ◽  
Natural Environments ◽  
Sensory Receptors ◽  
Natural Sounds ◽  
Neural Network Models

ABSTRACTComputations on receptor responses enable behavior in the environment. Behavior is plausibly shaped by both the sensory receptors and the environments for which organisms are optimized, but their roles are often opaque. One classic example is pitch perception, whose properties are commonly linked to peripheral neural coding limits rather than environmental acoustic constraints. We trained artificial neural networks to estimate fundamental frequency from simulated cochlear representations of natural sounds. The best-performing networks replicated many characteristics of human pitch judgments. To probe how our ears and environment shape these characteristics, we optimized networks given altered cochleae or sound statistics. Human-like behavior emerged only when cochleae had high temporal fidelity and when models were optimized for natural sounds. The results suggest pitch perception is critically shaped by the constraints of natural environments in addition to those of the cochlea, illustrating the use of contemporary neural networks to reveal underpinnings of behavior.

Predicting the exchange rate in Sudan using neural networks models during the period (1960 - 2017): التنبؤ بسعر الصرف في السودان باستخدام نماذج الشبكات العصبية خلال الفترة (1960م – 2017م)

2020 ◽  
Vol 4 (14) ◽  
Author(s):  
Fathi Ahmed Ali Adam, Mahmoud Mohamed Abdel Aziz Gamal El-Di
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Network Models ◽  
Neural Network Models ◽  
Classical Models ◽  
Artificial Neural ◽  
The Exchange Rate ◽  
Long Term Forecasting ◽  
Artificial Neural Network Models

The study examined the use of artificial neural network models to predict the exchange rate in Sudan through annual exchange rate data between the US dollar and the Sudanese pound. This study aimed to formulate the models of artificial neural networks in which the exchange rate can be predicted in the coming period. The importance of the study is that it is necessary to use modern models to predict instead of other classical models. The study hypothesized that the models of artificial neural networks have a high ability to predict the exchange rate. Use models of artificial neural networks. The most important results ability of artificial neural networks models to predict the exchange rate accurately, Form MLP (1-1-1) is the best model chosen for that purpose. The study recommended the development of the proposed model for long-term forecasting.

Artificial Neural Networks

2011 ◽  
pp. 222-243
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul Sarker
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Network Applications ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

Comparing Conventional and Artificial Neural Network Models for the Pricing of Options

2002 ◽  
pp. 220-235 ◽  
Author(s):  
Paul Lajbcygier
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Parameter ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Conventional Models ◽  
Artificial Neural Network Models

The pricing of options on futures is compared using conventional models and artificial neural networks. This work demonstrates superior pricing accuracy using the artificial neural networks in an important subset of the input parameter set.

scholarly journals Radyal Tabanlı Yapay Sinir Ağlarının Kurutma Kinetiğinin Modellenmesinde Kullanımı

2020 ◽  
Vol 8 (2) ◽  
pp. 511
Author(s):  
Adil Koray Yıldız ◽  
Muhammed Taşova ◽  
Hakan Polatcı
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Drying Kinetics ◽  
Hot Pepper ◽  
Neural Network Models ◽  
Drying Time ◽  
Drying Behavior ◽  
Artificial Neural ◽  
Hidden Layer

Drying method is preferred in agricultural products since it provides advantages in many processes such as increasing the strength of products, transporting and storing. It is necessary to estimate the drying behavior of the products in order to achieve the best drying without reducing the product quality. For this reason, many numerical drying models have been developed to estimate the drying kinetics of the products. Recently, artificial neural networks have been widely used for the development of these models. Artificial neural networks are mathematical models that work in a similar way to natural neuron cells. Radial based artificial neural networks are radial based activation functions in the transition to the hidden layer unlike other networks. In this study, modeling of drying kinetics with radial based networks was investigated. For the experiment, red hot pepper (Capsicum annuum L.) was dipped in boiled water and microwave pretreatments and, then dried in the oven at 65°C. The absorbable moisture values were calculated during the drying period. The radial based artificial neural network models were trained with the drying time values as input and the absorbable moisture values as output. The study was carried out with two data sets including all data and only the average. In trainings with all data, R value of the best model was calculated as 0.9566. R was calculated as 0.9998 with average data.

Artificial Neural Networks

2006 ◽  
pp. 1-27 ◽  
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul A. Sarker
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Network Applications ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

scholarly journals Artificial neural networks and the study of evolution of prey coloration

2007 ◽  
Vol 362 (1479) ◽  
pp. 421-430 ◽  
Author(s):  
Sami Merilaita
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Visual Information ◽  
Visual Complexity ◽  
Network Models ◽  
Neural Network Models ◽  
General Terms ◽  
Artificial Neural

In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolution of prey coloration. Therefore, the capability to process patterns as well as to imitate aspects of predator's information processing and responses to visual information makes neural networks a well-suited modelling approach for the study of prey coloration. In addition, their suitability for evolutionary simulations is an advantage when complex or dynamic interactions are modelled. Since not all behaviours of neural network models are necessarily biologically relevant, it is important to validate a neural network model with empirical data. Bringing together knowledge about neural networks with knowledge about topics of prey coloration would provide a potential way to deepen our understanding of the specific appearances of prey coloration.

scholarly journals Development and Application of Back-Propagation-Based Artificial Neural Network Models in Solving Engineering

2002 ◽  
Vol 7 (1) ◽  
pp. 55
Author(s):  
Saleh Mohammed Al-Alawi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Development ◽  
Back Propagation ◽  
Network Models ◽  
Research Area ◽  
Ann Model ◽  
Neural Network Models ◽  
Fields Of Study ◽  
Artificial Neural

Artificial Neural Networks (ANNs) are computer software programs that mimic the human brain's ability to classify patterns or to make forecasts or decisions based on past experience.  The development of this research area can be attributed to two factors, sufficient computer power to begin practical ANN-based research in the late 1970s and the development of back-propagation in 1986 that enabled ANN models to solve everyday business, scientific, and industrial problems.  Since then, significant applications have been implemented in several fields of study, and many useful intelligent applications and systems have been developed.  The objective of this paper is to generate awareness and to encourage applications development using artificial intelligence-based systems.  Therefore, this paper provides basic ANN concepts, outlines steps used for ANN model development, and lists examples of engineering applications based on the use of the back-propagation paradigm conducted in Oman.  The paper is intended to provide guidelines and necessary references and resources for novice individuals interested in conducting research in engineering or other fields of study using back-propagation artificial neural networks.      

scholarly journals Automatic migraine classification using artificial neural networks

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 618
Author(s):  
Paola A. Sanchez-Sanchez ◽  
José Rafael García-González ◽  
Juan Manuel Rúa Ascar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Accuracy And Precision ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Selection Of

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

scholarly journals Speech Command Recognition using Artificial Neural Networks

2020 ◽  
Vol 4 (2) ◽  
pp. 73
Author(s):  
Sushan Poudel ◽  
Dr. R Anuradha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Recognition System ◽  
Neural Network Models ◽  
Artificial Neural

Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is used to implement different Neural Network models which include Convolutional Neural Network and Recurrent Neural Network combined with Convolutional Neural Network. The combination of Convolutional and Recurrent Neural Network outperforms Convolutional Neural Network alone by 8% and achieved 96.66% accuracy for 20 labels.

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