Timbre Similarity: Convergence of Neural, Behavioral, and Computational Approaches

1998 ◽  
Vol 16 (2) ◽  
pp. 223-241 ◽  
Author(s):  
Petri Toiviainen ◽  
Mari Tervaniemi ◽  
Jukka Louhivuori ◽  
Marieke Saher ◽  
Minna Huotilainen ◽  
...  
Keyword(s):  
Brain Activity ◽  
Network Models ◽  
Large Set ◽  
Self Organizing Map ◽  
Neural Network Models ◽  
Behavioral Measures ◽  
Electrical Brain Activity ◽  
Behavioral Studies ◽  
Artificial Neurons ◽  
Self Organizing

The present study compared the degree of similarity of timbre representations as observed with brain recordings, behavioral studies, and computer simulations. To this end, the electrical brain activity of subjects was recorded while they were repetitively presented with five sounds differing in timbre. Subjects read simultaneously so that their attention was not focused on the sounds. The brain activity was quantified in terms of a change-specific mismatch negativity component. Thereafter, the subjects were asked to judge the similarity of all pairs along a five-step scale. A computer simulation was made by first training a Kohonen self-organizing map with a large set of instrumental sounds. The map was then tested with the experimental stimuli, and the distance between the most active artificial neurons was measured. The results of these methods were highly similar, suggesting that timbre representations reflected in behavioral measures correspond to neural activity, both as measured directly and as simulated in self-organizing neural network models.

scholarly journals Detecting Signatures in Hyperspectral Image Data of Wounds: A Compound Model of Self- Organizing Map and Least Square Fitting

2018 ◽  
Vol 4 (1) ◽  
pp. 419-422
Author(s):  
Redwan Abdo A. Mohammed ◽  
Daniel Schäle ◽  
Christoph Hornberger ◽  
Steffen Emmert
Keyword(s):  
Neural Network ◽  
Hyperspectral Image ◽  
Image Data ◽  
Network Models ◽  
Least Square Method ◽  
Least Square ◽  
Self Organizing Map ◽  
Neural Network Models ◽  
Wound Tissue ◽  
Self Organizing

AbstractThe purpose of this study is to develop a method to discriminate spectral signatures in wound tissue. We have collected a training set of the intensity of the remitted light for different types of wound tissue from different patients using a TIVITA™ tissue camera. We used a neural network technique (self-organizing map) to group areas with the same spectral properties together. The results of this work indicates that neural network models are capable of finding clusters of closely related hyperspectral signatures in wound tissue, and thus can be used as a powerful tool to reach the anticipated classification. Moreover, we used a least square method to fit literature spectra (i.e. oxygenated haemoglobin (O2Hb), deoxygenated haemoglobin (HHb), water and fat) to the learned spectral classes. This procedure enables us to label each spectral class with the corresponding absorbance properties for the different absorbance of interest (i.e. O2Hb, HHb, water and fat). The calculated parameters of a testing set were consistent with the expected behaviour and show a good agreement with the results of a second algorithm which is used in the TIVITA™ tissue camera.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

scholarly journals Caffeine boosts preparatory attention for reward-related stimulus information

2019 ◽  
Author(s):  
Berry van den Berg ◽  
Marlon de Jong ◽  
Marty G. Woldorff ◽  
Monicque M. Lorist
Keyword(s):  
Brain Activity ◽  
Cognitive Task ◽  
Relevant Information ◽  
Caffeine Intake ◽  
Alpha Power ◽  
Behavioral Measures ◽  
Placebo Condition ◽  
Electrical Brain Activity ◽  
Preparatory Attention ◽  
Stroop Stimulus

AbstractBoth the intake of caffeine-containing substances and the prospect of reward for performing a cognitive task have been associated with improved behavioral performance. To investigate the possible common and interactive influences of caffeine and reward-prospect on preparatory attention, we tested 24 participants during a 2-session experiment in which they performed a cued-reward color-word Stroop task. On each trial, participants were presented with a cue to inform them whether they had to prepare for presentation of a Stroop stimulus and whether they could receive a reward if they performed well on that trial. Prior to each session, participants received either coffee with caffeine (3 mg/kg bodyweight) or with placebo (3 mg/kg bodyweight lactose). In addition to behavioral measures, electroencephalography (EEG) measures of electrical brain activity were recorded. Results showed that both the intake of caffeine and the prospect of reward improved speed and accuracy, with the effects of caffeine and reward-prospect being additive on performance. Neurally, reward-prospect resulted in an enlarged contingent negative variation (CNV) and reduced posterior alpha power (indicating increased cortical activity), both hallmark neural markers for preparatory attention. Moreover, the CNV enhancement for reward-prospect trials was considerably more pronounced in the caffeine condition as compared to the placebo condition. These results thus suggest that caffeine intake boosts preparatory attention for task-relevant information, especially when performance on that task can lead to reward.

Computer Vision Applications of Self-Organizing Neural Networks

2013 ◽  
pp. 129-138
Author(s):  
José García-Rodríguez ◽  
Juan Manuel García-Chamizo ◽  
Sergio Orts-Escolano ◽  
Vicente Morell-Gimenez ◽  
José Antonio Serra-Pérez ◽  
...  
Keyword(s):  
Real Time ◽  
Network Models ◽  
Suitable Model ◽  
Surveillance Systems ◽  
Neural Network Models ◽  
Neural Gas ◽  
3D Data ◽  
Computer Vision Applications ◽  
Recognition Systems ◽  
Self Organizing

This chapter aims to address the ability of self-organizing neural network models to manage video and image processing in real-time. The Growing Neural Gas networks (GNG) with its attributes of growth, flexibility, rapid adaptation, and excellent quality representation of the input space makes it a suitable model for real time applications. A number of applications are presented, including: image compression, hand and medical image contours representation, surveillance systems, hand gesture recognition systems, and 3D data reconstruction.

Mathematics anxiety and cognition: an integrated neural network model

2020 ◽  
Vol 31 (3) ◽  
pp. 287-296
Author(s):  
Ahmed A. Moustafa ◽  
Angela Porter ◽  
Ahmed M. Megreya
Keyword(s):  
Neural Network ◽  
Educational Outcomes ◽  
Mathematics Anxiety ◽  
Network Models ◽  
Neural Mechanism ◽  
Neural Network Models ◽  
Cognitive Studies ◽  
Behavioral Studies ◽  
Negative Effect ◽  
The Impact

AbstractMany students suffer from anxiety when performing numerical calculations. Mathematics anxiety is a condition that has a negative effect on educational outcomes and future employment prospects. While there are a multitude of behavioral studies on mathematics anxiety, its underlying cognitive and neural mechanism remain unclear. This article provides a systematic review of cognitive studies that investigated mathematics anxiety. As there are no prior neural network models of mathematics anxiety, this article discusses how previous neural network models of mathematical cognition could be adapted to simulate the neural and behavioral studies of mathematics anxiety. In other words, here we provide a novel integrative network theory on the links between mathematics anxiety, cognition, and brain substrates. This theoretical framework may explain the impact of mathematics anxiety on a range of cognitive and neuropsychological tests. Therefore, it could improve our understanding of the cognitive and neurological mechanisms underlying mathematics anxiety and also has important applications. Indeed, a better understanding of mathematics anxiety could inform more effective therapeutic techniques that in turn could lead to significant improvements in educational outcomes.

CURVED TRAJECTORY PREDICTION USING A SELF-ORGANIZING NEURAL NETWORK

2000 ◽  
Vol 10 (01) ◽  
pp. 59-70 ◽  
Author(s):  
JONATHAN A. MARSHALL ◽  
VISWANATH SRIKANTH
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Network Models ◽  
Neural Mechanism ◽  
Computational Simulations ◽  
Trajectory Prediction ◽  
Neural Network Models ◽  
Visual Objects ◽  
Curved Trajectories ◽  
Self Organizing

Existing neural network models are capable of tracking linear trajectories of moving visual objects. This paper describes an additional neural mechanism, disfacilitation, that enhances the ability of a visual system to track curved trajectories. The added mechanism combines information about an object's trajectory with information about changes in the object's trajectory, to improve the estimates for the object's next probable location. Computational simulations are presented that show how the neural mechanism can learn to track the speed of objects and how the network operates to predict the trajectories of accelerating and decelerating objects.

scholarly journals Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

2018 ◽  
Vol 13 (No. 1) ◽  
pp. 11-17 ◽  
Author(s):  
M. Mokarram ◽  
M. Najafi-Ghiri ◽  
A.R. Zarei
Keyword(s):  
Neural Network ◽  
Soil Fertility ◽  
Network Models ◽  
The Other ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Soil Features ◽  
Close Relationship ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

A HIGH-ORDER GRAPH GENERATING SELF–ORGANIZING STRUCTURE

2005 ◽  
Vol 15 (05) ◽  
pp. 349-355
Author(s):  
RICCARDO RIZZO
Keyword(s):  
Neural Network ◽  
Large Class ◽  
General Structure ◽  
Network Models ◽  
High Order ◽  
Neural Network Models ◽  
Fixed Topology ◽  
High Level ◽  
High Level Abstraction ◽  
Self Organizing

A large class of neural network models have their units organized in a lattice with fixed topology or generate their topology during the learning process. These network models can be used as neighborhood preserving map of the input manifold, but such a structure is difficult to manage since these maps are graphs with a number of nodes that is just one or two orders of magnitude less than the number of input points (i.e., the complexity of the map is comparable with the complexity of the manifold) and some hierarchical algorithms were proposed in order to obtain a high-level abstraction of these structures. In this paper a general structure capable to extract high order information from the graph generated by a large class of self–organizing networks is presented. This algorithm will allow to build a two layers hierarchical structure starting from the results obtained by using the suitable neural network for the distribution of the input data. Moreover the proposed algorithm is also capable to build a topology preserving map if it is trained using a graph that is also a topology preserving map.

31P MRS Data Analysis of Liver Cancer Based on Neural Networks

2010 ◽  
Vol 20-23 ◽  
pp. 630-635
Author(s):  
Qiang Liu ◽  
Ning Wang ◽  
Yi Hui Liu ◽  
Shao Qing Wang ◽  
Jin Yong Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Cancer ◽  
Back Propagation ◽  
Network Models ◽  
Resonance Spectroscopy ◽  
Self Organizing Map ◽  
Neural Network Models ◽  
31P Mrs ◽  
Hepatic Diseases

31P MRS(31Phosphorus Magnetic Resonance Spectroscopy) is a non invasive protocol for analyzing the energetic metabolism and biomedical changes in cellular level. Evaluation of 31P MRS is important in diagnosis and treatment of many hepatic diseases. In this paper, we apply back-propagation neural network (BP) and self-organizing map (SOM) neural network to analyze 31P MRS data to distinguish three diagnostic classes of cancer, normal and cirrhosis tissue. 66 samples of 31P MRS data are selected including cancer, normal and cirrhosis tissue. Four experiments are carried out. Good performance is achieved with limited samples. Experimental results prove that neural network models based on 31P MRS data offer an alternative and promising technique for diagnostic prediction of liver cancer in vivo.

Sign in / Sign up

Export Citation Format

Share Document