Quantitative assessment of vocal development in the zebra finch using self-organizing neural networks

AbstractVocal behavior can be dramatically changed by both neural circuit development and postnatal maturation of the body. During song learning in songbirds, both the song system and syringeal muscles are functionally changing, but it is unknown if maturation of sound generators within the syrinx contributes to vocal development. Here we densely sample the respiratory pressure control space of the zebra finch syrinx in vitro. We show that the syrinx produces sound very efficiently and that key acoustic parameters, minimal fundamental frequency, entropy and source level, do not change over development in both sexes. Thus, our data suggest that the observed acoustic changes in vocal development must be attributed to changes in the motor control pathway, from song system circuitry to muscle force, and not by material property changes in the avian analog of the vocal folds. We propose that in songbirds, muscle use and training driven by the sexually dimorphic song system are the crucial drivers that lead to sexual dimorphism of the syringeal skeleton and musculature. The size and properties of the instrument are thus not changing, while its player is.

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Spam Review Detection Using Self-organizing Maps and Convolutional Neural Networks

Computers & Security ◽

10.1016/j.cose.2021.102274 ◽

2021 ◽

pp. 102274

Author(s):

Ashraf Neisari ◽

Luis Rueda ◽

Sherif Saad

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Self Organizing Maps ◽

Self Organizing

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Embedding Multiple Trajectories in Simulated Recurrent Neural Networks in a Self-Organizing Manner

Journal of Neuroscience ◽

10.1523/jneurosci.2358-09.2009 ◽

2009 ◽

Vol 29 (42) ◽

pp. 13172-13181 ◽

Cited By ~ 53

Author(s):

J. K. Liu ◽

D. V. Buonomano

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Self Organizing

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Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Sustainability ◽

10.3390/su13158295 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8295

Author(s):

Patricia Melin ◽

Oscar Castillo

Keyword(s):

Neural Networks ◽

Time Series ◽

Temporal Trends ◽

Main Idea ◽

Temporal Analysis ◽

Virus Propagation ◽

Self Organizing Maps ◽

Fractal Approach ◽

The Times ◽

Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

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