Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational device. Artificial neural networks, however, rely on human-designed, hand-programmed architectures for their remarkable performance. Can we develop artificial computational devices that can grow and self-organize without human intervention? In this paper, we propose a biologically inspired developmental algorithm that can'grow' a functional, layered neural network from a single initial cell. The algorithm organizes inter-layer connections to construct retinotopic pooling layers.

Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational device. Artificial neural networks, however, rely on human-designed, hand-programmed architectures for their remarkable performance. Can we develop artificial computational devices that can grow and self-organize without human intervention? In this paper, we propose a biologically inspired developmental algorithm that can'grow' a functional, layered neural network from a single initial cell. The algorithm organizes inter-layer connections to construct retinotopic pooling layers.

Every five years, computers become 10 times cheaper – something that has been happening since 1941. This is according to professor Jürgen Schmidhuber, director of the Artificial Intelligence Initiative at King Abdullah University of Science and Technology, and scientific director of Swiss AI Lab IDSIAI (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale). Schmidhuber this week delivered a keynote presentation on the future of self-improving artificial intelligence (AI) and neural networks at the second Global AI Summit in Riyadh, Saudi Arabia. The professor's AI lab's deep learning neural networks based on ideas published in his paper, the "Annus Mirabilis" in 1990-1991, have revolutionised machine learning and AI. According to Schmidhuber, who is known as the father of modern-day AI, technology has advanced so rapidly over the years that PCs today are significantly cheaper than they were in the past.

Classification of hand drawn sketches with respect to content quality is extremely challenging task, comparing to usual image classification methods. In brief, we need to train computational device to able to classify the images of the same object into different classes with respect their content quality. In this paper we tested several methods of image classification, using machine learning and computer vision algorithms, to classify Draw-a-Person test images sketched by primary school students in Nigeria, aged 4 to 11 years. We collected 1000 original sketches and manually classified them (using guidelines from existing literature) according to the ages (8 classes) before testing this dataset on a computational device. The highest accuracy achieved in this experiment was 62%.

Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational device. Artificial neural networks, however, rely on human-designed, hand-programmed architectures for their remarkable performance. Can we develop artificial computational devices that can grow and self-organize without human intervention? In this paper, we propose a biologically inspired developmental algorithm that can'grow' a functional, layered neural network from a single initial cell. The algorithm organizes inter-layer connections to construct retinotopic pooling layers.

In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to track wireless networks to ensure the integrity and confidentiality of the information transmitted. Therefore, it is necessary to quickly and efficiently identify the normal and abnormal traffic of such networks, so that administrators can take action. This work aims to analyze classification techniques in relation to data from Wireless Networks, using some classes of anomalies pre-established according to some defined criteria of the MAC layer. For data analysis, WEKA Data Mining software (Waikato Environment for Knowledge Analysis) is used. The classification algorithms present a success rate in the classification of viable data, being indicated in the use of intrusion detection systems for wireless networks.