Deep learning nowadays is the "buzz word": on my first postdoc, I have found out how deep learning stolen the scene in a matter of years, since I last worked directly with machine learning; I left for a while for working with white-box models in mathematical physiology, appetite control. More or less on the same time deep learning was leaving the underworld, we had spiking neural network; I came across that model by professor Kasabov. Deep learning is a set of artificial neural network. Being straight to the point: it is huge number of hidden layers on a multilayered perceptron (MLP). What does make those techniques (i.e., SNN) so different from what we already have and what may set them apart on future applications?

Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks [1]. In Stream Learning (SL), aka data stream mining or machine learning for data streams, applications (such as mobile phones, sensor networks, industrial process controls and intelligent user interfaces, among others) generate huge amounts of data in the form of fast streams, acquiring special relevance with the advent of the Big Data and IoT era. In these scenarios, algorithms cannot explicitly access all historical data because the storage capacity needed for this purpose becomes unmanageable. Indeed, data streams are fast and large (potentially, infinite), so information must be extracted from them in real-time, being therefore necessary to learn in an online manner [2]. Besides, some of these scenarios produce non-stationary data streams which are becoming increasingly prevalent, and where the process generating the data may change over time, producing changes in the patterns to be modeled (concept drift).

Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts.

AUT's Artificial Intelligence knows your choices before you do Picture standing in your local dairy, indecisively scanning the drinks fridge to choose which can you're going to buy. If you were hooked up to NeuCube, it would be able to predict which one you'd reach for – before you'd opened the fridge, and even before the thought was fully formed. Using artificial intelligence to predict a person's choices before they have even made up their mind is a world first - and it was developed at Auckland University of Technology (AUT). Professor Nikola Kasabov, who worked on the project, said they wanted to find out if there was brain activity before the conscious perception of an object. READ MORE: * Artificial intelligence presents us with an opportunity, and a challenge * Don't fear the new AI revolution * How computers'see' faces and other objects * How science may just end up killing crime There is, it turns out.

The author has performed an excellent job in explaining the fundamental ideas and practical methods of different AI techniques. AI problems in the field ( pattern recognition, speech and image processing, classification, planning, optimization, control, time-series and analogy-based prediction, diagnosis, decision making and game simulations) are discussed and illustrated with examples . Especially useful are the comparisons between different techniques (AI rule Cbased methods, fuzzy methods, connectionist methods, and hybrid systems for knowledge engineering) used to solve the same or similar problems. The presented text is suitable for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering.The book¡ s appendices summarize data sets for the examples in the book. All data sets are available through anonymous FTP.

This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern recognition, a better and earlier event prediction and a better understanding of complex temporal stream data through visualization of the NeuCube connectivity. The effect of the new mapping is demonstrated on three bench mark problems. The first one is early prediction of patient sleep stage event from temporal physiological data. The second one is pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set. In all cases the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared to traditional machine learning techniques or spiking neural network reservoirs with arbitrary mapping of the variables.