The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.
The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of: spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatio- temporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, MLP. Future development and applications are discussed
Early event prediction challenges most of existing modeling methods especially when dealing with complex spatio-temporal data. In this paper we propose a new method for predictive data modelling based on a new development of the recently proposed NeuCube spiking neural network architecture, called here NeuCube (ST) . The NeuCube uses a Spiking Neural Network reservoir (SNNr) and dynamic evolving Spiking Neuron Network (deSNN) classifier. NeuCube (ST) is an integrated environment including data conversion into spike trains, input variable mapping, unsupervised learning in the SNNr, supervised classification learning, activity visualization and network structure analysis. A case study on a real world ecological data set is presented to demonstrate the validity of the proposed method.
This paper discusses the proposed model of the collaborative virtual learning system for the introductory computer programming course which uses one of the collaborative learning techniques known as the "Think-Pair-Share". The main objective of this study is to design a model for an online learning system that facilitates the collaborative learning activities in a virtual environment such as online communications and pair or small group discussions. In order to model the virtual learning environment, the RUP methodology has been used where it involves the data collection phase and the analysis and design phase. Fifty respondents have been randomly selected to participate in the data collection phase to investigate the students' interest and learning styles as well as their learning preferences. The results have shown the needs for the development of online small group discussions that can be used as an alternative learning style for programming courses. The proposed design of the virtual learning system named as the Online Collaborative Learning System or OCLS is being depicted using the objectoriented models which are the use-case model and class diagram in order to show the concise processes of virtual "Think-Pair-Share" collaborative activities. The "Think-Pair-Share" collaborative learning technique that is being used in this model has been chosen because of its simplicity and relatively low-risk. This paper also presents the proposed model of the system's architecture that will become the guidelines for the physical development of OCLS using the web-based applications.
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