Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Spiking neurons and the induction of finite state machines
Theoretical Computer Science - Natural computing
The evidence for neural information processing with precise spike-times: A survey
Natural Computing: an international journal
Learning to Decode Cognitive States from Brain Images
Machine Learning
Polychronization: Computation with Spikes
Neural Computation
Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
Computational Neurogenetic Modeling
Computational Neurogenetic Modeling
Editorial: Recent advances in brain-machine interfaces
Neural Networks
Applications of spiking neural networks
Information Processing Letters - Special issue on applications of spiking neural networks
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Real-time epileptic seizure detection on intra-cranial rat data using reservoir computing
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Towards spatio-temporal pattern recognition using evolving spiking neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A systematic method for configuring vlsi networks of spiking neurons
Neural Computation
Reservoir-based evolving spiking neural network for spatio-temporal pattern recognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots
IEEE Transactions on Autonomous Mental Development
Probabilistic Computational Neurogenetic Modeling: From Cognitive Systems to Alzheimer's Disease
IEEE Transactions on Autonomous Mental Development
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Integrating neural networks and chaotic measurements for modelling epileptic brain
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input STBD or the stimuli data is presented, thus acting as an associative memory. The NeuCube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships 'hidden' in the data. NeuCube will allow the integration in one model of various brain data, information and knowledge, related to a single subject (personalized modeling) or to a population of subjects. The use of NeuCube for classification of STBD is illustrated in a case study problem of EEG data. NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques. They are robust to noise (so typical in brain data) and facilitate a better interpretation of the results and understanding of the STBD and the brain conditions under which data was collected. Future directions for the use of SNN for STBD are discussed.