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Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Self-organizing maps
Pulsed neural networks
Information Sciences—Informatics and Computer Science: An International Journal
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The Handbook of Brain Theory and Neural Networks
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Spiking Neuron Models: An Introduction
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Quantum Computing (Natural Computing Series)
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Advances in Design and Application of Spiking Neural Networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
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Computational Neurogenetic Modeling
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Natural Computing: an international journal
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IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
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ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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Evolving Intelligence in Humans and Machines: Integrative Evolving Connectionist Systems Approach
IEEE Computational Intelligence Magazine
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IEEE Transactions on Neural Networks
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ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
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Integrative evolving connectionist systems (iECOS) integrate principles from different levels of information processing in the brain, including cognitive-, neuronal-, genetic- and quantum, in their dynamic interaction over time. The paper introduces a new framework of iECOS called integrative probabilistic evolving spiking neural networks (ipSNN) that incorporate probability learning parameters. ipSNN utilize a quantum inspired evolutionary optimization algorithm to optimize the probability parameters as these algorithms belong to the class of estimation of distribution algorithms (EDA). Both spikes and input features in ipESNN are represented as quantum bits being in a superposition of two states (1 and 0) defined by a probability density function. This representation allows for the state of an entire ipESNN at any time to be represented probabilistically in a quantum bit register and probabilistically optimised until convergence using quantum gate operators and a fitness function. The proposed ipESNN is a promising framework for both engineering applications and brain data modeling as it offers faster and more efficient feature selection and model optimization in a large dimensional space in addition to revealing new knowledge that is not possible to obtain using other models. Further development of ipESNN are the neuro-genetic models - ipESNG, that are introduced too, along with open research questions.