Conferences related to Computational Neurogenetic Modeling

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2019 International Joint Conference on Neural Networks (IJCNN)

IJCNN covers a wide range of topics in the field of neural networks, from biological neural network modeling to artificial neural computation.


2018 International Conference on Intelligent Systems (IS)

Recent advances and challenges in the theory, design and applications of broadly perceived intelligent systems, including technology business management aspects.

  • 2016 IEEE 8th International Conference on Intelligent Systems (IS)

    Building upon the success of IS’02, IS’04, IS’08, IS‘12 (held in Sofia and Varna, Bulgaria), IS’06, IS’10 (held in London, UK), and IS’14 (held in Warsaw, Poland) the 8th IEEE International Conference on Intelli¬gent Systems IS’16 shall continue the tradition of bringing to¬gether top specialists in the broad area of intelligent systems. This forum is an opportunity for scientists from all over the world to share ideas and achieve¬ments in the theory and practice of intel¬ligent control, artificial intelligence, decision sup¬port systems, neu¬ral networks, soft computing, data mining and knowledge discovery, ontologies, machine learning, intelligent measurement, etc.

  • 2014 IEEE 7th International Conference Intelligent Systems (IS)

    The conference is mainly concerned with recent advances and challenges in the theory, design and applications of broadly perceived intelligent systems. With respect to tools and techniques, a special emphasis will be on modern artificial intelligence, computational intelligence (fuzzy sets, rough sets, evolutionary computation, neural networks, swarm intelligence, etc.), calculi of uncertainty, nonstandard logics, data analysis, data mining and knowledge discovery, machine learning, information aggregation and fusion, multimedia information processing, Web technology and intelligence, cognitive and affective computing, multiagent systems, ontologies for intelligent systems, "big data", etc. Main application areas include, but are not limited to: bioinformatics, business and finance, intelligent decision support systems, database systems, e-learning, e-administration, environmental engineering, healthcare, security, sensors, automation, mobile robotics, manufacturing systems, logistics,

  • 2012 6th IEEE International Conference Intelligent Systems (IS)

    This forum is an opportunity for scientists from all over the world to share ideas and achievements in the theory and practice of intelligent control, intelligent information security systems, artificial intelligence, decision support systems, neural networks, soft computing, data mining and knowledge discovery, ontologies, machine learning, intelligent measurement, etc.

  • 2010 5th IEEE International Conference Intelligent Systems (IS)

    This conference is an opportunity for scientists and industrialists from all over the world to share ideas and achievements in the theory and practice of soft computing, intelligent databases, intelligent information systems, business intelligence, data mining and knowledge discovery, ontologies, intelligent control, decision support systems, neural networks, machine learning and intelligent measurement.

  • 2008 4th IEEE International Conference Intelligent Systems (IS)

    This forum is an opportunity for scientists from all over the world to share ideas and achievements in the theory and practice of intelligent control, artificial intelligence, decision support systems, neural networks, soft computing, data mining and knowledge discovery, ontologies, machine learning, intelligent measurements, etc.

  • 2006 3rd International IEEE Conference Intelligent Systems (IS)

  • 2004 2nd International IEEE Conference "Intelligent Systems" (IS)

  • 2002 1st International IEEE Symposium "Intelligent Systems" (IS)


2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

GEFS2013 will provide an opportunity to meet researchers working on the topic, make new contacts and exchange ideas. The GEFS series of workshops are an important part of the activities of the Evolutionary Fuzzy Systems Task Force of the Fuzzy System Technical Committee (IEEE Computational Intelligence Society).

  • 2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

    Genetic and evolutionary fuzzy systems meld the approximate reasoning method of fuzzy systems with the adaptation capabilities of evolutionary algorithms. The objective of GEFS2010 is to facilitate the promotion of novel problems, research, results, and future directions in the latter growing area.

  • 2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)

    One of the most prominent approaches to hybridize fuzzy systems with learning and adaptation methods has resulted in the emergence of genetic and evolving fuzzy systems, which combine the approximate reasoning method of fuzzy systems with the adaptation capabilities of evolutionary algorithms. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans.

  • 2006 International Symposium on Evolving Fuzzy Systems (EFS)


2011 International Conference on Adaptive and Intelligent Systems (ICAIS)

- Self-X Systems - Incremental Learning - Online Processing - Dynamic and Evolving Models in Computational Intelligence - Applications

  • 2009 International Conference on Adaptive and Intelligent Systems (ICAIS)

    ICAIS strives to deepen understanding of various concepts pertaining to self-adaptation of systems evolving in dynamically changing environment. Typical topics of ICAIS include online learning, online pattern recognition techniques (online clustering, classification, regression, feature selection, etc.), self-growing, self-monitoring, self-correction, self-organization and other concepts which are at the heart of system adaptation.



Periodicals related to Computational Neurogenetic Modeling

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Computational Neurogenetic Modeling

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Xplore Articles related to Computational Neurogenetic Modeling

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Probabilistic Computational Neurogenetic Modeling: From Cognitive Systems to Alzheimer's Disease

IEEE Transactions on Autonomous Mental Development, 2011

The paper proposes a novel research framework for building probabilistic computational neurogenetic models (pCNGM). The pCNGM is a multilevel modeling framework inspired by the multilevel information processes in the brain. The framework comprises a set of several dynamic models, namely low (molecular) level models, a more abstract dynamic model of a protein regulatory network (PRN) and a probabilistic spiking neural ...


Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques

IEEE International Workshop on Biomedical Circuits and Systems, 2004., 2004

The paper presents a theory and a new generic computational model of a biologically plausible artificial neural network (ANN) that can mimic certain brain neuronal ensembles, the dynamics of which is influenced by the dynamics of internal gene regulatory networks (GRN). We call these models "computational neurogenetic models" (CNGM) and this new area of research computational neurogenetics. We are aiming ...


Computational Neurogenetic Modeling: A Methodology to Study Gene Interactions Underlying Neural Oscillations

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006

We present new results from computational neurogenetic modeling to aid discoveries of complex gene interactions underlying oscillations in neural systems. Interactions of genes in neurons affect the dynamics of the whole neural network model through neuronal parameters, which change their values as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and neuronal ...


Neuro-, Genetic-, and Quantum Inspired Evolving Intelligent Systems

2006 International Symposium on Evolving Fuzzy Systems, 2006

This paper discusses opportunities and challenges for the creation of evolving artificial neural network (ANN) and more general computational intelligence (CI) models inspired by principles at different levels of information processing in the brain - neuronal-, genetic-, and quantum - and mainly the issues related to the integration of these principles into more powerful and accurate ANN models. A particular ...


Bioinformatics: a knowledge engineering approach

2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791), 2004

The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other knowledge representation procedures. Examples of the KE approach, and especially of one of the recently developed techniques - evolving connectionist systems (ECOS), to challenging problems in bioinformatics are ...


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Educational Resources on Computational Neurogenetic Modeling

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IEEE.tv Videos

Brooklyn 5G Summit 2014: Modeling the Indoor Radio Propagation with Dr. K Haneda
EMBC 2012 Theme Speaker: Dr. James Bassingthwaighte
IROS TV 2019- Rutgers University- Center for Accelerated Real Time Analytics
Uncovering the Neural Code of Learning Control - Jennie Si - WCCI 2012 invited lecture
BSIM Spice Model Enables FinFET and UTB IC Design
EMBC 2011-Keynote Lectures and Panel Discussion-PT I-Subra Suresh
Toward Cognitive Integration of Prosthetic Devices - IEEE WCCI 2014
IROS TV 2019- Macau- Episode 2- Robots Connecting People
IMS 2012 Microapps - Practical Electromagnetic Modeling of Parallel Plate Capacitors at High Frequency
Brooklyn 5G Summit 2014: Jonas Medbo on 5G Channel Modeling Challenges
Brooklyn 5G Summit: Critical Modeling Aspects and Their Effect on System Design and Performance
Data Modeling using Kernels and Information Theoretic Learning
APEC 2012 - Dr. Fred Lee Plenary
Brooklyn 5G Summit 2014: Tommi Jamsa on METIS Channel Modeling Activities
Using Computational Intelligence to automate Craniofacial Superimposition for Skeleton-based Human Identification
Applications of Computational Intelligence in Biomedicine
IMS 2015: Luca Pierantoni - A New Challenge in Computational Engineering
An Introduction to Computational Intelligence in Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff
Computational Intelligence for Brain Computer Interface
IMS 2011 Microapps - Understanding the Proper Dielectric Constant of High Frequency Laminates to Be Used for Circuit Modeling and Design

IEEE-USA E-Books

  • Probabilistic Computational Neurogenetic Modeling: From Cognitive Systems to Alzheimer's Disease

    The paper proposes a novel research framework for building probabilistic computational neurogenetic models (pCNGM). The pCNGM is a multilevel modeling framework inspired by the multilevel information processes in the brain. The framework comprises a set of several dynamic models, namely low (molecular) level models, a more abstract dynamic model of a protein regulatory network (PRN) and a probabilistic spiking neural network model (pSNN), all linked together. Genes/proteins from the PRN control parameters of the pSNN and the spiking activity of the pSNN provides feedback to the PRN model. The overall spatio-temporal pattern of spiking activity of the pSNN is interpreted as the highest level state of the pCNGM. The paper demonstrates that this framework can be used for modeling both artificial cognitive systems and brain processes. In the former application, the pCNGM utilises parameters that correspond to sensory elements and neuromodulators. In the latter application a pCNGM uses data obtained from relevant genes/proteins to model their dynamic interaction that matches data related to brain development, higher-level brain function or disorder in different scenarios. An exemplar case study on Alzheimer's Disease is presented. Future applications of pCNGM are discussed.

  • Computational neurogenetic modeling: integration of spiking neural networks, gene networks, and signal processing techniques

    The paper presents a theory and a new generic computational model of a biologically plausible artificial neural network (ANN) that can mimic certain brain neuronal ensembles, the dynamics of which is influenced by the dynamics of internal gene regulatory networks (GRN). We call these models "computational neurogenetic models" (CNGM) and this new area of research computational neurogenetics. We are aiming at developing a novel computational modeling paradigm and also at bringing original insights into how genes and their interactions influence the function of brain neural networks in normal and diseased states. Both brain activity and an ANN model can be analyzed using same signal processing techniques and then compared. In the proposed model, FFT and spectral characteristics of the ANN behavior are analyzed and compared with the brain EEG signal. The model will include a large set of biologically plausible parameters and functions related to genes/proteins, spiking neuronal activities, etc., which define the GRN and the corresponding ANN. These parameters will be optimized, based for instance on targeted EEG data, through using evolutionary algorithms. The paper also offers a list of open questions in the field of CNGM. It outlines directions for further research.

  • Computational Neurogenetic Modeling: A Methodology to Study Gene Interactions Underlying Neural Oscillations

    We present new results from computational neurogenetic modeling to aid discoveries of complex gene interactions underlying oscillations in neural systems. Interactions of genes in neurons affect the dynamics of the whole neural network model through neuronal parameters, which change their values as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and neuronal parameters, particular target states of the neural network operation can be achieved, and statistics about gene interaction matrix can be extracted. In such a way it is possible to model the role of genes and their interactions in different brain states and conditions. Experiments with human EEG data are presented as an illustration of this methodology and also, as a source for the discovery of unknown interactions between genes in relation to their impact on brain activity.

  • Neuro-, Genetic-, and Quantum Inspired Evolving Intelligent Systems

    This paper discusses opportunities and challenges for the creation of evolving artificial neural network (ANN) and more general computational intelligence (CI) models inspired by principles at different levels of information processing in the brain - neuronal-, genetic-, and quantum - and mainly the issues related to the integration of these principles into more powerful and accurate ANN models. A particular type of ANN, evolving connectionist systems (ECOS), is used to illustrate this approach. ECOS evolve their structure and functionality through continuous learning from data and facilitate data and knowledge integration and knowledge elucidation. ECOS gain inspiration from the evolving processes in the brain. Evolving fuzzy neural networks and evolving spiking neural networks are presented as examples. With more genetic information available now, it becomes possible to integrate the gene and the neuronal information into neuro-genetic models and to use them for a better understanding of complex brain processes. Further down in the information processing hierarchy are the quantum processes. Quantum inspired ANN may help solve efficiently the hardest computational problems. It may be possible to integrate quantum principles into brain-gene inspired ANN models for a faster and more accurate modeling. All the topics above are illustrated with some contemporary solutions, but many more open questions and challenges are raised and directions for further research outlined

  • Bioinformatics: a knowledge engineering approach

    The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other knowledge representation procedures. Examples of the KE approach, and especially of one of the recently developed techniques - evolving connectionist systems (ECOS), to challenging problems in bioinformatics are given, that include: DNA sequence analysis, microarray gene expression profiling, protein structure prediction, finding gene regulatory networks, medical prognostic systems, computational neurogenetic modeling.

  • Proceedings of the International Joint Conference on Neural Networks 2005 (IEEE Cat. No. 05CH37663C)

    The following topics are dealt with: neurodynamics and intentional dynamic systems; neural networks applications to bioinformatics; information-theoretic and Bayesian learning; ICA and PCA; models of neurons, local circuits and systems; evolvable and emergent neural systems; control and system identification; spiking neurons; computational neurogenetic modeling; advancements in adaptive resonance theory; genomics applications; proteomics and neuroinformatics and data mining, text and pattern recognition.



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