IJCNN 2020: Difference between revisions
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{{Event | {{Event | ||
|Acronym=IJCNN 2020 | |Acronym=IJCNN 2020 | ||
|Title=IEEE International Joint Conference on Neural Networks | |||
|Series=IJCNN | |Series=IJCNN | ||
| | |Type=Conference | ||
|Start date=2020/07/19 | |||
|End date=2020/07/24 | |||
|Homepage=https://wcci2020.org/ijcnn-sessions/ | |||
|City=Glasgow | |City=Glasgow | ||
|Country=UK | |Country=UK | ||
}} | }} | ||
The International Joint Conference on Neural Networks (IJCNN) covers a wide range of topics in the field of neural networks, from biological neural networks to artificial neural computation. | |||
List of topics: | |||
NEURAL NETWORK MODELS | |||
* Feedforward neural networks | |||
* Recurrent neural networks | |||
* Self-organizing maps | |||
* Radial basis function networks | |||
* Attractor neural networks and associative memory | |||
* Modular networks | |||
* Fuzzy neural networks | |||
* Spiking neural networks | |||
* Reservoir networks (echo-state networks, liquid-state machines, etc.) | |||
* Large-scale neural networks | |||
* Learning vector quantization | |||
* Deep neural networks | |||
* Randomized neural networks | |||
* Other topics in artificial neural networks | |||
MACHINE LEARNING | |||
* Supervised learning | |||
* Unsupervised learning and clustering, (including PCA, and ICA) | |||
* Reinforcement learning and adaptive dynamic programming | |||
* Semi-supervised learning | |||
* Online learning | |||
* Probabilistic and information-theoretic methods | |||
* Support vector machines and kernel methods | |||
* EM algorithms | |||
* Mixture models, ensemble learning, and other meta-learning or committee algorithms | |||
* Bayesian, belief, causal, and semantic networks | |||
* Statistical and pattern recognition algorithms | |||
* Sparse coding and models | |||
* Visualization of data | |||
* Feature selection, extraction, and aggregation | |||
* Evolutionary learning | |||
* Hybrid learning methods | |||
* Computational power of neural networks | |||
* Deep learning | |||
* Other topics in machine learning | |||
NEURODYNAMICS | |||
* Dynamical models of spiking neurons | |||
* Synchronization and temporal correlation in neural networks | |||
* Dynamics of neural systems | |||
* Chaotic neural networks | |||
* Dynamics of analog networks | |||
* Itinerant dynamics in neural systems | |||
* Neural oscillators and oscillator networks | |||
* Dynamics of attractor networks | |||
* Other topics in neurodynamics | |||
COMPUTATIONAL NEUROSCIENCE | |||
* Connectomics | |||
* Models of large-scale networks in the nervous system | |||
* Models of neurons and local circuits | |||
* Models of synaptic learning and synaptic dynamics | |||
* Models of neuromodulation | |||
* Brain imaging | |||
* Analysis of neurophysiological and neuroanatomical data | |||
* Cognitive neuroscience | |||
* Models of neural development | |||
* Models of neurochemical processes | |||
* Neuroinformatics | |||
* Brain Informatics | |||
* Other topics in computational neuroscience | |||
NEURAL MODELS OF PERCEPTION, COGNITION AND ACTION | |||
* Neurocognitive networks | |||
* Cognitive architectures | |||
* Models of conditioning, reward and behavior | |||
* Cognitive models of decision-making | |||
* Embodied cognition | |||
* Cognitive agents | |||
* Multi-agent models of group cognition | |||
* Developmental and evolutionary models of cognition | |||
* Visual system | |||
* Auditory system | |||
* Olfactory system | |||
* Other sensory systems | |||
* Attention | |||
* Learning and memory | |||
* Spatial cognition, representation and navigation | |||
* Semantic cognition and language | |||
* Grounding, symbol grounding | |||
* Neural models of symbolic processing | |||
* Reasoning and problem-solving | |||
* Working memory and cognitive control | |||
* Emotion and motivation | |||
* Motor control and action | |||
* Dynamical models of coordination and behavior | |||
* Consciousness and awareness | |||
* Models of sleep and diurnal rhythms | |||
* Mental disorders | |||
* Other topics in neural models of perception, cognition and action | |||
NEUROENGINEERING | |||
* Brain-machine interfaces | |||
* Neural prostheses | |||
* Neuromorphic hardware | |||
* Embedded neural systems | |||
* Other topics in neuroengineering | |||
BIO-INSPIRED AND BIOMORPHIC SYSTEMS | |||
* Brain-inspired cognitive architectures | |||
* Embodied robotics | |||
* Evolutionary robotics | |||
* Developmental robotics | |||
* Computational models of development | |||
* Collective intelligence | |||
* Swarms | |||
* Autonomous complex systems | |||
* Self-configuring systems | |||
* Self-healing systems | |||
* Self-aware systems | |||
* Emotional computation | |||
* Artificial life | |||
* Other topics in bio-inspired and biomorphic systems | |||
APPLICATIONS | |||
* Applications of deep neural networks | |||
* Bioinformatics | |||
* Biomedical engineering | |||
* Data analysis and pattern recognition | |||
* Speech recognition and speech production | |||
* Robotics | |||
* Neurocontrol | |||
* Approximate dynamic programming, adaptive critics, and Markov decision processes | |||
* Neural network approaches to optimization | |||
* Signal processing, image processing, and multi-media | |||
* Temporal data analysis, prediction, and forecasting; time series analysis | |||
* Communications and computer networks | |||
* Data mining and knowledge discovery | |||
* Power system applications | |||
* Financial engineering applications | |||
* Security applications | |||
* Applications in multi-agent systems and social computing | |||
* Manufacturing and industrial applications | |||
* Expert systems | |||
* Clinical applications | |||
* Big data applications | |||
* Other applications | |||
* Smart grid applications | |||
CROSS-DISCIPLINARY TOPICS | |||
* Hybrid intelligent systems | |||
* Swarm intelligence | |||
* Sensor networks | |||
* Quantum computation | |||
* Computational biology | |||
* Molecular and DNA computation | |||
* Computation in tissues and cells | |||
* Artificial immune systems | |||
* Philosophical issues | |||
* Other cross-disciplinary topics | |||
Latest revision as of 13:58, 6 March 2020
| IJCNN 2020 | |
|---|---|
IEEE International Joint Conference on Neural Networks
| |
| Event in series | IJCNN |
| Dates | 2020/07/19 (iCal) - 2020/07/24 |
| Homepage: | https://wcci2020.org/ijcnn-sessions/ |
| Location | |
| Location: | Glasgow, UK |
| Table of Contents | |
The International Joint Conference on Neural Networks (IJCNN) covers a wide range of topics in the field of neural networks, from biological neural networks to artificial neural computation.
List of topics:
NEURAL NETWORK MODELS
- Feedforward neural networks
- Recurrent neural networks
- Self-organizing maps
- Radial basis function networks
- Attractor neural networks and associative memory
- Modular networks
- Fuzzy neural networks
- Spiking neural networks
- Reservoir networks (echo-state networks, liquid-state machines, etc.)
- Large-scale neural networks
- Learning vector quantization
- Deep neural networks
- Randomized neural networks
- Other topics in artificial neural networks
MACHINE LEARNING
- Supervised learning
- Unsupervised learning and clustering, (including PCA, and ICA)
- Reinforcement learning and adaptive dynamic programming
- Semi-supervised learning
- Online learning
- Probabilistic and information-theoretic methods
- Support vector machines and kernel methods
- EM algorithms
- Mixture models, ensemble learning, and other meta-learning or committee algorithms
- Bayesian, belief, causal, and semantic networks
- Statistical and pattern recognition algorithms
- Sparse coding and models
- Visualization of data
- Feature selection, extraction, and aggregation
- Evolutionary learning
- Hybrid learning methods
- Computational power of neural networks
- Deep learning
- Other topics in machine learning
NEURODYNAMICS
- Dynamical models of spiking neurons
- Synchronization and temporal correlation in neural networks
- Dynamics of neural systems
- Chaotic neural networks
- Dynamics of analog networks
- Itinerant dynamics in neural systems
- Neural oscillators and oscillator networks
- Dynamics of attractor networks
- Other topics in neurodynamics
COMPUTATIONAL NEUROSCIENCE
- Connectomics
- Models of large-scale networks in the nervous system
- Models of neurons and local circuits
- Models of synaptic learning and synaptic dynamics
- Models of neuromodulation
- Brain imaging
- Analysis of neurophysiological and neuroanatomical data
- Cognitive neuroscience
- Models of neural development
- Models of neurochemical processes
- Neuroinformatics
- Brain Informatics
- Other topics in computational neuroscience
NEURAL MODELS OF PERCEPTION, COGNITION AND ACTION
- Neurocognitive networks
- Cognitive architectures
- Models of conditioning, reward and behavior
- Cognitive models of decision-making
- Embodied cognition
- Cognitive agents
- Multi-agent models of group cognition
- Developmental and evolutionary models of cognition
- Visual system
- Auditory system
- Olfactory system
- Other sensory systems
- Attention
- Learning and memory
- Spatial cognition, representation and navigation
- Semantic cognition and language
- Grounding, symbol grounding
- Neural models of symbolic processing
- Reasoning and problem-solving
- Working memory and cognitive control
- Emotion and motivation
- Motor control and action
- Dynamical models of coordination and behavior
- Consciousness and awareness
- Models of sleep and diurnal rhythms
- Mental disorders
- Other topics in neural models of perception, cognition and action
NEUROENGINEERING
- Brain-machine interfaces
- Neural prostheses
- Neuromorphic hardware
- Embedded neural systems
- Other topics in neuroengineering
BIO-INSPIRED AND BIOMORPHIC SYSTEMS
- Brain-inspired cognitive architectures
- Embodied robotics
- Evolutionary robotics
- Developmental robotics
- Computational models of development
- Collective intelligence
- Swarms
- Autonomous complex systems
- Self-configuring systems
- Self-healing systems
- Self-aware systems
- Emotional computation
- Artificial life
- Other topics in bio-inspired and biomorphic systems
APPLICATIONS
- Applications of deep neural networks
- Bioinformatics
- Biomedical engineering
- Data analysis and pattern recognition
- Speech recognition and speech production
- Robotics
- Neurocontrol
- Approximate dynamic programming, adaptive critics, and Markov decision processes
- Neural network approaches to optimization
- Signal processing, image processing, and multi-media
- Temporal data analysis, prediction, and forecasting; time series analysis
- Communications and computer networks
- Data mining and knowledge discovery
- Power system applications
- Financial engineering applications
- Security applications
- Applications in multi-agent systems and social computing
- Manufacturing and industrial applications
- Expert systems
- Clinical applications
- Big data applications
- Other applications
- Smart grid applications
CROSS-DISCIPLINARY TOPICS
- Hybrid intelligent systems
- Swarm intelligence
- Sensor networks
- Quantum computation
- Computational biology
- Molecular and DNA computation
- Computation in tissues and cells
- Artificial immune systems
- Philosophical issues
- Other cross-disciplinary topics