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{{Event | {{Event | ||
|Acronym=IJCNN 2020 | |Acronym=IJCNN 2020 | ||
| − | |Title=IEEE International Joint Conference on Neural Networks | + | |Title=2020 IEEE International Joint Conference on Neural Networks |
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|In Event Series=Event Series:IJCNN | |In Event Series=Event Series:IJCNN | ||
|Single Day Event=no | |Single Day Event=no | ||
| Line 14: | Line 7: | ||
|End Date=2020/07/24 | |End Date=2020/07/24 | ||
|Event Status=as scheduled | |Event Status=as scheduled | ||
| − | |Event Mode= | + | |Event Mode=online |
| + | |Academic Field=Neural Networks | ||
| + | |Official Website=https://wcci2020.org/ | ||
| + | |DOI=10.25798/bxpy-0r69 | ||
| + | |Type=Conference | ||
| + | |pageCreator=User:Curator 89 | ||
| + | |pageEditor=User:Curator 59 | ||
| + | |contributionType=1 | ||
}} | }} | ||
{{Event Deadline}} | {{Event Deadline}} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=International Neural Network Society (INNS) | ||
| + | }} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers | ||
| + | }} | ||
| + | {{Event Metric}} | ||
{{S Event}} | {{S Event}} | ||
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. | 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. | ||
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NEURAL NETWORK MODELS | NEURAL NETWORK MODELS | ||
| − | * Feedforward neural networks | + | *Feedforward neural networks |
| − | * Recurrent neural networks | + | *Recurrent neural networks |
| − | * Self-organizing maps | + | *Self-organizing maps |
| − | * Radial basis function networks | + | *Radial basis function networks |
| − | * Attractor neural networks and associative memory | + | *Attractor neural networks and associative memory |
| − | * Modular networks | + | *Modular networks |
| − | * Fuzzy neural networks | + | *Fuzzy neural networks |
| − | * Spiking neural networks | + | *Spiking neural networks |
| − | * Reservoir networks (echo-state networks, liquid-state machines, etc.) | + | *Reservoir networks (echo-state networks, liquid-state machines, etc.) |
| − | * Large-scale neural networks | + | *Large-scale neural networks |
| − | * Learning vector quantization | + | *Learning vector quantization |
| − | * Deep neural networks | + | *Deep neural networks |
| − | * Randomized neural networks | + | *Randomized neural networks |
| − | * Other topics in artificial neural networks | + | *Other topics in artificial neural networks |
MACHINE LEARNING | MACHINE LEARNING | ||
| − | * Supervised learning | + | *Supervised learning |
| − | * Unsupervised learning and clustering, (including PCA, and ICA) | + | *Unsupervised learning and clustering, (including PCA, and ICA) |
| − | * Reinforcement learning and adaptive dynamic programming | + | *Reinforcement learning and adaptive dynamic programming |
| − | * Semi-supervised learning | + | *Semi-supervised learning |
| − | * Online learning | + | *Online learning |
| − | * Probabilistic and information-theoretic methods | + | *Probabilistic and information-theoretic methods |
| − | * Support vector machines and kernel methods | + | *Support vector machines and kernel methods |
| − | * EM algorithms | + | *EM algorithms |
| − | * Mixture models, ensemble learning, and other meta-learning or committee algorithms | + | *Mixture models, ensemble learning, and other meta-learning or committee algorithms |
| − | * Bayesian, belief, causal, and semantic networks | + | *Bayesian, belief, causal, and semantic networks |
| − | * Statistical and pattern recognition algorithms | + | *Statistical and pattern recognition algorithms |
| − | * Sparse coding and models | + | *Sparse coding and models |
| − | * Visualization of data | + | *Visualization of data |
| − | * Feature selection, extraction, and aggregation | + | *Feature selection, extraction, and aggregation |
| − | * Evolutionary learning | + | *Evolutionary learning |
| − | * Hybrid learning methods | + | *Hybrid learning methods |
| − | * Computational power of neural networks | + | *Computational power of neural networks |
| − | * Deep learning | + | *Deep learning |
| − | * Other topics in machine learning | + | *Other topics in machine learning |
NEURODYNAMICS | NEURODYNAMICS | ||
| − | * Dynamical models of spiking neurons | + | *Dynamical models of spiking neurons |
| − | * Synchronization and temporal correlation in neural networks | + | *Synchronization and temporal correlation in neural networks |
| − | * Dynamics of neural systems | + | *Dynamics of neural systems |
| − | * Chaotic neural networks | + | *Chaotic neural networks |
| − | * Dynamics of analog networks | + | *Dynamics of analog networks |
| − | * Itinerant dynamics in neural systems | + | *Itinerant dynamics in neural systems |
| − | * Neural oscillators and oscillator networks | + | *Neural oscillators and oscillator networks |
| − | * Dynamics of attractor networks | + | *Dynamics of attractor networks |
| − | * Other topics in neurodynamics | + | *Other topics in neurodynamics |
COMPUTATIONAL NEUROSCIENCE | COMPUTATIONAL NEUROSCIENCE | ||
| − | * Connectomics | + | *Connectomics |
| − | * Models of large-scale networks in the nervous system | + | *Models of large-scale networks in the nervous system |
| − | * Models of neurons and local circuits | + | *Models of neurons and local circuits |
| − | * Models of synaptic learning and synaptic dynamics | + | *Models of synaptic learning and synaptic dynamics |
| − | * Models of neuromodulation | + | *Models of neuromodulation |
| − | * Brain imaging | + | *Brain imaging |
| − | * Analysis of neurophysiological and neuroanatomical data | + | *Analysis of neurophysiological and neuroanatomical data |
| − | * Cognitive neuroscience | + | *Cognitive neuroscience |
| − | * Models of neural development | + | *Models of neural development |
| − | * Models of neurochemical processes | + | *Models of neurochemical processes |
| − | * Neuroinformatics | + | *Neuroinformatics |
| − | * Brain Informatics | + | *Brain Informatics |
| − | * Other topics in computational neuroscience | + | *Other topics in computational neuroscience |
NEURAL MODELS OF PERCEPTION, COGNITION AND ACTION | NEURAL MODELS OF PERCEPTION, COGNITION AND ACTION | ||
| − | * Neurocognitive networks | + | *Neurocognitive networks |
| − | * Cognitive architectures | + | *Cognitive architectures |
| − | * Models of conditioning, reward and behavior | + | *Models of conditioning, reward and behavior |
| − | * Cognitive models of decision-making | + | *Cognitive models of decision-making |
| − | * Embodied cognition | + | *Embodied cognition |
| − | * Cognitive agents | + | *Cognitive agents |
| − | * Multi-agent models of group cognition | + | *Multi-agent models of group cognition |
| − | * Developmental and evolutionary models of cognition | + | *Developmental and evolutionary models of cognition |
| − | * Visual system | + | *Visual system |
| − | * Auditory system | + | *Auditory system |
| − | * Olfactory system | + | *Olfactory system |
| − | * Other sensory systems | + | *Other sensory systems |
| − | * Attention | + | *Attention |
| − | * Learning and memory | + | *Learning and memory |
| − | * Spatial cognition, representation and navigation | + | *Spatial cognition, representation and navigation |
| − | * Semantic cognition and language | + | *Semantic cognition and language |
| − | * Grounding, symbol grounding | + | *Grounding, symbol grounding |
| − | * Neural models of symbolic processing | + | *Neural models of symbolic processing |
| − | * Reasoning and problem-solving | + | *Reasoning and problem-solving |
| − | * Working memory and cognitive control | + | *Working memory and cognitive control |
| − | * Emotion and motivation | + | *Emotion and motivation |
| − | * Motor control and action | + | *Motor control and action |
| − | * Dynamical models of coordination and behavior | + | *Dynamical models of coordination and behavior |
| − | * Consciousness and awareness | + | *Consciousness and awareness |
| − | * Models of sleep and diurnal rhythms | + | *Models of sleep and diurnal rhythms |
| − | * Mental disorders | + | *Mental disorders |
| − | * Other topics in neural models of perception, cognition and action | + | *Other topics in neural models of perception, cognition and action |
NEUROENGINEERING | NEUROENGINEERING | ||
| − | * Brain-machine interfaces | + | *Brain-machine interfaces |
| − | * Neural prostheses | + | *Neural prostheses |
| − | * Neuromorphic hardware | + | *Neuromorphic hardware |
| − | * Embedded neural systems | + | *Embedded neural systems |
| − | * Other topics in neuroengineering | + | *Other topics in neuroengineering |
BIO-INSPIRED AND BIOMORPHIC SYSTEMS | BIO-INSPIRED AND BIOMORPHIC SYSTEMS | ||
| − | * Brain-inspired cognitive architectures | + | *Brain-inspired cognitive architectures |
| − | * Embodied robotics | + | *Embodied robotics |
| − | * Evolutionary robotics | + | *Evolutionary robotics |
| − | * Developmental robotics | + | *Developmental robotics |
| − | * Computational models of development | + | *Computational models of development |
| − | * Collective intelligence | + | *Collective intelligence |
| − | * Swarms | + | *Swarms |
| − | * Autonomous complex systems | + | *Autonomous complex systems |
| − | * Self-configuring systems | + | *Self-configuring systems |
| − | * Self-healing systems | + | *Self-healing systems |
| − | * Self-aware systems | + | *Self-aware systems |
| − | * Emotional computation | + | *Emotional computation |
| − | * Artificial life | + | *Artificial life |
| − | * Other topics in bio-inspired and biomorphic systems | + | *Other topics in bio-inspired and biomorphic systems |
APPLICATIONS | APPLICATIONS | ||
| − | * Applications of deep neural networks | + | *Applications of deep neural networks |
| − | * Bioinformatics | + | *Bioinformatics |
| − | * Biomedical engineering | + | *Biomedical engineering |
| − | * Data analysis and pattern recognition | + | *Data analysis and pattern recognition |
| − | * Speech recognition and speech production | + | *Speech recognition and speech production |
| − | * Robotics | + | *Robotics |
| − | * Neurocontrol | + | *Neurocontrol |
| − | * Approximate dynamic programming, adaptive critics, and Markov decision processes | + | *Approximate dynamic programming, adaptive critics, and Markov decision processes |
| − | * Neural network approaches to optimization | + | *Neural network approaches to optimization |
| − | * Signal processing, image processing, and multi-media | + | *Signal processing, image processing, and multi-media |
| − | * Temporal data analysis, prediction, and forecasting; time series analysis | + | *Temporal data analysis, prediction, and forecasting; time series analysis |
| − | * Communications and computer networks | + | *Communications and computer networks |
| − | * Data mining and knowledge discovery | + | *Data mining and knowledge discovery |
| − | * Power system applications | + | *Power system applications |
| − | * Financial engineering applications | + | *Financial engineering applications |
| − | * Security applications | + | *Security applications |
| − | * Applications in multi-agent systems and social computing | + | *Applications in multi-agent systems and social computing |
| − | * Manufacturing and industrial applications | + | *Manufacturing and industrial applications |
| − | * Expert systems | + | *Expert systems |
| − | * Clinical applications | + | *Clinical applications |
| − | * Big data applications | + | *Big data applications |
| − | * Other applications | + | *Other applications |
| − | * Smart grid applications | + | *Smart grid applications |
CROSS-DISCIPLINARY TOPICS | CROSS-DISCIPLINARY TOPICS | ||
| − | * Hybrid intelligent systems | + | *Hybrid intelligent systems |
| − | * Swarm intelligence | + | *Swarm intelligence |
| − | * Sensor networks | + | *Sensor networks |
| − | * Quantum computation | + | *Quantum computation |
| − | * Computational biology | + | *Computational biology |
| − | * Molecular and DNA computation | + | *Molecular and DNA computation |
| − | * Computation in tissues and cells | + | *Computation in tissues and cells |
| − | * Artificial immune systems | + | *Artificial immune systems |
| − | * Philosophical issues | + | *Philosophical issues |
| − | * Other cross-disciplinary topics | + | *Other cross-disciplinary topics |
Latest revision as of 13:23, 8 March 2024
Deadlines
Metrics
Venue
Warning: Venue is missing. The map might not show the exact location.
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
