Welcome to ICANN 2016
The 25th International Conference on Artificial Neural Networks.
A conference of the European Neural Network Society.
- Hurry up, our submission is closing in a few days! Information about submission here and on the online submission system.
- Registration open!
- The list of keynote speakers and the Second Announcement are now available.
Aims and scope
The International Conference on Artificial Neural Networks (ICANN) is the annual flagship conference of the European Neural Network Society (ENNS). The ideal of ICANN is to bring together researchers from two worlds: information sciences and neurosciences. The scope is wide, ranging from machine learning algorithms to models of real nervous systems. The aim is to facilitate discussions and interactions in the effort towards developing more intelligent computational systems and increasing our understanding of neural and cognitive processes in the brain.
BarcelonaTech (Universitat Politècnica de Catalunya), Barcelona, Spain.
ICANN 2016 will feature two main tracks: Brain inspired computing and Machine learning research, with strong cross-disciplinary interactions and applications. All research fields dealing with Neural Networks will be present at the Conference with emphasis on “Neural Coding”, “Decision Making” and “Unsupervised Learning”.
Scheduled Sessions are:
1. Theoretical Neural Computation
2. Information and Optimization
3. From Neurons to Neuromorphism
4. Spiking Dynamics
5. From Single Neurons to Networks
6. Complex Firing Patterns
7. Movement and Motion
8. From Sensation to Perception
9. Object and Face Recognition
10. Reinforcement Learning
11. Bayesian and Echo State Networks
12. Recurrent Neural Networks and Reservoir Computing
13. Coding Architectures
14. Interacting with The Brain
15. Swarm Intelligence and Decision-Making
16. Multilayer Perceptrons and Kernel Networks
17. Training and Learning
18. Inference and Recognition
19. Support Vector Machines
20. Self-Organizing Maps and Clustering
21. Clustering, Mining and Exploratory Analysis
23. Time Series and Forecasting