Welcome to ICANN 2019

17th – 19th September, 2019, Munich, Germany

The 28th International Conference on Artificial Neural Networks.
A conference of the European Neural Network Society.

Conference proceedings are published by Springer in Lecture Notes in Computer Science.

The proceedings are now published as Springer Lecture Notes in Computer Science Series, volumes 11727, 11728, 11729, 11730, and 11731.


  • ICANN 2020 will be in Bratislava, Slovakia, September 15-18, 2020. Please find the announcement here.
  • ICANN2019 and Springer Open Access collaboration. The authors of articles/abstracts submitted to the BIGCHEM Special Session are qualified for a 25% discount on the journal’s article-processing charge for the special issue of J. Cheminformatics. To be qualified for the discount one of the authors should participate to the ICANN2019 and the article should be submitted to the special issue before the start of the conference. The article submitted to J. Cheminformatics will be fully peer-reviewed and should comply to the usual publishing ethics.

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.


Platinum sponsors

ARGMAX.ai, Volkswagen Group ML Research

Bronze Sponsors


Conference topics

ICANN 2019 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

Deep Learning

  • Neural Network Theory
  • Neural Network Models
  • Graphical Models
  • Bayesian Networks
  • Kernel Methods
  • Generative Models
  • Information Theoretic Learning
  • Reinforcement Learning
  • Relational Learning
  • Dynamical Models
  • Recurrent Networks.

Brain Inspired Computing

  • Cognitive models
  • Computational Neuroscience
  • Self-organisation
  • Reinforcement Learning
  • Neural Control and Planning
  • Hybrid Neural-Symbolic Architectures
  • Neural Dynamics

Neural Applications for

  • Bioinformatics
  • Biomedicine
  • Intelligent Robotics
  • Neurorobotics
  • Language Processing
  • Image Processing
  • Sensor Fusion
  • Pattern Recognition
  • Data Mining
  • Neural Agents
  • Brain-Computer Interaction
  • Neural Hardware
  • Evolutionary Neural Networks

Special Session BIGCHEM: Big Data and AI in chemistry

  • Big Data analysis in chemistry
  • Chemoinformatics
  • Use of deep learning to predict molecular properties
  • Drug-discovery
  • Modelling and prediction of chemical reaction data
  • Synthetic route prediction
  • Structure generation
  • Molecular dynamics simulations and quantum chemistry

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