Workshop on

Novel Substrates and Models for the Emergence of
Developmental, Learning and Cognitive Capabilities

9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB 2019), Oslo, Norway

Workshop Date: August 19th, 2019 - Engineer's House Conference Centre, Oslo, Norway
Paper submission deadline: May 1st, 2019


 Organizers

Stefano Nichele, Oslo Metropolitan University, Oslo, Norway


Jianhua Zhang, Oslo Metropolitan University, Oslo, Norway



Scope and call for papers

ICDL-EpiRob is a unique conference gathering researchers from the domains of computer science, robotics, psychology and developmental studies to share knowledge and new research results on how humans and animals develop sensing, perception, reasoning, and actions.

In order for sensing, reasoning, development and learning to take place in artificial systems/beings, computing substrates and models that support the emergence of such properties are required.

This workshop encompasses the understanding, analysis, modeling and development of novel substrates that show development and learning abilities, such as (but not limited to):

  • Cellular automata and random boolean networks,
  • Reservoir computing,
  • Novel nanoscale materials (e.g. carbon nanotubes),
  • Biological materials and systems (e.g. neuronal cultures),
  • Evolvable hardware and neuromorphic computing systems,
  • Novel neural models and models of biological neuron,
  • Evolution-in-materio systems,
  • Computational matter,
  • Slime mould computing,
  • Micro- and nano-scale electronic chemistry,
  • Substrates that exhibit self-reconfiguration, fault tolerance, self-repair, and adaptation capabilities,
  • Artificial generative and developmental systems,
  • Computational intelligence techniques for novel developmental and learning substrates
  • Learning through self-organization and emergence


Workshop program (Engineer's House Conference Centre, Kronprinsensgate 17, 0251 Oslo)

08:55 - Welcome
09:00 - Keynote 1: Evolvable neural networks and attractor dynamics. Alessandro E.P. Villa
09:30 - Adaptive difficulty adjustment for task assignment in online learning environments and adjustment of remembering methods (PDF). Yazidi, Goodwin, Hammer, Mofrad, Arntzen
09:45 - Classification of mental workload levels using EEG and hybrid model of stacked denoising autoencoder (PDF). Cao, Yin, Zhang
10:00 - Theory for autonomous functional learning (PDF). Mau
10:15 - Instantaneous mental workload assessment using wavelet-packet analysis and semi-supervised learning classifier (PDF). Zhang, Li, Wang
10:30 - Coffee Break
10:50 - Computing with magnetic field in carbon nanotube tissue (PDF). Laketic, Tufte
11:05 - Exploration and exploitation of computational capabilities in in-vitro biological neural networks (PDF). Aaser, Ramstad, Tufte, Sandvig
11:20 - A general representation of dynamical systems for reservoir computing (PDF). Pontes-Filho, Yazidi, Zhang, Hammer, Mello, Sandvig, Tufte, Nichele
11:35 - Evaluation of the criticality of in vitro neuronal networks: toward and assessment of computational capacity (PDF). Heiney, Valderhaug, Sandvig, Tufte, Hammer, Nichele
11:50 - DeepTEGINN: Deep learning based tools to extract graphs from images of neural networks (PDF). Mello, Valderhaug, Pontes-Filho, Zouganeli, Sandvig, Nichele
12:05 - Keynote 2: Developmental Approaches to Artificial Neural Network Models. Julian Miller
12:35 - Lunch


Keynote presentations

  • Julian Francis Miller, Honorary Fellow, University of York, UK

    Developmental Approaches to Artificial Neural Network Models
    Brains are created by a process of biological development in which neurons and neurites proliferate and change in response to environmental stimuli. Indeed, there is abundant evidence that topological change is a powerful aspect of learning in brains and is responsible for the brain's ability to solve multiple problems. In contrast artificial neural network models have been idealised as fixed networks of neurons in which learning happens solely through weight adjustment. Such models struggle to solve multiple problems because of catastrophic forgetting whereby learning on one problem is undone by training on another. This talk concerns a new model of artificial neural networks in which two programs are evolved which control neurons and dendrites. The programs allow dendrites and neurons to die, replicate and change. The programs are represented using Cartesian Genetic Programming. Catastrophic forgetting is avoided and the new approach allows multiple traditional ANNs each solving a different computational problem to be extracted from the underlying artificial brain.



  • Alessandro E.P. Villa, Full Professor, University of Lausanne, Switzerland

    Evolvable neural networks and attractor dynamics
    The sequence of spikes of a neuron carries important information processed by the brain and depends on the subsequent connectivity within the cell assemblies. The topology of such connectivity is the outcome of a neurodevelopmental process associated with genetic as well as context-dependent processes. An association between spatiotemporal patterns of neural discharges and chaotic attractor dynamics was observed in experimental, theoretical and large scale neuronal networks simulations with embedded neuro-developmental features. This talk presents the latest findings of this approach, where the state of the network at any time is represented by the values of control parameters and particular invariant states referred to as attractor states.



Submission Instructions

We welcome original contributions describing ongoing projects / completed work (6 pages paper) or preliminary results / novel ideas (3/4 pages brief paper or extended abstract). The instructions for authors and templates can be found at https://icdl-epirob2019.org/submission/. All contributions accepted to the workshop will be invited to submit extended version for a special issue in the journal of Cognitive Neurodynamics, published by Springer. Submissions will be peer reviewed consistent with the journal practices. The papers/abstracts accepted to the workshop will be also published on the workshop webpage.

Submission page: https://ras.papercept.net/conferences/scripts/start.pl#ICDLER19, select -Submit a contribution to ICDL-EPIROB 2019-, then select -Workshop Paper, Submit-, then use the following workshop code: kqxb4

If you have any problems with the submission system, please contact kaiolae@ifi.uio.no

Important dates

Paper submission: 01 May 2019
Author notification: 01 June 2019
Camera ready version: 01 July 2019


Contacts

Please feel free to contact us:
Stefano Nichele: stenic@oslomet.no
Jianhua Zhang: jianhuaz@oslomet.no


About the organizers

Stefano Nichele is an Associate Professor at the Department of Computer Science at the Oslo Metropolitan University, where he is the founder and deputy head of the Applied Artificial Intelligence (AI^2) research group. He has recently started the Living Technology Lab, where he uses an artificial life and complex systems approach to intelligence. In addition, he is member of the Autonomous Systems and Networks (ASN) research group. Nichele is the founder and interim chair of the IEEE Computational Intelligence Society Norway. His research interests include Artificial Life, Complex Systems, Biological and Artificial Neural Networks, Reservoir Computing, and Neuro-evolution. Nichele holds a PhD from the Norwegian University of Science and Technology, Trondheim, Norway.

Webpage: http://www.nichele.eu/

Prof. Jianhua Zhang received his MSc degree in Control Theory and Engineering from the Beijing University of Technology, Beijing, China in 1996 and PhD degree in Electrical Engineering and Information Sciences from the Ruhr University Bochum, Bochum, Germany in 2005. Subsequently he was Postdoctoral Research Associate at Intelligent Systems Research Lab, The University of Sheffield, Sheffield, UK from 2005 to 2006. From 2007 to 2017 he was a Professor and Head of Intelligent Systems Group at School of Information Science and Engineering, East China University of Science and Technology (ECUST), Shanghai, China. In 2017 he joined VEKIA (Lille, France) as R&D Scientific Director and Head of Machine Learning Research Lab. Since Sep. 2018 he has been a Professor of AI at Applied AI Group, Department of Computer Science, Oslo Metropolitan University (OsloMet), Norway. He also held research positions of Guest Scientist at Technische Universität Dresden, Germany from 2002 to 2003, invited Adjunct Professor at Institute for Cognitive Neurodynamics of ECUST from 2009 to 2017, and Visiting Professor at Technische Universität Berlin and Max Planck Institute for Dynamics of Complex Technical Systems, Germany, between 2008 and 2015.

Webpage: http://jzhang.academic.bio/



Program Committee
  • Johannes Jensen, NTNU, Norway
  • Zhong Yin, University of Shanghai for Science and Technology, China
  • Anis Yazidi, Oslo Metropolitan University, Norway
  • Tamsyn E. Edwards, NASA Ames Research Center/ San Jose State University, USA
  • Matthew Dale, University of York, UK
  • Odd Rune Lykkebø, NTNU, Norway
  • Sven Nömm, Tallinn University of Technology, Estonia
  • Gunnar Tufte, NTNU, Norway
  • Frederic Vanderhaegen, University of Valenciennes, France
  • Dragana Laketic, ARM, Norway
  • Julian Francis Miller, University of York, UK
  • Jochen Mau, University of Duesseldorf, Germany