<html><head><meta http-equiv="Content-Type" content="text/html charset=windows-1252"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><div><br></div><div><div>Dear colleagues,<br><br>I am happy to announce the release of the latest issue of the IEEE CIS Newsletter on Cognitive and Developmental Systems (open access).<br>This is a biannual newsletter addressing the sciences of developmental and cognitive processes in natural and artificial organisms, from humans to robots, at the crossroads of cognitive science, developmental psychology, machine intelligence and neuroscience. <br><br>It is available at: <a href="http://goo.gl/MLijbn">http://goo.gl/MLijbn</a></div><div><br>Featuring dialog:<br>=== "What is Computational Reproducibility?"<br>== Dialog initiated by Olivia Guest and Nicolas Rougier</div><div>with responses from: Konrad Hinsen, Sharon Crooke, Gaël Varoquaux, Todd Gureckis and Alexander Rich, Robert French and Caspar Addyman, and Celeste Kidd.<br>== Topic: Computational models of cognitive and developmental living systems need to address several major challenges in order to achieve scientific impact: reproducibility, replicability, but also reusability in an interdisciplinary community. Indeed, one needs to ensure that models’ implementations and experimentations match their high-level specifications. </div><div>£%nIt is also key to conduct alternative implementations and experimentations to distinguish which aspects of these models are key concepts, and which others are tools for experimenting these concepts. Last but not least, models should be understandable and reusable by other researchers who are not always themselves computational experts, which is facilitated when they are delivered in a way that allows non-experts to directly “play” with these models.<br><br>Call for new dialog:<br>=== "Exploring Robotic Minds by Predictive Coding Principle"<br>== Dialog initiated by Jun Tani<br>== In a new dialog initiation, Jun Tani, who has been studying recurrent neural networks models of sensorimotor development for the last 20 years, asks which ingredients are needed to enable neural architectures with capabilities of infant-like learning and development. In particular, he observes that recent deep learning advances are still lacking infant-like capabilities for learning incrementally from very little data, and asks whether computational models of staged development could enable progress towards infant-like lifelong deep learning. He also discusses the potential role of the predictive coding principle in development. Those of you interested in reacting to this dialog initiation are welcome to submit a response by May 30th, 2017. The length of each response must be between 600 and 800 words including references (contact <a href="mailto:pierre-yves.oudeyer@inria.fr">pierre-yves.oudeyer@inria.fr</a>).</div><div><br>Let me remind you that all issues of the newsletter are all open-access and available at: <a href="http://icdl-epirob.org/cdsnl">http://icdl-epirob.org/cdsnl</a><br><br>I wish you a stimulating reading!<br><br>Best regards,<br><br>Pierre-Yves Oudeyer,<br><br>Editor of the IEEE CIS Newsletter on Cognitive and Developmental Systems<br>Research director, Inria<br>Head of Flower project-team<br>Inria and Ensta ParisTech, France<br><a href="http://www.pyoudeyer.com">http://www.pyoudeyer.com</a><br><a href="https://flowers.inria.fr">https://flowers.inria.fr</a><br><a href="http://www.poppy-project.org">http://www.poppy-project.org</a></div><div>Twitter: <a href="https://twitter.com/pyoudeyer">https://twitter.com/pyoudeyer</a></div></div><div><br></div><div>and </div><div><br></div><div>Fabien Benureau</div><div>Assistand Editor </div><div>Inria Mnemosyne team</div><div>Inria, France</div><div><br></div></body></html>