Deep Learning Seminar  /  October 08, 2020, 10:00 – 11:00

Computational Neuroscience vs. Neural Computation


In my talk I will share a personal point of view regarding the complementary but distinct disciplines of neural computation and computational neuroscience. Though these terms have been used interchangeably in the past, creating clear distinction between the two can help sharpen the understanding of the current state of the research on brain and AI. In this informal conversation I would like to share insights about assumptions, goals and recent breakthroughs of these disciplines, attempting to spot challenges and opportunities from their interaction.


Curriculum Vitae

Dr. Mario Negrello obtained a mechanical engineering degree in Brazil (1997), and later after a period in the industry (VW 1999-2004) obtained his Masters degree (2006) and PhD (summa cum laude) in Cognitive Science at the University of Osnabrück in Germany, in 2009. At the Fraunhofer Institute in Sankt Augustin (Germany) for Intelligent Dynamics and Autonomous Systems, he researched artificial evolution of neural network controllers for autonomous robots (2007/08). This work was awarded a scholarship by the International Society of Neural Networks (INNS) to sponsor an eight-month period (2008/09) as a visiting researcher at the Computational Synthesis Lab at the Aerospace Engineering department of the Cornell University in USA (with Hod Lipson). In his first post doctoral period he acted a group leader at the Computational Neuroscience laboratory at the Okinawa Institute of Science and Technology (with Erik De Schutter). He now is assistant professor in computational neuroscience in the Erasmus Medical Center in Rotterdam, where he combines empirical research and computational models (dept. head Chris De Zeeuw). He has published in the fields of Machine Learning and Cognitive Robotics, Artificial Life, Evolutionary Robotics, Neuroethology and Neuroscience, as well as a monograph published by Springer US in the Series Cognitive and Neural systems entitled Invariants of Behavior (2012).