New research group 'Bio-inspired Circuits & Systems'

On August 1st, 2020, Elisabetta Chicca joined the Zernike Institute for Advanced Materials and the Groningen Cognitive Systems and Materials Center (CogniGron) at the University of Groningen who will lead the new research group 'Bio-inspired Circuits & Systems'.

Elisabetta Chicca studied physics at the University of Rome 1 ‘La Sapienza’‚ Italy, where she graduated in 1999. In 2006 she received a PhD in Natural Sciences from the Physics department of the Federal Institute of Technology Zurich (ETHZ), Switzerland, and a PhD in Neuroscience from the Neuroscience Center Zurich (ZNZ). Immediately after the PhD, she started a PostDoc at the Institute of Neuroinformatics at the University of Zurich and ETH Zurich, where she continued working as Research Group Leader from May 2010 to August 2011. From August 2011, Elisabetta was leading the 'Neuromorphic Behaving Systems' group at Bielefeld University in Germany where she was appointed as full professor.

The overall goal of her research is to identify the principles of neural computation and implement them in fully parallel and low-power neuromorphic very-large-scale integration (VLSI) systems for solving computational problems by learning from examples and experience. The focus of her research in the fields of neuromorphic systems and theories for brain inspired computation and neuromorphic sensing and actuating. In close collaboration with the material and device research groups at Zernike she also aims at investigating new technological solutions. This research has high potential for developing compact low-power real-time systems with natural application to the field of autonomous robotics.

Alongside Elisabetta Chicca, three PhDs from Bielefeld University are joining the group to continue their PhD research in Groningen:

Philipp Klein's PhD evolves around advancing the learning behavior of spiking neural networks. He received his bachelor degree in Biomimetics from the Westphalian University of Applied Sciences, Germany and his master degree in Computer Science from Bielefeld University, Germany. He will investigate how biologically inspired learning schemes, such as spike-timing dependent plasticity and hebbian learning can be integrated into analog neuromorphic CMOS applications. Philipp also has a high interest in combining conventional CMOS circuits with novel technologies and devices to facilitate the integration of large scale neural networks.

Michele Mastella is a PhD student working in the field of neuromorphic embedded processing for touch as part of the H2020 Marie Skłodowska-Curie action Innovative Training Networks - Understanding neural coding of touch as enabling technology for prosthetics and robotics (NeuTouch). Michele has received his bachelor and master degree in Electronics Engineering from Politecnico di Milano, Italy. His main interests are analog circuit design, neural networks and information processing. During his PhD Michele will design, simulate, fabricate and characterize neuromorphic circuits for spike-based computation of tactile sensory data and evalute network performances in close collaboration with the research themes focussing on computational neuroscience and sensor development. To facilitate this collaboration he will conduct various visits to partner institutions of the Innovative Training Network.

In his PhD Thorben Schoepe tries to understand insect navigation and will develop a computational model including collision avoidance. He received his bachelor degree in Biomimetics from the Westphalian University of Applied Sciences, Germany and his master degree in Biomechatronic from University of Applied Sciences Bielefeld and Bielefeld University, Germany. In close collaboration with biologists he is developing a computaitonal system which exhibits interesting behaviors comparable to that of the biological counterpart. This will allow to verify the hypothesis about the compuational role of bits and pieces of the insect brain. The model will be transferred into a spiking neural network employed on a robot.