Dr. Paschalis Gkoupidenis
Paschalis Gkoupidenis earned his PhD in materials science from NCSR “Demokritos”, Athens, Greece, in 2014. During his PhD, his research focused on ionic transport mechanisms of organic electrolytes, physics of ionic-based devices, and of non-volatile memories. Following his PhD, in 2015 he joined the group of George Malliaras at the Department of Bioelectronics (EMSE, France) as a postdoctoral researcher. At the Department of Bioelectronics, his research focused on the design and development of organic neuromorphic devices based on electrochemical concepts. In 2017, Paschalis Gkoupidenis joined the Max Planck Institute for Polymer Research, and he is currently a Group Leader at the Department of Molecular Electronics.
Hardware-based implementation of neuromorphic architectures offers efficient ways of data manipulation and processing, especially in data intensive applications such as big data analysis and real-time processing. In contrast to traditional von Neumann architectures, neuro-inspired devices may offer promising solutions in interacting with human sensory data and processing of information in real time. Therefore, such kind of devices may offer in the future novel ways of data manipulation in bioelectronics. Harnessing brain efficiency at the technological level can be condensed to the term “brain-inspired computing”. Over the past years, organic materials and devices have drawn significant attention due to their attractive characteristics for neuro-inspired devices and bioelectronics such as their ability to operate in electrolytes, spatiotemporal response, analogue memory phenomena, tunability via chemical synthesis, low-cost fabrication processes and biocompatibility. Our research interest focuses on the development of neuro-inspired information processing functions and architectures with organic electrochemical devices which are traditionally being used in bioelectronics. Our group is investigating various concepts for inducing neuroplasticity, learning forms and spatiotemporal information processing functions at a single-device level as well as new paradigms of neuromorphic architectures at circuit level. Such neuro-inspired functions are essential for trainable/adaptable circuits in energy restricted environments, as well as for local signal processing in bioelectronics.