Symposium SB04-Materials and Algorithms for Neuromorphic Computing and Adaptive Bio-Interfacing, Sensing and Actuation

It is becoming well-known that traditional computing systems are unable to capture the efficiency of the brain in information processing. The computational primitives of biological neural networks on device and circuit level is the first step towards efficient neuromorphic computing systems that are able to analyze, interpret, perceive and act upon a dynamic, real-world environment. Thus, a new era of smart sensor and actuation applications is emerging with systems that perceive and interact with the world and efficiently couple with biological environments. Nevertheless, such intelligent agents also require novel algorithmic support in a co-design fabric. Allowing actual biological substrates to compute is an even longer-term approach to directly harness the biological level of computational efficiency. However, this approach requires materials, devices and systems that would be able to interface biology in a smart and dynamic way beyond signal acquisition. In this symposium, the latest advancements of inorganic and organic materials for bio-inspired information processing and bio-computation will be covered. Emerging applications will be showcased in neuromorphic computing, sensing, actuation and bio-interfacing along with recent advancements in algorithmic development. This symposium aspires to bring together world-wide experts in the fields of neuromorphic computing, bioelectronics and neuroscience in order to enhance transdisciplinary interactions and thus bridge the gaps between materials science, computing and neuroscience by initiating a dialogue around the proposed emerging topic.

• Bio-inspired information processing
• Neuromorphic computing
• Computational primitives for neuromorphic engineering
• Inorganic and organic materials for neuromorphic devices
• Neuromorphic sensing and actuation
• Adaptive bio-interfacing
• Neural interface devices
• Memristive materials / devices at the interface with biology
• Bioelectronics
• Systems neuroscience
• Algorithmic advances for neuro-inspired computing and smart sensing
• Algorithm-hardware co-design for neuro-inspired computing

• Magnus Berggren (Linköping University, Sweden)
• Kevin Cao (Arizona State University, USA)
• Bianxiao Cui (Stanford University, USA)
• Jullie Grollier (CNRS/Thales, France)
• Daniele Ielmini (Politecnico di Milano, Italy)
• Sahika Inal (King Abdullah University of Science and Technology, Saudi Arabia)
• Dion Khodagholy (Columbia University, USA)
• Hans Kleemann (Technische Universität Dresden, Germany)
• Tae-Woo Lee (Seoul National University, Republic of Korea)
• Hai Li (Duke University, USA)
• George Malliaras (University of Cambridge, United Kingdom)
• Robert Nawrocki (Purdue University, USA)
• Emre Neftci (University of California, Irvine, USA)
• Beatriz Noheda (University of Groningen, Netherlands)
• Andreas Offenhäusser (Forschungszentrum Jülich, Germany)
• Jonathan Rivnay (Northwestern University, USA)
• Jacob Robinson (Rice University, USA)
• Tajana Rosing (University of California, San Diego, USA)
• Kaushik Roy (Purdue University, USA)
• Jennifer Rupp (Massachusetts Institute of Technology, USA)
• Alberto Salleo (Stanford University, USA)
• Molly Stevens (Imperial College London, United Kingdom)
• Benjamin Tee (National University of Singapore, Singapore)
• Fabrizio Torricelli (Università degli Studi di Brescia, Italy)
• Stefano Vassanelli (Università degli Studi di Padova, Italy)
• Qiangfei Xia (University of Massachusetts Amherst, USA)