Organic materials and devices for brain-inspired computing: From artificial implementation to biophysical realism
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Introduction Data-driven information is everywhere today and springs from vast sources, ranging from the internet to bodily functions. Yet developing meaningful ways of information extraction, processing, and representation remains a necessity. Such information management would not only be meaningful, but it also needs to conform to the societal needs for sustainability. Any technological attempt at energy-efficient management of information can be traced back to the never-ending quest to understand and imitate the inner workings of the brain that gives rise to intelligence. Harnessing brain efficiency at the technological level can be condensed to the term “braininspired computing.” An intelligent agent is a system that perceives and interacts with the environment in order to achieve its goals in an autonomous and rational manner. This interaction is dynamic, therefore intelligent agents are adaptive and with learning capabilities that improve their performance over time.
Interaction is also bidirectional, which means that a fully functional system consists of sensors to acquire data from the environment, processing units to perceive the environment, and actuators that act upon that environment. Nevertheless, the borderline between these elements is blurred, as properties of one element can fuse to another. Indeed, this also applies to living organisms wherein sensing, processing, and actuation are not centralized into a single entity, but rather are distributed all over the body.1 Intelligent agents can take the form of software or hardware. A popular approach for software-based agents, is the representation of information processing aspects found in biological systems with artificial neural networks (ANNs), a field commonly known as machine learning or artificial intelligence. This approach is based on executing algorithms that loosely represent the function of the nervous system, on traditional computer architectures. Today, ANNs have spread across a variety of domains, including object/pattern and
Yoeri van de Burgt, Neuromorphic Engineering Group, Eindhoven University of Technology, The Netherlands; [email protected] Paschalis Gkoupidenis, Department of Molecular Electronics, Max Planck Institute for Polymer Research, Germany; [email protected] doi:10.1557/mrs.2020.194 • VOLUME © 2020 Materials Research Society Carleton University Library, on 11 Aug 2020 at 08:39:49, subject to the MRS BULLETINCore AUGUST 2020at• Downloaded from https://www.cambridge.org/core. Cambridge terms45 of •use, available https://www.cambridge.org/core/terms. https://doi.org/10.1557/mrs.2020.194
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ORGANIC MATERIALS AND DEVICES FOR BRAIN-INSPIRED COMPUTING
spoken language recognition, data mining in research fields such as chemistry and medicine, robotics, autonomous driving, as well as strategy planning for decision-/policymaking.2,3 Although ANNs are successful in these shorter-term applications, still face major challenges in approaching the level of biological intelligence and energy effici
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