Dear Colleagues,

In recent years, artificial intelligence (AI) has become increasingly important in a variety of fields, including complex systems physics, data science, adaptive robotics, the Internet of Things (IoT), and so on. Most AIs are based on software that operates on digital computers, but development in software-based AI has the disadvantage of an ever-increasing energy cost. For this reason, researchers are trying to develop an energy-efficient hardware version of AI. Recently, molecules/materials-based AI has received a lot of attention in an effort to create intelligent machines that consume less energy. Furthermore, rather than program-based machines such as digital computers, which are considered unsuitable for mathematical modeling of real physical space people are living in, they are expected to adaptively respond to unexpected and rapid environmental changes. Against this backdrop on the program-based system, stochastic computing has received a lot of attention because molecules/materials such as polymers exhibit affinities such as noise generation and neuron-like nonlinear responses to external stimuli. In particular, as with neural networks, the connections and interactions between device elements are important in realizing network devices such as sensor networks, allowing the formation of dynamical spatiotemporal patterns and is therefore considered as an elemental technology for brain-inspired cognitive computing devices. Furthermore, materials-based reservoir computing is growing as a research area that mimics recurrent neural networks. The purpose of this Special Issue is to share new ideas on molecules/materials-based AIs and practical implementation related to these research topics. To that end, we welcome submissions to this Special Issue to promote neuromorphic organic/polymer devices.

Prof. Dr. Naoki Asakawa
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

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• Organic/polymer neuromorphic devices
• Bifurcation-based nonlinear dynamics in molecular/materials systems
• Materials-based stochastic signal/information processing devices
• Materials-based bio-mimicking embodiment systems
• Materials-based neural networks
• Materials-based bio-mimicking sensor and its networks
• Materials-based reservoir computing
• Attractor selection/switching devices