Studies in neuromorphic and neurohybrid systems currently represent one of the most exciting and intriguing multidisciplinary trends in modern science and technology. They integrate the fields of neurobiology, electronics, physics, and mathematics. Recent progress in building artificial neurons and neural networks based on microelectronic devices and memristive crossbars is setting the foundations for a qualitative leap towards general artificial intelligence (AI). Analog neuromorphic systems based on memristive components are among the peculiarities of this approach. They can significantly improve throughput and energy efficiency compared to existing AI accelerators based on digital components. Such systems mimic computational features of biological neural networks, which are capable of solving ill-understood tasks known to be either intractable by traditional AI or highly time-consuming. In addition, neuroelectronic solutions can be integrated with the brain or living neuronal cultures and form neurohybrid systems. Such systems can take advantage of complex molecular mechanisms of biological cells and support fast computations carried out by the artificial part of the tandem. The symbiosis of natural and artificial systems may also make it possible to develop new learning approaches for neuromorphic devices where a network of living neurons acts as a “teacher”.
A strategic question from both fundamental and applied points of view is the involvement of living neural networks in synthetic information processing. It implies the implementation of computation and learning in artificial networks that are shaped through their interaction with living systems, eventually implementing specific brain functions (replacing damaged biology or enhancing existing biology). Steps towards this authentically hybrid approach based on bi-directional interaction between synthetic and biological systems can lead to significant benefits: They can lead to a technological breakthrough in such applications of neurohybrid systems as neuro-implants and neuroprosthetics, as well as the emergence of a new class of AI, including the systems using living cells as an element base for computing.
The main goal of this research topic is to
• Stimulate interaction and discussion between different aspects of this "authentically hybrid" challenge. This can include both electronics and neurotechnologies.
• Neuroelectronic’s can include investigation of new materials, emerging memory devices, and brain-like computing systems.
• Neurotechnology’s can cover cellular and network mechanisms of brain function, adaptation, and plasticity in neural networks, biocompatible materials, and in vitro / in vivo recording systems, neuroengineering.
While this topic seeks to bring together a very broad range of communities, there is sharp focus on the application which is that of functional biointerfacing.
In this Research Topic, we welcome contributions highlighting the latest original results on the topic of “the symbiosis of emerging electronic and neuronal systems” and particular topics introduced above. This specifically includes functional biointerfacing where biological signals either directly drive, or are directly driven by artificial signal processing modules. The latter can be anything between simple pacemakers to full-fledged artificial neural networks. Systems may run either open-loop (1-way communication) or closed-loop. The submission of reviews and perspective articles on this topic are also strongly encouraged.
Studies in neuromorphic and neurohybrid systems currently represent one of the most exciting and intriguing multidisciplinary trends in modern science and technology. They integrate the fields of neurobiology, electronics, physics, and mathematics. Recent progress in building artificial neurons and neural networks based on microelectronic devices and memristive crossbars is setting the foundations for a qualitative leap towards general artificial intelligence (AI). Analog neuromorphic systems based on memristive components are among the peculiarities of this approach. They can significantly improve throughput and energy efficiency compared to existing AI accelerators based on digital components. Such systems mimic computational features of biological neural networks, which are capable of solving ill-understood tasks known to be either intractable by traditional AI or highly time-consuming. In addition, neuroelectronic solutions can be integrated with the brain or living neuronal cultures and form neurohybrid systems. Such systems can take advantage of complex molecular mechanisms of biological cells and support fast computations carried out by the artificial part of the tandem. The symbiosis of natural and artificial systems may also make it possible to develop new learning approaches for neuromorphic devices where a network of living neurons acts as a “teacher”.
A strategic question from both fundamental and applied points of view is the involvement of living neural networks in synthetic information processing. It implies the implementation of computation and learning in artificial networks that are shaped through their interaction with living systems, eventually implementing specific brain functions (replacing damaged biology or enhancing existing biology). Steps towards this authentically hybrid approach based on bi-directional interaction between synthetic and biological systems can lead to significant benefits: They can lead to a technological breakthrough in such applications of neurohybrid systems as neuro-implants and neuroprosthetics, as well as the emergence of a new class of AI, including the systems using living cells as an element base for computing.
The main goal of this research topic is to
• Stimulate interaction and discussion between different aspects of this "authentically hybrid" challenge. This can include both electronics and neurotechnologies.
• Neuroelectronic’s can include investigation of new materials, emerging memory devices, and brain-like computing systems.
• Neurotechnology’s can cover cellular and network mechanisms of brain function, adaptation, and plasticity in neural networks, biocompatible materials, and in vitro / in vivo recording systems, neuroengineering.
While this topic seeks to bring together a very broad range of communities, there is sharp focus on the application which is that of functional biointerfacing.
In this Research Topic, we welcome contributions highlighting the latest original results on the topic of “the symbiosis of emerging electronic and neuronal systems” and particular topics introduced above. This specifically includes functional biointerfacing where biological signals either directly drive, or are directly driven by artificial signal processing modules. The latter can be anything between simple pacemakers to full-fledged artificial neural networks. Systems may run either open-loop (1-way communication) or closed-loop. The submission of reviews and perspective articles on this topic are also strongly encouraged.
