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Volume 2 Issue 3, March 2020

Neuromorphic olfaction

Neuromorphic chips are designed to use computational machinery inspired by the brain, but it has been challenging to use that machinery in real-world practical problems. In a paper in this issue, Imam and Cleland describe a neural algorithm for the learning and identification of odour samples based on the architecture of the mammalian olfactory system. They implement their neural algorithm in the Intel Loihi neuromorphic system.

See Nabil Imam and Thomas A. Cleland.

Image: Springer Nature Limited/Jordan Abina. Cover Design: Karen Moore.


  • Generative deep learning can produce artificial, natural-looking images and other data, which has many promising applications in research — and in art. But the wide availability of generative models poses a challenge for society, which needs tools and best practices to distinguish between real and synthetic data.



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    • Adam Poulsen
    • Eduard Fosch-Villaronga
    • Roger Andre Søraa
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Comment & Opinion

  • Martial Hebert is the director of the Robotics Institute and dean of the School of Computer Science at Carnegie Mellon University. We spoke with him at the O’Reilly AI Conference in New York in April 2019, where he delivered a keynote talk, ‘AI and the robotics revolution’, and in a follow-up conversation.

    • Trenton Jerde
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News & Views

  • Recurrent networks can be trained using a generalization of backpropagation, called backpropagation through time, but a gap exists between the mathematics of this learning algorithm and biological plausibility. E-prop is a biologically inspired alternative that opens up possibilities for a new generation of online training algorithms for recurrent networks.

    • Luca Manneschi
    • Eleni Vasilaki
    News & Views
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  • As artists are beginning to employ deep learning techniques to create new and interesting art, questions arise about how copyright and ownership apply to those works. This Perspective discusses how artists, programmers and users can ensure clarity about the ownership of their creations.

    • Jason K. Eshraghian
  • Predicting the properties of batteries, such as their state of charge and remaining lifetime, is crucial for improving battery manufacturing, usage and optimisation for energy storage. The authors discuss how machine learning methods and high-throughput experimentation provide a data-driven approach to this problem, and highlight challenges in building models which provide fast and accurate battery state predictions.

    • Man-Fai Ng
    • Jin Zhao
    • Zhi Wei Seh
    Review Article
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  • With the aid of deep learning, the space of chemical molecules, such as candidates for drugs, can be constrained to find new bioactive molecules. A new open source tool can generate libraries of novel molecules with user defined properties.

    • Michael Moret
    • Lukas Friedrich
    • Gisbert Schneider
  • Integrating knowledge about the circuit-level organization of the brain into neuromorphic artificial systems is a challenging research problem. The authors present a neural algorithm for the learning of odourant signals and their robust identification under noise, based on the architecture of the mammalian olfactory bulb and implemented on the Intel Loihi neuromorphic system.

    • Nabil Imam
    • Thomas A. Cleland

    Nature Outlook:

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