There are two main approaches to what we consider neuromorphic computing. The first involves emulating biological neural processing systems through the physics of computation of computational substrates that have similar properties and constraints as real neural systems, with potential for denser structures and advantages in energy cost. The other simulates neural processing systems on scalable architectures that allow the simulation of large neural networks, with higher degree of abstraction, arbitrary precision, high resolution, and no constraints imposed by the physics of the computing medium.

Both may be required to advance the field, but is either approach ‘better’? Hosted by Neuromorphic Computing and Engineering, this webinar will see teams of leading experts in the field of neuromorphic computing argue the case for either approach, overseen by an impartial moderator.

Team emulation:
Elisa Donati. Elisa’s research interests aim at designing neuromorphic circuits that are ideally suited for interfacing with the nervous system and show how they can be used to build closed-loop hybrid artificial and biological neural processing systems.  She is also involved in the development of neuromorphic hardware and software systems able to mimic the functions of biological brains to apply for medical and robotics applications.

Jennifer Hasler received her BSE and MS degrees in electrical engineering from Arizona State University in August 1991. She received her PhD in computation and neural systems from California Institute of Technology in February 1997. Jennifer is a professor at the Georgia Institute of Technology in the School of Electrical and Computer Engineering; Atlanta is the coldest climate in which she has lived. Jennifer founded the Integrated Computational Electronics (ICE) laboratory at Georgia Tech, a laboratory affiliated with the Laboratories for Neural Engineering. She is a member of Tau Beta P, Eta Kappa Nu, and the IEEE.

Team simulation:
Catherine (Katie) Schuman is an assistant professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee (UT). She received her PhD in computer science from UT in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. Katie previously served as a research scientist at Oak Ridge National Laboratory, where her research focused on algorithms and applications of neuromorphic systems. Katie co-leads the TENNLab Neuromorphic Computing Research Group at UT. She has written for more than 70 publications as well as seven patents in the field of neuromorphic computing. She received the Department of Energy Early Career Award in 2019. Katie is a senior member of the Association of Computing Machinery and the IEEE.

Emre Neftci received his MSc degree in physics from EPFL in Switzerland, and his PhD in 2010 at the Institute of Neuroinformatics at the University of Zurich and ETH Zurich. He is currently an institute director at the Jülich Research Centre and professor at RWTH Aachen. His current research explores the bridges between neuroscience and machine learning, with a focus on the theoretical and computational modelling of learning algorithms that are best suited to neuromorphic hardware and non-von Neumann computing architectures.

Discussion chair:
Giulia D’Angelo is currently a Marie Skłodowska-Curie postdoctoral fellow at the Czech Technical University in Prague, where she focuses on neuromorphic algorithms for active vision. She obtained a bachelor’s degree in biomedical engineering from the University of Genoa and a master’s degree in neuroengineering with honours. During her master’s, she developed a neuromorphic system for the egocentric representation of peripersonal visual space at King’s College London. She earned her PhD in neuromorphic algorithms at the University of Manchester, receiving the President’s Doctoral Scholar Award, in collaboration with the Event-Driven Perception for Robotics Laboratory at the Italian Institute of Technology. There, she proposed a biologically plausible model for event-driven, saliency-based visual attention. She was recently awarded the Marie Skłodowska-Curie Fellowship to explore sensorimotor contingency theories in the context of neuromorphic active vision algorithms.

About this journal

Neuromorphic Computing and Engineering is a multidisciplinary, open access journal publishing cutting-edge research on the design, development and application of artificial neural networks and systems from both a hardware and computational perspective.

Editor-in-chief: Giacomo Indiveri, University of Zurich, Switzerland

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Scientists have created a novel antimicrobial coating that, when mixed with paint, can be applied to a range of surfaces to destroy bacteria and viruses – including particularly persistent and difficult to kill strains like MRSA, flu virus and SARS-CoV-2. The development potentially paves the way for substantial improvements in scientific, commercial and clinical hygiene.

The University of Nottingham-led team made the material by combining chlorhexidine digluconate (CHX) – a disinfectant commonly used by dentists to treat mouth infections and by clinicians for cleaning before surgery – with everyday paint-on epoxy resin. Using this material, the team worked with staff at Birmingham-based specialist coating company Indestructible Paint to create a prototype antimicrobial paint. They found that, when dried, the coating can kill a wide range of pathogens.

The findings of the study, which was funded by the Royal Academy of Engineering Industrial Fellowship Scheme, were published in Scientific Reports.

Persistent antimicrobial protection

As part of the project, the researchers painted the antimicrobial coating onto a surface and used a range of scientific techniques to analyse the distribution of the biocide in the paint, to confirm that it remained uniformly distributed at a molecular level.

According to project leader Felicity de Cogan, the new paint can be used to provide antimicrobial protection on a wide array of plastic and hard non-porous surfaces. Crucially, it could be effective in a range of clinical environments, where surfaces like hospital beds and toilet seats can act as a breeding ground for bacteria for extended periods of time – even after the introduction of stringent cleaning regimes.

The team, based at the University’s School of Pharmacy, is also investigating the material’s use in the transport and aerospace industries, especially on frequently touched surfaces in public spaces such as aeroplane seats and tray tables.

“The antimicrobial in the paint is chlorhexidine – a biocide commonly used in products like mouthwash. Once it is added, the paint works in exactly the same way as all other paint and the addition of the antimicrobial doesn’t affect its application or durability on the surface,” says de Cogan.

The researchers also note that adding CHX to the epoxy resin did not affect its optical transparency.

According to de Cogan, the novel concoction has a range of potential scientific, clinical and commercial applications.

“We have shown that it is highly effective against a range of different pathogens like E. coli and MRSA. We have also shown that it is effective against bacteria even when they are already resistant to antibiotics and biocides,” she says. “This means the technology could be a useful tool to circumvent the global problem of antimicrobial resistance.”

In de Cogan’s view, there are also number of major advantages to using the new coating to tackle bacterial infection – especially when compared to existing approaches – further boosting the prospects of future applications.

The key advantage of the technology is that the paint is “self-cleaning” – meaning that it would no longer be necessary to carry out the arduous task of repeatedly cleaning a surface to remove harmful microbes. Instead, after a single application, the simple presence of the paint on the surface would actively and continuously kill bacteria and viruses whenever they come into contact with it.

“This means that you can be sure a surface won’t pass on infections when you touch it,” says de Cogan.

“We are looking at more extensive testing in harsher environments and long-term durability testing over months and years. This work is ongoing and we will be following up with another publication shortly,” she adds.

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Roses have been cultivated for thousands of years, admired for their beauty. Despite their use in fragrance, skincare and even in teas and jams, there are some things, however, that we still don’t know about these symbolic flowers.

And that includes the physical mechanism behind the shape of rose petals.

The curves and curls of leaves and flower petals arise due to the interplay between their natural growth and geometry.

Uneven growth in a flat sheet, in which the edges grow quicker than the interior, gives rise to strain and in plant leaves and petals, for example, this can result in a variety of shapes such as saddle and ripple shapes.

Yet when it comes to rose petals, the sharply pointed cusps – a point where two curves meet — that form at the edge of the petals set it apart from soft, wavy patterns seen in many other plants.

While young rose petals have smooth edges, as the rose matures the petals change to a polygonal shape with multiples cusps.

To investigate this intriguing difference, researchers from The Hebrew University of Jerusalem carried out theoretical modelling and conducted a series of experiments with synthetic disc “petals”.

They found that the pointed cusps that form at the edge of rose petals are due to a type of geometric frustration called a Mainardi-Codazzi-Peterson (MCP) incompatibility.

This type of mechanism results in stress concentrating in a specific area, which go on to form cusps to avoid tearing or forming unnatural folding.

When the researchers supressed the formation of cusps, they found that the discs reverted to being smooth and concave.

The researchers say that the findings could be used for applications in soft robotics and even the deployment of spacecraft components.

And it also goes some way to deepen our appreciation of nature’s ability to juggle growth and geometry.

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