Tag Archives: University of Münster

Adaptive neural connectivity with an event-based architecture using photonic processors

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On first glance it looked like a set of matches. If there were more dimension, this could also have been a set pencils but no,

Caption: The chip contains almost 8,400 functioning artificial neurons from waveguide-coupled phase-change material. The researchers trained this neural network to distinguish between German and English texts on the basis of vowel frequency. Credit: Jonas Schütte / Pernice Group Courtesy: University of Münster

An October 23, 2023 news item on Nanowerk introduces research into a new approach to optical neural networks

A team of researchers headed by physicists Prof. Wolfram Pernice and Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of Münster, has developed a so-called event-based architecture, using photonic processors. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network.

Key Takeaways

Researchers have created a new computing architecture that mimics biological neural networks, using photonic processors for data transportation and processing.

The new system enables continuous adaptation of connections within the neural network, crucial for learning processes. This is known as both synaptic and structural plasticity.

Unlike traditional studies, the connections or synapses in this photonic neural network are not hardware-based but are coded based on optical pulse properties, allowing for a single chip to hold several thousand neurons.

Light-based processors in this system offer a much higher bandwidth and lower energy consumption compared to traditional electronic processors.

The researchers successfully tested the system using an evolutionary algorithm to differentiate between German and English texts based on vowel count, highlighting its potential for rapid and energy-efficient AI applications.

The Research

Modern computer models – for example for complex, potent AI applications – push traditional digital computer processes to their limits.

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The person who edited the original press release, which is included in the news item in the above, is not credited.

Here’s the unedited original October 23, 2023 University of Münster press release (also on EurekAlert)

Modern computer models – for example for complex, potent AI applications – push traditional digital computer processes to their limits. New types of computing architecture, which emulate the working principles of biological neural networks, hold the promise of faster, more energy-efficient data processing. A team of researchers has now developed a so-called event-based architecture, using photonic processors with which data are transported and processed by means of light. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network. This changeable connections are the basis for learning processes. For the purposes of the study, a team working at Collaborative Research Centre 1459 (“Intelligent Matter”) – headed by physicists Prof. Wolfram Pernice and Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of Münster – joined forces with researchers from the Universities of Exeter and Oxford in the UK. The study has been published in the journal “Science Advances”.

What is needed for a neural network in machine learning are artificial neurons which are activated by external excitatory signals, and which have connections to other neurons. The connections between these artificial neurons are called synapses – just like the biological original. For their study, the team of researchers in Münster used a network consisting of almost 8,400 optical neurons made of waveguide-coupled phase-change material, and the team showed that the connection between two each of these neurons can indeed become stronger or weaker (synaptic plasticity), and that new connections can be formed, or existing ones eliminated (structural plasticity). In contrast to other similar studies, the synapses were not hardware elements but were coded as a result of the properties of the optical pulses – in other words, as a result of the respective wavelength and of the intensity of the optical pulse. This made it possible to integrate several thousand neurons on one single chip and connect them optically.

In comparison with traditional electronic processors, light-based processors offer a significantly higher bandwidth, making it possible to carry out complex computing tasks, and with lower energy consumption. This new approach consists of basic research. “Our aim is to develop an optical computing architecture which in the long term will make it possible to compute AI applications in a rapid and energy-efficient way,” says Frank Brückerhoff-Plückelmann, one of the lead authors.

Methodology: The non-volatile phase-change material can be switched between an amorphous structure and a crystalline structure with a highly ordered atomic lattice. This feature allows permanent data storage even without an energy supply. The researchers tested the performance of the neural network by using an evolutionary algorithm to train it to distinguish between German and English texts. The recognition parameter they used was the number of vowels in the text.

The researchers received financial support from the German Research Association, the European Commission and “UK Research and Innovation”.

Here’s a link to and a citation for the paper,

Event-driven adaptive optical neural network by Frank Brückerhoff-Plückelmann, Ivonne Bente, Marlon Becker, Niklas Vollmar, Nikolaos Farmakidis, Emma Lomonte, Francesco Lenzini, C. David Wright, Harish Bhaskaran, Martin Salinga, Benjamin Risse, and Wolfram H. P. Pernice. Science Advances 20 Oct 2023 Vol 9, Issue 42 DOI: 10.1126/sciadv.adi9127

This paper is open access.

Electronics begone! Enter: the light-based brainlike computing chip

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At this point, it’s possible I’m wrong but I think this is the first ‘memristor’ type device (also called a neuromorphic chip) based on light rather than electronics that I’ve featured here on this blog. In other words, it’s not, technically speaking, a memristor but it does have the same properties so it is a neuromorphic chip.

Caption: The optical microchips that the researchers are working on developing are about the size of a one-cent piece. Credit: WWU Muenster – Peter Leßmann

A May 8, 2019 news item on Nanowerk announces this new approach to neuromorphic hardware (Note: A link has been removed),

Researchers from the Universities of Münster (Germany), Oxford and Exeter (both UK) have succeeded in developing a piece of hardware which could pave the way for creating computers which resemble the human brain.

The scientists produced a chip containing a network of artificial neurons that works with light and can imitate the behaviour of neurons and their synapses. The network is able to “learn” information and use this as a basis for computing and recognizing patterns. As the system functions solely with light and not with electrons, it can process data many times faster than traditional systems. …

A May 8, 2019 University of Münster press release (also on EurekAlert), which originated the news item, reveals the full story,

A technology that functions like a brain? In these times of artificial intelligence, this no longer seems so far-fetched – for example, when a mobile phone can recognise faces or languages. With more complex applications, however, computers still quickly come up against their own limitations. One of the reasons for this is that a computer traditionally has separate memory and processor units – the consequence of which is that all data have to be sent back and forth between the two. In this respect, the human brain is way ahead of even the most modern computers because it processes and stores information in the same place – in the synapses, or connections between neurons, of which there are a million-billion in the brain. An international team of researchers from the Universities of Münster (Germany), Oxford and Exeter (both UK) have now succeeded in developing a piece of hardware which could pave the way for creating computers which resemble the human brain. The scientists managed to produce a chip containing a network of artificial neurons that works with light and can imitate the behaviour of neurons and their synapses.

The researchers were able to demonstrate, that such an optical neurosynaptic network is able to “learn” information and use this as a basis for computing and recognizing patterns – just as a brain can. As the system functions solely with light and not with traditional electrons, it can process data many times faster. “This integrated photonic system is an experimental milestone,” says Prof. Wolfram Pernice from Münster University and lead partner in the study. “The approach could be used later in many different fields for evaluating patterns in large quantities of data, for example in medical diagnoses.” The study is published in the latest issue of the “Nature” journal.

The story in detail – background and method used

Most of the existing approaches relating to so-called neuromorphic networks are based on electronics, whereas optical systems – in which photons, i.e. light particles, are used – are still in their infancy. The principle which the German and British scientists have now presented works as follows: optical waveguides that can transmit light and can be fabricated into optical microchips are integrated with so-called phase-change materials – which are already found today on storage media such as re-writable DVDs. These phase-change materials are characterised by the fact that they change their optical properties dramatically, depending on whether they are crystalline – when their atoms arrange themselves in a regular fashion – or amorphous – when their atoms organise themselves in an irregular fashion. This phase-change can be triggered by light if a laser heats the material up. “Because the material reacts so strongly, and changes its properties dramatically, it is highly suitable for imitating synapses and the transfer of impulses between two neurons,” says lead author Johannes Feldmann, who carried out many of the experiments as part of his PhD thesis at the Münster University.

In their study, the scientists succeeded for the first time in merging many nanostructured phase-change materials into one neurosynaptic network. The researchers developed a chip with four artificial neurons and a total of 60 synapses. The structure of the chip – consisting of different layers – was based on the so-called wavelength division multiplex technology, which is a process in which light is transmitted on different channels within the optical nanocircuit.

In order to test the extent to which the system is able to recognise patterns, the researchers “fed” it with information in the form of light pulses, using two different algorithms of machine learning. In this process, an artificial system “learns” from examples and can, ultimately, generalise them. In the case of the two algorithms used – both in so-called supervised and in unsupervised learning – the artificial network was ultimately able, on the basis of given light patterns, to recognise a pattern being sought – one of which was four consecutive letters.

“Our system has enabled us to take an important step towards creating computer hardware which behaves similarly to neurons and synapses in the brain and which is also able to work on real-world tasks,” says Wolfram Pernice. “By working with photons instead of electrons we can exploit to the full the known potential of optical technologies – not only in order to transfer data, as has been the case so far, but also in order to process and store them in one place,” adds co-author Prof. Harish Bhaskaran from the University of Oxford.

A very specific example is that with the aid of such hardware cancer cells could be identified automatically. Further work will need to be done, however, before such applications become reality. The researchers need to increase the number of artificial neurons and synapses and increase the depth of neural networks. This can be done, for example, with optical chips manufactured using silicon technology. “This step is to be taken in the EU joint project ‘Fun-COMP’ by using foundry processing for the production of nanochips,” says co-author and leader of the Fun-COMP project, Prof. C. David Wright from the University of Exeter.

Here’s a link to and a citation for the paper,

All-optical spiking neurosynaptic networks with self-learning capabilities by J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran & W. H. P. Pernice. Nature volume 569, pages208–214 (2019) DOI: https://doi.org/10.1038/s41586-019-1157-8 Issue Date: 09 May 2019

This paper is behind a paywall.

For the curious, I found a little more information about Fun-COMP (functionally-scaled computer technology). It’s a European Commission (EC) Horizon 2020 project coordinated through the University of Exeter. For information with details such as the total cost, contribution from the EC, the list of partnerships and more there is the Fun-COMP webpage on fabiodisconzi.com.

Better anti-parasitic medicine delivery with chitosan-based nanocapsules

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I mage: The common liver fluke which can cause fascioliasis. Credit: Wikimedia creative commons Courtesy: Leeds University

It looks like a pair of lips to me but, according to a December 12, 2018 news item on Nanowerk, this liver fluke heralds a flatworm infection is a serious health problem,

An international team, led by Professor Francisco Goycoolea from the University of Leeds [UK] and Dr Claudio Salomon from the Universidad Nacional de Rosario, Argentina, and in collaboration with colleagues at the University of Münster, Germany, have developed a novel pharmaceutical formulation to administer triclabendazole – an anti-parasitic drug used to treat a type of flatworm infection – in billions of tiny capsules.

The World Health Organisation estimates that 2.4 million people are infected with fascioliasis, the disease caused by flatworms and treated with triclabendazole.

A December 12, 2018 University of Leeds  press release (also on EurekAlert), which originated the news item,

Anti-parasitic drugs do not become effective until they dissolve and are absorbed. Traditionally, these medicines are highly insoluble and this limits their therapeutic effect.
In a bid to overcome this limitation and accomplish the new formulation, the team used “soft” nanotechnology and nanomedicine approaches, which utilises the self-assembly properties of organic nanostructures and uses techniques in which components, such as polymers and surfactants in solution, play key roles.

Their formulation produces capsules that are less than one micron in size – the diameter of a human hair is roughly 75 microns. These tiny capsules are loaded with triclabendazole and then bundled together to deliver the required dose.

The team used chitosan, a naturally-occurring sugar polymer found in the exoskeleton of shellfish and the cell walls of certain fungi, to coat the oil-core of capsules and bind the drug together, while stabilising the capsule and helping to preserve it.
In its nanocapsule form, the drug would be 100 times more soluble than its current tablet form.

Professor Goycoolea, from the School of Food Science and Nutrition at Leeds, said: “Solubility is critical challenge for effective anti-parasite medicine. We looked to tackle this problem at the particle level. Triclabendazole taken as a dose made up of billions of tiny capsules would mean the medicine would be more efficiently and quickly absorbed

“Through the use of nanocapsules and nanoemulsions, drug efficiency can be enhanced and new solutions can be considered for the best ways to target medicine delivery.”
Dr Salomon said: “To date, this is the first report on triclabendazole nanoencapsulation and we believe this type of formulation could be applied to other anti-parasitic drugs as well. But more research is needed to ensure this new pharmaceutical formulation of the drug does not diminish the anti-parasitic effect. Our ongoing research is working to answer this very question.”

Although there have been cases of fascioliasis in more than 70 countries worldwide, with increasing reports from Europe and the Americas, it is considered a neglected disease, as it does not receive much attention and often goes untreated.
Symptoms of the disease when it reaches the chronic phase include intermittent pain, jaundice and anaemia. Patients can also experience hardening of the liver in the case of long-term inflammation.

Because of the highly insoluble nature of anti-parasitic drugs, they need to be administered in very high dosages to ensure enough of the active ingredient is absorbed. This is particularly problematic when treating children for parasites. Tablets needs to be divided into smaller pieces to adjust the dosage and make swallowing easier, but this can cause side effects due to incorrect dosage.

The team’s technique to formulate triclabendazole into nanocapsules, published today [Dec. 12, 2018] in the journal PLOS ONE, would also allow for lower doses to be administered. s

Here’s a link to and a citation for the paper,

Chitosan-based nanodelivery systems applied to the development of novel triclabendazole formulations by Daniel Real, Stefan Hoffmann, Darío Leonardi, Claudio Salomon, Francisco M. Goycoolea. PLOS DOI https://doi.org/10.1371/journal.pone.0207625 Published: December 12, 2018

This paper is open access. BTW, I loved the title for the press release (Helping the anti-parasitic medicine go down) for its reference to the song, A spoonful of sugar helps the medicine go down, in the 1964 film musical, Mary Poppins, and the shout out for the sort of sequel, Mary Poppins Returns, released on Dec. 19, 2018.