Tag Archives: Human Brain Project (HBP)

FrogHeart’s 2023 comes to an end as 2024 comes into view

My personal theme for this last year (2023) and for the coming year was and is: catching up. On the plus side, my 2023 backlog (roughly six months) to be published was whittled down considerably. On the minus side, I start 2024 with a backlog of two to three months.

2023 on this blog had a lot in common with 2022 (see my December 31, 2022 posting), which may be due to what’s going on in the world of emerging science and technology or to my personal interests or possibly a bit of both. On to 2023 and a further blurring of boundaries:

Energy, computing and the environment

The argument against paper is that it uses up resources, it’s polluting, it’s affecting the environment, etc. Somehow the part where electricity which underpins so much of our ‘smart’ society does the same thing is left out of the discussion.

Neuromorphic (brainlike) computing and lower energy

Before launching into the stories about lowering energy usage, here’s an October 16, 2023 posting “The cost of building ChatGPT” that gives you some idea of the consequences of our insatiable desire for more computing and more ‘smart’ devices,

In its latest environmental report, Microsoft disclosed that its global water consumption spiked 34% from 2021 to 2022 (to nearly 1.7 billion gallons , or more than 2,500 Olympic-sized swimming pools), a sharp increase compared to previous years that outside researchers tie to its AI research. [emphases mine]

“It’s fair to say the majority of the growth is due to AI,” including “its heavy investment in generative AI and partnership with OpenAI,” said Shaolei Ren, [emphasis mine] a researcher at the University of California, Riverside who has been trying to calculate the environmental impact of generative AI products such as ChatGPT.

Why it matters: Microsoft’s five WDM [West Des Moines in Iowa] data centers — the “epicenter for advancing AI” — represent more than $5 billion in investments in the last 15 years.

Yes, but: They consumed as much as 11.5 million gallons of water a month for cooling, or about 6% of WDM’s total usage during peak summer usage during the last two years, according to information from West Des Moines Water Works.

The focus is AI but it doesn’t take long to realize that all computing has energy and environmental costs. I have more about Ren’s work and about water shortages in the “The cost of building ChatGPT” posting.

This next posting would usually be included with my other art/sci postings but it touches on the issues. My October 13, 2023 posting about Toronto’s Art/Sci Salon events, in particular, there’s the Streaming Carbon Footprint event (just scroll down to the appropriate subhead). For the interested, I also found this 2022 paper “The Carbon Footprint of Streaming Media:; Problems, Calculations, Solutions” co-authored by one of the artist/researchers (Laura U. Marks, philosopher and scholar of new media and film at Simon Fraser University) who presented at the Toronto event.

I’m late to the party; Thomas Daigle posted a January 2, 2020 article about energy use and our appetite for computing and ‘smart’ devices for the Canadian Broadcasting Corporation’s online news,

For those of us binge-watching TV shows, installing new smartphone apps or sharing family photos on social media over the holidays, it may seem like an abstract predicament.

The gigabytes of data we’re using — although invisible — come at a significant cost to the environment. Some experts say it rivals that of the airline industry. 

And as more smart devices rely on data to operate (think internet-connected refrigerators or self-driving cars), their electricity demands are set to skyrocket.

“We are using an immense amount of energy to drive this data revolution,” said Jane Kearns, an environment and technology expert at MaRS Discovery District, an innovation hub in Toronto.

“It has real implications for our climate.”

Some good news

Researchers are working on ways to lower the energy and environmental costs, here’s a sampling of 2023 posts with an emphasis on brainlike computing that attest to it,

If there’s an industry that can make neuromorphic computing and energy savings sexy, it’s the automotive indusry,

On the energy front,

Most people are familiar with nuclear fission and some its attendant issues. There is an alternative nuclear energy, fusion, which is considered ‘green’ or greener anyway. General Fusion is a local (Vancouver area) company focused on developing fusion energy, alongside competitors from all over the planet.

Part of what makes fusion energy attractive is that salt water or sea water can be used in its production and, according to that December posting, there are other applications for salt water power,

More encouraging developments in environmental science

Again, this is a selection. You’ll find a number of nano cellulose research projects and a couple of seaweed projects (seaweed research seems to be of increasing interest).

All by myself (neuromorphic engineering)

Neuromorphic computing is a subset of neuromorphic engineering and I stumbled across an article that outlines the similarities and differences. My ‘summary’ of the main points and a link to the original article can be found here,

Oops! I did it again. More AI panic

I included an overview of the various ‘recent’ panics (in my May 25, 2023 posting below) along with a few other posts about concerning developments but it’s not all doom and gloom..

Governments have realized that regulation might be a good idea. The European Union has a n AI act, the UK held an AI Safety Summit in November 2023, the US has been discussing AI regulation with its various hearings, and there’s impending legislation in Canada (see professor and lawyer Michael Geist’s blog for more).

A long time coming, a nanomedicine comeuppance

Paolo Macchiarini is now infamous for his untested, dangerous approach to medicine. Like a lot of people, I was fooled too as you can see in my August 2, 2011 posting, “Body parts nano style,”

In early July 2011, there were reports of a new kind of transplant involving a body part made of a biocomposite. Andemariam Teklesenbet Beyene underwent a trachea transplant that required an artificial windpipe crafted by UK experts then flown to Sweden where Beyene’s stem cells were used to coat the windpipe before being transplanted into his body.

It is an extraordinary story not least because Beyene, a patient in a Swedish hospital planning to return to Eritrea after his PhD studies in Iceland, illustrates the international cooperation that made the transplant possible.

The scaffolding material for the artificial windpipe was developed by Professor Alex Seifalian at the University College London in a landmark piece of nanotechnology-enabled tissue engineering. …

Five years later I stumbled across problems with Macchiarini’s work as outlined in my April 19, 2016 posting, “Macchiarini controversy and synthetic trachea transplants (part 1 of 2)” and my other April 19, 2016 posting, “Macchiarini controversy and synthetic trachea transplants (part 2 of 2)“.

This year, Gretchen Vogel (whose work was featured in my 2016 posts) has written a June 21, 2023 update about the Macchiarini affair for Science magazine, Note: Links have been removed,

Surgeon Paolo Macchiarini, who was once hailed as a pioneer of stem cell medicine, was found guilty of gross assault against three of his patients today and sentenced to 2 years and 6 months in prison by an appeals court in Stockholm. The ruling comes a year after a Swedish district court found Macchiarini guilty of bodily harm in two of the cases and gave him a suspended sentence. After both the prosecution and Macchiarini appealed that ruling, the Svea Court of Appeal heard the case in April and May. Today’s ruling from the five-judge panel is largely a win for the prosecution—it had asked for a 5-year sentence whereas Macchiarini’s lawyer urged the appeals court to acquit him of all charges.

Macchiarini performed experimental surgeries on the three patients in 2011 and 2012 while working at the renowned Karolinska Institute. He implanted synthetic windpipes seeded with stem cells from the patients’ own bone marrow, with the hope the cells would multiply over time and provide an enduring replacement. All three patients died when the implants failed. One patient died suddenly when the implant caused massive bleeding just 4 months after it was implanted; the two others survived for 2.5 and nearly 5 years, respectively, but suffered painful and debilitating complications before their deaths.

In the ruling released today, the appeals judges disagreed with the district court’s decision that the first two patients were treated under “emergency” conditions. Both patients could have survived for a significant length of time without the surgeries, they said. The third case was an “emergency,” the court ruled, but the treatment was still indefensible because by then Macchiarini was well aware of the problems with the technique. (One patient had already died and the other had suffered severe complications.)

A fictionalized tv series ( part of the Dr. Death anthology series) based on Macchiarini’s deceptions and a Dr. Death documentary are being broadcast/streamed in the US during January 2024. These come on the heels of a November 2023 Macchiarini documentary also broadcast/streamed on US television.

Dr. Death (anthology), based on the previews I’ve seen, is heavily US-centric, which is to be expected since Adam Ciralsky is involved in the production. Ciralsky wrote an exposé about Macchiarini for Vanity Fair published in 2016 (also featured in my 2016 postings). From a December 20, 2023 article by Julie Miller for Vanity Fair, Note: A link has been removed,

Seven years ago [2016], world-renowned surgeon Paolo Macchiarini was the subject of an ongoing Vanity Fair investigation. He had seduced award-winning NBC producer Benita Alexander while she was making a special about him, proposed, and promised her a wedding officiated by Pope Francis and attended by political A-listers. It was only after her designer wedding gown was made that Alexander learned Macchiarini was still married to his wife, and seemingly had no association with the famous names on their guest list.

Vanity Fair contributor Adam Ciralsky was in the midst of reporting the story for this magazine in the fall of 2015 when he turned to Dr. Ronald Schouten, a Harvard psychiatry professor. Ciralsky sought expert insight into the kind of fabulist who would invent and engage in such an audacious lie.

“I laid out the story to him, and he said, ‘Anybody who does this in their private life engages in the same conduct in their professional life,” recalls Ciralsky, in a phone call with Vanity Fair. “I think you ought to take a hard look at his CVs.”

That was the turning point in the story for Ciralsky, a former CIA lawyer who soon learned that Macchiarini was more dangerous as a surgeon than a suitor. …

Here’s a link to Ciralsky’s original article, which I described this way, from my April 19, 2016 posting (part 2 of the Macchiarini controversy),

For some bizarre frosting on this disturbing cake (see part 1 of the Macchiarini controversy and synthetic trachea transplants for the medical science aspects), a January 5, 2016 Vanity Fair article by Adam Ciralsky documents Macchiarini’s courtship of an NBC ([US] National Broadcasting Corporation) news producer who was preparing a documentary about him and his work.

[from Ciralsky’s article]

“Macchiarini, 57, is a magnet for superlatives. He is commonly referred to as “world-renowned” and a “super-surgeon.” He is credited with medical miracles, including the world’s first synthetic organ transplant, which involved fashioning a trachea, or windpipe, out of plastic and then coating it with a patient’s own stem cells. That feat, in 2011, appeared to solve two of medicine’s more intractable problems—organ rejection and the lack of donor organs—and brought with it major media exposure for Macchiarini and his employer, Stockholm’s Karolinska Institute, home of the Nobel Prize in Physiology or Medicine. Macchiarini was now planning another first: a synthetic-trachea transplant on a child, a two-year-old Korean-Canadian girl named Hannah Warren, who had spent her entire life in a Seoul hospital. … “

Other players in the Macchiarini story

Pierre Delaere, a trachea expert and professor of head and neck surgery at KU Leuven (a university in Belgium) was one of the first to draw attention to Macchiarini’s dangerous and unethical practices. To give you an idea of how difficult it was to get attention for this issue, there’s a September 1, 2017 article by John Rasko and Carl Power for the Guardian illustrating the issue. Here’s what they had to say about Delaere and other early critics of the work, Note: Links have been removed,

Delaere was one of the earliest and harshest critics of Macchiarini’s engineered airways. Reports of their success always seemed like “hot air” to him. He could see no real evidence that the windpipe scaffolds were becoming living, functioning airways – in which case, they were destined to fail. The only question was how long it would take – weeks, months or a few years.

Delaere’s damning criticisms appeared in major medical journals, including the Lancet, but weren’t taken seriously by Karolinska’s leadership. Nor did they impress the institute’s ethics council when Delaere lodged a formal complaint. [emphases mine]

Support for Macchiarini remained strong, even as his patients began to die. In part, this is because the field of windpipe repair is a niche area. Few people at Karolinska, especially among those in power, knew enough about it to appreciate Delaere’s claims. Also, in such a highly competitive environment, people are keen to show allegiance to their superiors and wary of criticising them. The official report into the matter dubbed this the “bandwagon effect”.

With Macchiarini’s exploits endorsed by management and breathlessly reported in the media, it was all too easy to jump on that bandwagon.

And difficult to jump off. In early 2014, four Karolinska doctors defied the reigning culture of silence [emphasis mine] by complaining about Macchiarini. In their view, he was grossly misrepresenting his results and the health of his patients. An independent investigator agreed. But the vice-chancellor of Karolinska Institute, Anders Hamsten, wasn’t bound by this judgement. He officially cleared Macchiarini of scientific misconduct, allowing merely that he’d sometimes acted “without due care”.

For their efforts, the whistleblowers were punished. [emphasis mine] When Macchiarini accused one of them, Karl-Henrik Grinnemo, of stealing his work in a grant application, Hamsten found him guilty. As Grinnemo recalls, it nearly destroyed his career: “I didn’t receive any new grants. No one wanted to collaborate with me. We were doing good research, but it didn’t matter … I thought I was going to lose my lab, my staff – everything.”

This went on for three years until, just recently [2017], Grinnemo was cleared of all wrongdoing.

It is fitting that Macchiarini’s career unravelled at the Karolinska Institute. As the home of the Nobel prize in physiology or medicine, one of its ambitions is to create scientific celebrities. Every year, it gives science a show-business makeover, picking out from the mass of medical researchers those individuals deserving of superstardom. The idea is that scientific progress is driven by the genius of a few.

It’s a problematic idea with unfortunate side effects. A genius is a revolutionary by definition, a risk-taker and a law-breaker. Wasn’t something of this idea behind the special treatment Karolinska gave Macchiarini? Surely, he got away with so much because he was considered an exception to the rules with more than a whiff of the Nobel about him. At any rate, some of his most powerful friends were themselves Nobel judges until, with his fall from grace, they fell too.

The September 1, 2017 article by Rasko and Power is worth the read if you have the interest and the time. And, Delaere has written up a comprehensive analysis, which includes basic information about tracheas and more, “The Biggest Lie in Medical History” 2020, PDF, 164 pp., Creative Commons Licence).

I also want to mention Leonid Schneider, science journalist and molecular cell biologist, whose work the Macchiarini scandal on his ‘For Better Science’ website was also featured in my 2016 pieces. Schneider’s site has a page titled, ‘Macchiarini’s trachea transplant patients: the full list‘ started in 2017 and which he continues to update with new information about the patients. The latest update was made on December 20, 2023.

Promising nanomedicine research but no promises and a caveat

Most of the research mentioned here is still in the laboratory. i don’t often come across work that has made its way to clinical trials since the focus of this blog is emerging science and technology,

*If you’re interested in the business of neurotechnology, the July 17, 2023 posting highlights a very good UNESCO report on the topic.

Funky music (sound and noise)

I have couple of stories about using sound for wound healing, bioinspiration for soundproofing applications, detecting seismic activity, more data sonification, etc.

Same old, same old CRISPR

2023 was relatively quiet (no panics) where CRISPR developments are concerned but still quite active.

Art/Sci: a pretty active year

I didn’t realize how active the year was art/sciwise including events and other projects until I reviewed this year’s postings. This is a selection from 2023 but there’s a lot more on the blog, just use the search term, “art/sci,” or “art/science,” or “sciart.”

While I often feature events and projects from these groups (e.g., June 2, 2023 posting, “Metacreation Lab’s greatest hits of Summer 2023“), it’s possible for me to miss a few. So, you can check out Toronto’s Art/Sci Salon’s website (strong focus on visual art) and Simon Fraser University’s Metacreation Lab for Creative Artificial Intelligence website (strong focus on music).

My selection of this year’s postings is more heavily weighted to the ‘writing’ end of things.

Boundaries: life/nonlife

Last year I subtitled this section, ‘Aliens on earth: machinic biology and/or biological machinery?” Here’s this year’s selection,

Canada’s 2023 budget … military

2023 featured an unusual budget where military expenditures were going to be increased, something which could have implications for our science and technology research.

Then things changed as Murray Brewster’s November 21, 2023 article for the Canadian Broadcasting Corporation’s (CBC) news online website comments, Note: A link has been removed,

There was a revelatory moment on the weekend as Defence Minister Bill Blair attempted to bridge the gap between rhetoric and reality in the Liberal government’s spending plans for his department and the Canadian military.

Asked about an anticipated (and long overdue) update to the country’s defence policy (supposedly made urgent two years ago by Russia’s full-on invasion of Ukraine), Blair acknowledged that the reset is now being viewed through a fiscal lens.

“We said we’re going to bring forward a new defence policy update. We’ve been working through that,” Blair told CBC’s Rosemary Barton Live on Sunday.

“The current fiscal environment that the country faces itself does require (that) that defence policy update … recognize (the) fiscal challenges. And so it’ll be part of … our future budget processes.”

One policy goal of the existing defence plan, Strong, Secure and Engaged, was to require that the military be able to concurrently deliver “two sustained deployments of 500 [to] 1,500 personnel in two different theaters of operation, including one as a lead nation.”

In a footnote, the recent estimates said the Canadian military is “currently unable to conduct multiple operations concurrently per the requirements laid out in the 2017 Defence Policy. Readiness of CAF force elements has continued to decrease over the course of the last year, aggravated by decreasing number of personnel and issues with equipment and vehicles.”

Some analysts say they believe that even if the federal government hits its overall budget reduction targets, what has been taken away from defence — and what’s about to be taken away — won’t be coming back, the minister’s public assurances notwithstanding.

10 years: Graphene Flagship Project and Human Brain Project

Graphene and Human Brain Project win biggest research award in history (& this is the 2000th post)” on January 28, 2013 was how I announced the results of what had been a a European Union (EU) competition that stretched out over several years and many stages as projects were evaluated and fell to the wayside or were allowed onto the next stage. The two finalists received €1B each to be paid out over ten years.

Future or not

As you can see, there was plenty of interesting stuff going on in 2023 but no watershed moments in the areas I follow. (Please do let me know in the Comments should you disagree with this or any other part of this posting.) Nanotechnology seems less and less an emerging science/technology in itself and more like a foundational element of our science and technology sectors. On that note, you may find my upcoming (in 2024) post about a report concerning the economic impact of its National Nanotechnology Initiative (NNI) from 2002 to 2022 of interest.

Following on the commercialization theme, I have noticed an increase of interest in commercializing brain and brainlike engineering technologies, as well as, more discussion about ethics.

Colonizing the brain?

UNESCO held events such as, this noted in my July 17, 2023 posting, “Unveiling the Neurotechnology Landscape: Scientific Advancements, Innovations and Major Trends—a UNESCO report” and this noted in my July 7, 2023 posting “Global dialogue on the ethics of neurotechnology on July 13, 2023 led by UNESCO.” An August 21, 2023 posting, “Ethical nanobiotechnology” adds to the discussion.

Meanwhile, Australia has been producing some very interesting mind/robot research, my June 13, 2023 posting, “Mind-controlled robots based on graphene: an Australian research story.” I have more of this kind of research (mind control or mind reading) from Australia to be published in early 2024. The Australians are not alone, there’s also this April 12, 2023 posting, “Mind-reading prosthetic limbs” from Germany.

My May 12, 2023 posting, “Virtual panel discussion: Canadian Strategies for Responsible Neurotechnology Innovation on May 16, 2023” shows Canada is entering the discussion. Unfortunately, the Canadian Science Policy Centre (CSPC), which held the event, has not posted a video online even though they have a youtube channel featuring other of their events.

As for neurmorphic engineering, China has produced a roadmap for its research in this area as noted in my March 20, 2023 posting, “A nontraditional artificial synaptic device and roadmap for Chinese research into neuromorphic devices.”

Quantum anybody?

I haven’t singled it out in this end-of-year posting but there is a great deal of interest in quantum computer both here in Canada and elsewhere. There is a 2023 report from the Council of Canadian Academies on the topic of quantum computing in Canada, which I hope to comment on soon.

Final words

I have a shout out for the Canadian Science Policy Centre, which celebrated its 15th anniversary in 2023. Congratulations!

For everyone, I wish peace on earth and all the best for you and yours in 2024!

10 years of the European Union’s roll of the dice: €1B or 1billion euros each for the Human Brain Project (HBP) and the Graphene Flagship

Graphene and Human Brain Project win biggest research award in history (& this is the 2000th post)” on January 28, 2013 was how I announced the results of what had been a a European Union (EU) competition that stretched out over several years and many stages as projects were evaluated and fell to the wayside or were allowed onto the next stage. The two finalists received €1B each to be paid out over ten years.

Human Brain Project (HBP)

A September 12, 2023 Human Brain Project (HBP) press release (also on EurekAlert) summarizes the ten year research effort and the achievements,

The EU-funded Human Brain Project (HBP) comes to an end in September and celebrates its successful conclusion today with a scientific symposium at Forschungszentrum Jülich (FZJ). The HBP was one of the first flagship projects and, with 155 cooperating institutions from 19 countries and a total budget of 607 million euros, one of the largest research projects in Europe. Forschungszentrum Jülich, with its world-leading brain research institute and the Jülich Supercomputing Centre, played an important role in the ten-year project.

“Understanding the complexity of the human brain and explaining its functionality are major challenges of brain research today”, says Astrid Lambrecht, Chair of the Board of Directors of Forschungszentrum Jülich. “The instruments of brain research have developed considerably in the last ten years. The Human Brain Project has been instrumental in driving this development – and not only gained new insights for brain research, but also provided important impulses for information technologies.”

HBP researchers have employed highly advanced methods from computing, neuroinformatics and artificial intelligence in a truly integrative approach to understanding the brain as a multi-level system. The project has contributed to a deeper understanding of the complex structure and function of the brain and enabled novel applications in medicine and technological advances.

Among the project’s highlight achievements are a three-dimensional, digital atlas of the human brain with unprecedented detail, personalised virtual models of patient brains with conditions like epilepsy and Parkinson’s, breakthroughs in the field of artificial intelligence, and an open digital research infrastructure – EBRAINS – that will remain an invaluable resource for the entire neuroscience community beyond the end of the HBP.

Researchers at the HBP have presented scientific results in over 3000 publications, as well as advanced medical and technical applications and over 160 freely accessible digital tools for neuroscience research.

“The Human Brain Project has a pioneering role for digital brain research with a unique interdisciplinary approach at the interface of neuroscience, computing and technology,” says Katrin Amunts, Director of the HBP and of the Institute for Neuroscience and Medicine at FZJ. “EBRAINS will continue to power this new way of investigating the brain and foster developments in brain medicine.”

“The impact of what you achieved in digital science goes beyond the neuroscientific community”, said Gustav Kalbe, CNECT, Acting Director of Digital Excellence and Science Infrastructures at the European Commission during the opening of the event. “The infrastructure that the Human Brain Project has established is already seen as a key building block to facilitate cooperation and research across geographical boundaries, but also across communities.”

Further information about the Human Brain Project as well as photos from research can be found here: https://fz-juelich.sciebo.de/s/hWJkNCC1Hi1PdQ5.

Results highlights and event photos in the online press release.

Results overviews:
– “Human Brain Project: Spotlights on major achievements” and “A closer Look on Scientific
Advances”

– “Human Brain Project: An extensive guide to the tools developed”

Examples of results from the Human Brain Project:

As the “Google Maps of the brain” [emphasis mine], the Human Brain Project makes the most comprehensive digital brain atlas to date available to all researchers worldwide. The atlas by Jülich researchers and collaborators combines high-resolution data of neurons, fibre connections, receptors and functional specialisations in the brain, and is designed as a constantly growing system.

13 hospitals in France are currently testing the new “Virtual Epileptic Patient” – a platform developed at the University of Marseille [Aix-Marseille University?] in the Human Brain Project. It creates personalised simulation models of brain dynamics to provide surgeons with predictions for the success of different surgical treatment strategies. The approach was presented this year in the journals Science Translational Medicine and The Lancet Neurology.



SpiNNaker2 is a “neuromorphic” [brainlike] computer developed by the University of Manchester and TU Dresden within the Human Brain Project. The company SpiNNcloud Systems in Dresden is commercialising the approach for AI applications. (Image: Sprind.org)

As an openly accessible digital infrastructure, EBRAINS offers scientists easy access to the best techniques for complex research questions.

[https://www.ebrains.eu/]

There was a Canadian connection at one time; Montréal Neuro at Canada’s McGill University was involved in developing a computational platform for neuroscience (CBRAIN) for HBP according to an announcement in my January 29, 2013 posting. However, there’s no mention of the EU project on the CBRAIN website nor is there mention of a Canadian partner on the EBRAINS website, which seemed the most likely successor to the CBRAIN portion of the HBP project originally mentioned in 2013.

I couldn’t resist “Google maps of the brain.”

In any event, the statement from Astrid Lambrecht offers an interesting contrast to that offered by the leader of the other project.

Graphene Flagship

In fact, the Graphene Flagship has been celebrating its 10th anniversary since last year; see my September 1, 2022 posting titled “Graphene Week (September 5 – 9, 2022) is a celebration of 10 years of the Graphene Flagship.”

The flagship’s lead institution, Chalmers University of Technology in Sweden, issued an August 28, 2023 press release by Lisa Gahnertz (also on the Graphene Flagship website but published September 4, 2023) touting its achievement with an ebullience I am more accustomed to seeing in US news releases,

Chalmers steers Europe’s major graphene venture to success

For the past decade, the Graphene Flagship, the EU’s largest ever research programme, has been coordinated from Chalmers with Jari Kinaret at the helm. As the project reaches the ten-year mark, expectations have been realised, a strong European research field on graphene has been established, and the journey will continue.

‘Have we delivered what we promised?’ asks Graphene Flagship Director Jari Kinaret from his office in the physics department at Chalmers, overlooking the skyline of central Gothenburg.

‘Yes, we have delivered more than anyone had a right to expect,’ [emphasis mine] he says. ‘In our analysis for the conclusion of the project, we read the documents that were written at the start. What we promised then were over a hundred specific things. Some of them were scientific and technological promises, and they have all been fulfilled. Others were for specific applications, and here 60–70 per cent of what was promised has been delivered. We have also delivered applications we did not promise from the start, but these are more difficult to quantify.’

The autumn of 2013 saw the launch of the massive ten-year Science, Technology and Innovation research programme on graphene and other related two-dimensional materials. Joint funding from the European Commission and EU Member States totalled a staggering €1,000 million. A decade later, it is clear that the large-scale initiative has succeeded in its endeavours. According to a report by the research institute WifOR, the Graphene Flagship will have created a total contribution to GDP of €3,800 million and 38,400 new jobs in the 27 EU countries between 2014 and 2030.

Exceeded expectations

‘Per euro invested and compared to other EU projects, the flagship has performed 13 times better than expected in terms of patent applications, and seven times better for scientific publications. We have 17 spin-off companies that have received over €130 million in private funding – people investing their own money is a real example of trust in the fact that the technology works,’ says Jari Kinaret.

He emphasises that the long time span has been crucial in developing the concepts of the various flagship projects.

‘When it comes to new projects, the ability to work on a long timescale is a must and is more important than a large budget. It takes a long time to build trust, both in one another within a team and in the technology on the part of investors, industry and the wider community. The size of the project has also been significant. There has been an ecosystem around the material, with many graphene manufacturers and other organisations involved. It builds robustness, which means you have the courage to invest in the material and develop it.’

From lab to application

In 2010, Andre Geim and Konstantin Novoselov of the University of Manchester won the Nobel Prize in Physics for their pioneering experiments isolating the ultra-light and ultra-thin material graphene. It was the first known 2D material and stunned the world with its ‘exceptional properties originating in the strange world of quantum physics’ according to the Nobel Foundation’s press release. Many potential applications were identified for this electrically conductive, heat-resistant and light-transmitting material. Jari Kinaret’s research team had been exploring the material since 2006, and when Kinaret learned of the European Commission’s call for a ten-year research programme, it prompted him to submit an application. The Graphene Flagship was initiated to ensure that Europe would maintain its leading position in graphene research and innovation, and its coordination and administration fell to Chalmers.

Is it a staggering thought that your initiative became the biggest EU research project of all time?

‘The fact that the three-minute presentation I gave at a meeting in Brussels has grown into an activity in 22 countries, with 170 organisations and 1,300 people involved … You can’t think about things like that because it can easily become overwhelming. Sometimes you just have to go for it,’ says Jari Kinaret.

One of the objectives of the Graphene Flagship was to take the hopes for this material and move them from lab to application. What has happened so far?

‘We are well on track with 100 products priced and on their way to the market. Many of them are business-to-business products that are not something we ordinary consumers are going to buy, but which may affect us indirectly.’

‘It’s important to remember that getting products to the application stage is a complex process. For a researcher, it may take ten working prototypes; for industry, ten million. Everything has to click into place, on a large scale. All components must work identically and in exactly the same way, and be compatible with existing production in manufacturing as you cannot rebuild an entire factory for a new material. In short, it requires reliability, reproducibility and manufacturability.’

Applications in a wide range of areas

Graphene’s extraordinary properties are being used to deliver the next generation of technologies in a wide range of fields, such as sensors for self-driving cars, advanced batteries, new water purification methods and sophisticated instruments for use in neuroscience. When asked if there are any applications that Jani Kinaret himself would like to highlight, he mentions, among other things, the applications that are underway in the automotive industry – such as sensors to detect obstacles for self-driving cars. Thanks to graphene, they will be so cost-effective to produce that it will be possible to make them available in more than just the most expensive car models.

He also highlights the aerospace industry, where a graphene material for removing ice from aircraft and helicopter wings is under development for the Airbus company. Another favourite, which he has followed from basic research to application, is the development of an air cleaner for Lufthansa passenger aircraft, based on a kind of ‘graphene foam’. Because graphene foam is very light, it can be heated extremely quickly. A pulse of electricity lasting one thousandth of a second is enough to raise the temperature to 300 degrees, thus killing micro-organisms and effectively cleaning the air in the aircraft.

He also mentions the Swedish company ABB, which has developed a graphene composite for circuit breakers in switchgear. These circuit breakers are used to protect the electricity network and must be safe to use. The graphene composite replaces the manual lubrication of the circuit breakers, resulting in significant cost savings.

‘We also see graphene being used in medical technology, but its application requires many years of testing and approval by various bodies. For example, graphene technology can more effectively map the brain before neurosurgery, as it provides a more detailed image. Another aspect of graphene is that it is soft and pliable. This means it can be used for electrodes that are implanted in the brain to treat tremors in Parkinson’s patients, without the electrodes causing scarring,’ says Jari Kinaret.

Coordinated by Chalmers

Jari Kinaret sees the fact that the EU chose Chalmers as the coordinating university as a favourable factor for the Graphene Flagship.

‘Hundreds of millions of SEK [Swedish Kroner] have gone into Chalmers research, but what has perhaps been more important is that we have become well-known and visible in certain areas. We also have the 2D-Tech competence centre and the SIO Grafen programme, both funded by Vinnova and coordinated by Chalmers and Chalmers industriteknik respectively. I think it is excellent that Chalmers was selected, as there could have been too much focus on the coordinating organisation if it had been more firmly established in graphene research at the outset.’

What challenges have been encountered during the project?

‘With so many stakeholders involved, we are not always in agreement. But that is a good thing. A management book I once read said that if two parties always agree, then one is redundant. At the start of the project, it was also interesting to see the major cultural differences we had in our communications and that different cultures read different things between the lines; it took time to realise that we should be brutally straightforward in our communications with one another.’

What has it been like to have the coordinating role that you have had?

‘Obviously, I’ve had to worry about things an ordinary physics professor doesn’t have to worry about, like a phone call at four in the morning after the Brexit vote or helping various parties with intellectual property rights. I have read more legal contracts than I thought I would ever have to read as a professor. As a researcher, your approach when you go into a role is narrow and deep, here it was rather all about breadth. I would have liked to have both, but there are only 26 hours in a day,’ jokes Jari Kinaret.

New phase for the project and EU jobs to come

A new assignment now awaits Jari Kinaret outside Chalmers as Chief Executive Officer of the EU initiative KDT JU (Key Digital Technologies Joint Undertaking, soon to become Chips JU), where industry and the public sector interact to drive the development of new electronic components and systems.

The Graphene Flagship may have reached its destination in its current form, but the work started is progressing in a form more akin to a flotilla. About a dozen projects will continue to live on under the auspices of the European Commission’s Horizon Europe programme. Chalmers is going to coordinate a smaller CSA project called GrapheneEU, where CSA stands for ‘Coordination and Support Action’. It will act as a cohesive force between the research and innovation projects that make up the next phase of the flagship, offering them a range of support and services, including communication, innovation and standardisation.

The Graphene Flagship is about to turn ten. If the project had been a ten-year-old child, what kind of child would it have been?

‘It would have been a very diverse organism. Different aspirations are beginning to emerge – perhaps it is adolescence that is approaching. In addition, within the project we have also studied other related 2D materials, and we found that there are 6,000 distinct materials of this type, of which only about 100 have been studied. So, it’s the younger siblings that are starting to arrive now.’

Facts about the Graphene Flagship:

The Graphene Flagship is the first European flagship for future and emerging technologies. It has been coordinated and administered from the Department of Physics at Chalmers, and as the project enters its next phase, GrapheneEU, coordination will continue to be carried out by staff currently working on the flagship led by Chalmers Professor Patrik Johansson.

The project has proved highly successful in developing graphene-based technology in Europe, resulting in 17 new companies, around 100 new products, nearly 500 patent applications and thousands of scientific papers. All in all, the project has exceeded the EU’s targets for utilisation from research projects by a factor of ten. According to the assessment of the EU research programme Horizon 2020, Chalmers’ coordination of the flagship has been identified as one of the key factors behind its success.

Graphene Week will be held at the Svenska Mässan in Gothenburg from 4 to 8 September 2023. Graphene Week is an international conference, which also marks the finale of the ten-year anniversary of the Graphene Flagship. The conference will be jointly led by academia and industry – Professor Patrik Johansson from Chalmers and Dr Anna Andersson from ABB – and is expected to attract over 400 researchers from Sweden, Europe and the rest of the world. The programme includes an exhibition, press conference and media activities, special sessions on innovation, diversity and ethics, and several technical sessions. The full programme is available here.

Read the press release on Graphene Week from 4 to 8 September and the overall results of the Graphene Flagship. …

Ten years and €1B each. Congratulations to the organizers on such massive undertakings. As for whether or not (and how they’ve been successful), I imagine time will tell.

Unveiling the Neurotechnology Landscape: Scientific Advancements, Innovations and Major Trends—a UNESCO report

Launched on Thursday, July 13, 2023 during UNESCO’s (United Nations Educational, Scientific, and Cultural Organization) “Global dialogue on the ethics of neurotechnology,” is a report tying together the usual measures of national scientific supremacy (number of papers published and number of patents filed) with information on corporate investment in the field. Consequently, “Unveiling the Neurotechnology Landscape: Scientific Advancements, Innovations and Major Trends” by Daniel S. Hain, Roman Jurowetzki, Mariagrazia Squicciarini, and Lihui Xu provides better insight into the international neurotechnology scene than is sometimes found in these kinds of reports. By the way, the report is open access.

Here’s what I mean, from the report‘s short summary,

Since 2013, government investments in this field have exceeded $6 billion. Private investment has also seen significant growth, with annual funding experiencing a 22-fold increase from 2010 to 2020, reaching $7.3 billion and totaling $33.2 billion.

This investment has translated into a 35-fold growth in neuroscience publications between 2000-2021 and 20-fold growth in innovations between 2022-2020, as proxied by patents. However, not all are poised to benefit from such developments, as big divides emerge.

Over 80% of high-impact neuroscience publications are produced by only ten countries, while 70% of countries contributed fewer than 10 such papers over the period considered. Similarly, five countries only hold 87% of IP5 neurotech patents.

This report sheds light on the neurotechnology ecosystem, that is, what is being developed, where and by whom, and informs about how neurotechnology interacts with other technological trajectories, especially Artificial Intelligence [emphasis mine]. [p. 2]

The money aspect is eye-opening even when you already have your suspicions. Also, it’s not entirely unexpected to learn that only ten countries produce over 80% of the high impact neurotech papers and that only five countries hold 87% of the IP5 neurotech patents but it is stunning to see it in context. (If you’re not familiar with the term ‘IP5 patents’, scroll down in this post to the relevant subhead. Hint: It means the patent was filed in one of the top five jurisdictions; I’ll leave you to guess which ones those might be.)

“Since 2013 …” isn’t quite as informative as the authors may have hoped. I wish they had given a time frame for government investments similar to what they did for corporate investments (e.g., 2010 – 2020). Also, is the $6B (likely in USD) government investment cumulative or an estimated annual number? To sum up, I would have appreciated parallel structure and specificity.

Nitpicks aside, there’s some very good material intended for policy makers. On that note, some of the analysis is beyond me. I haven’t used anything even somewhat close to their analytical tools in years and years. This commentaries reflects my interests and a very rapid reading. One last thing, this is being written from a Canadian perspective. With those caveats in mind, here’s some of what I found.

A definition, social issues, country statistics, and more

There’s a definition for neurotechnology and a second mention of artificial intelligence being used in concert with neurotechnology. From the report‘s executive summary,

Neurotechnology consists of devices and procedures used to access, monitor, investigate, assess, manipulate, and/or emulate the structure and function of the neural systems of animals or human beings. It is poised to revolutionize our understanding of the brain and to unlock innovative solutions to treat a wide range of diseases and disorders.

Similarly to Artificial Intelligence (AI), and also due to its convergence with AI, neurotechnology may have profound societal and economic impact, beyond the medical realm. As neurotechnology directly relates to the brain, it triggers ethical considerations about fundamental aspects of human existence, including mental integrity, human dignity, personal identity, freedom of thought, autonomy, and privacy [emphases mine]. Its potential for enhancement purposes and its accessibility further amplifies its prospect social and societal implications.

The recent discussions held at UNESCO’s Executive Board further shows Member States’ desire to address the ethics and governance of neurotechnology through the elaboration of a new standard-setting instrument on the ethics of neurotechnology, to be adopted in 2025. To this end, it is important to explore the neurotechnology landscape, delineate its boundaries, key players, and trends, and shed light on neurotech’s scientific and technological developments. [p. 7]

Here’s how they sourced the data for the report,

The present report addresses such a need for evidence in support of policy making in
relation to neurotechnology by devising and implementing a novel methodology on data from scientific articles and patents:

● We detect topics over time and extract relevant keywords using a transformer-
based language models fine-tuned for scientific text. Publication data for the period
2000-2021 are sourced from the Scopus database and encompass journal articles
and conference proceedings in English. The 2,000 most cited publications per year
are further used in in-depth content analysis.
● Keywords are identified through Named Entity Recognition and used to generate
search queries for conducting a semantic search on patents’ titles and abstracts,
using another language model developed for patent text. This allows us to identify
patents associated with the identified neuroscience publications and their topics.
The patent data used in the present analysis are sourced from the European
Patent Office’s Worldwide Patent Statistical Database (PATSTAT). We consider
IP5 patents filed between 2000-2020 having an English language abstract and
exclude patents solely related to pharmaceuticals.

This approach allows mapping the advancements detailed in scientific literature to the technological applications contained in patent applications, allowing for an analysis of the linkages between science and technology. This almost fully automated novel approach allows repeating the analysis as neurotechnology evolves. [pp. 8-9[

Findings in bullet points,

Key stylized facts are:
● The field of neuroscience has witnessed a remarkable surge in the overall number
of publications since 2000, exhibiting a nearly 35-fold increase over the period
considered, reaching 1.2 million in 2021. The annual number of publications in
neuroscience has nearly tripled since 2000, exceeding 90,000 publications a year
in 2021. This increase became even more pronounced since 2019.
● The United States leads in terms of neuroscience publication output (40%),
followed by the United Kingdom (9%), Germany (7%), China (5%), Canada (4%),
Japan (4%), Italy (4%), France (4%), the Netherlands (3%), and Australia (3%).
These countries account for over 80% of neuroscience publications from 2000 to
2021.
● Big divides emerge, with 70% of countries in the world having less than 10 high-
impact neuroscience publications between 2000 to 2021.
● Specific neurotechnology-related research trends between 2000 and 2021 include:
○ An increase in Brain-Computer Interface (BCI) research around 2010,
maintaining a consistent presence ever since.
○ A significant surge in Epilepsy Detection research in 2017 and 2018,
reflecting the increased use of AI and machine learning in healthcare.
○ Consistent interest in Neuroimaging Analysis, which peaks around 2004,
likely because of its importance in brain activity and language
comprehension studies.
○ While peaking in 2016 and 2017, Deep Brain Stimulation (DBS) remains a
persistent area of research, underlining its potential in treating conditions
like Parkinson’s disease and essential tremor.
● Between 2000 and 2020, the total number of patent applications in this field
increased significantly, experiencing a 20-fold increase from less than 500 to over
12,000. In terms of annual figures, a consistent upward trend in neurotechnology-10
related patent applications emerges, with a notable doubling observed between
2015 and 2020.
• The United States account for nearly half of all worldwide patent applications (47%).
Other major contributors include South Korea (11%), China (10%), Japan (7%),
Germany (7%), and France (5%). These five countries together account for 87%
of IP5 neurotech patents applied between 2000 and 2020.
○ The United States has historically led the field, with a peak around 2010, a
decline towards 2015, and a recovery up to 2020.
○ South Korea emerged as a significant contributor after 1990, overtaking
Germany in the late 2000s to become the second-largest developer of
neurotechnology. By the late 2010s, South Korea’s annual neurotechnology
patent applications approximated those of the United States.
○ China exhibits a sharp increase in neurotechnology patent applications in
the mid-2010s, bringing it on par with the United States in terms of
application numbers.
● The United States ranks highest in both scientific publications and patents,
indicating their strong ability to transform knowledge into marketable inventions.
China, France, and Korea excel in leveraging knowledge to develop patented
innovations. Conversely, countries such as the United Kingdom, Germany, Italy,
Canada, Brazil, and Australia lag behind in effectively translating neurotech
knowledge into patentable innovations.
● In terms of patent quality measured by forward citations, the leading countries are
Germany, US, China, Japan, and Korea.
● A breakdown of patents by technology field reveals that Computer Technology is
the most important field in neurotechnology, exceeding Medical Technology,
Biotechnology, and Pharmaceuticals. The growing importance of algorithmic
applications, including neural computing techniques, also emerges by looking at
the increase in patent applications in these fields between 2015-2020. Compared
to the reference year, computer technologies-related patents in neurotech
increased by 355% and by 92% in medical technology.
● An analysis of the specialization patterns of the top-5 countries developing
neurotechnologies reveals that Germany has been specializing in chemistry-
related technology fields, whereas Asian countries, particularly South Korea and
China, focus on computer science and electrical engineering-related fields. The
United States exhibits a balanced configuration with specializations in both
chemistry and computer science-related fields.
● The entities – i.e. both companies and other institutions – leading worldwide
innovation in the neurotech space are: IBM (126 IP5 patents, US), Ping An
Technology (105 IP5 patents, CH), Fujitsu (78 IP5 patents, JP), Microsoft (76 IP511
patents, US)1, Samsung (72 IP5 patents, KR), Sony (69 IP5 patents JP) and Intel
(64 IP5 patents US)

This report further proposes a pioneering taxonomy of neurotechnologies based on International Patent Classification (IPC) codes.

• 67 distinct patent clusters in neurotechnology are identified, which mirror the diverse research and development landscape of the field. The 20 most prominent neurotechnology groups, particularly in areas like multimodal neuromodulation, seizure prediction, neuromorphic computing [emphasis mine], and brain-computer interfaces, point to potential strategic areas for research and commercialization.
• The variety of patent clusters identified mirrors the breadth of neurotechnology’s potential applications, from medical imaging and limb rehabilitation to sleep optimization and assistive exoskeletons.
• The development of a baseline IPC-based taxonomy for neurotechnology offers a structured framework that enriches our understanding of this technological space, and can facilitate research, development and analysis. The identified key groups mirror the interdisciplinary nature of neurotechnology and underscores the potential impact of neurotechnology, not only in healthcare but also in areas like information technology and biomaterials, with non-negligible effects over societies and economies.

1 If we consider Microsoft Technology Licensing LLM and Microsoft Corporation as being under the same umbrella, Microsoft leads worldwide developments with 127 IP5 patents. Similarly, if we were to consider that Siemens AG and Siemens Healthcare GmbH belong to the same conglomerate, Siemens would appear much higher in the ranking, in third position, with 84 IP5 patents. The distribution of intellectual property assets across companies belonging to the same conglomerate is frequent and mirrors strategic as well as operational needs and features, among others. [pp. 9-11]

Surprises and comments

Interesting and helpful to learn that “neurotechnology interacts with other technological trajectories, especially Artificial Intelligence;” this has changed and improved my understanding of neurotechnology.

It was unexpected to find Canada in the top ten countries producing neuroscience papers. However, finding out that the country lags in translating its ‘neuro’ knowledge into patentable innovation is not entirely a surprise.

It can’t be an accident that countries with major ‘electronics and computing’ companies lead in patents. These companies do have researchers but they also buy startups to acquire patents. They (and ‘patent trolls’) will also file patents preemptively. For the patent trolls, it’s a moneymaking proposition and for the large companies, it’s a way of protecting their own interests and/or (I imagine) forcing a sale.

The mention of neuromorphic (brainlike) computing in the taxonomy section was surprising and puzzling. Up to this point, I’ve thought of neuromorphic computing as a kind of alternative or addition to standard computing but the authors have blurred the lines as per UNESCO’s definition of neurotechnology (specifically, “… emulate the structure and function of the neural systems of animals or human beings”) . Again, this report is broadening my understanding of neurotechnology. Of course, it required two instances before I quite grasped it, the definition and the taxonomy.

What’s puzzling is that neuromorphic engineering, a broader term that includes neuromorphic computing, isn’t used or mentioned. (For an explanation of the terms neuromorphic computing and neuromorphic engineering, there’s my June 23, 2023 posting, “Neuromorphic engineering: an overview.” )

The report

I won’t have time for everything. Here are some of the highlights from my admittedly personal perspective.

It’s not only about curing disease

From the report,

Neurotechnology’s applications however extend well beyond medicine [emphasis mine], and span from research, to education, to the workplace, and even people’s everyday life. Neurotechnology-based solutions may enhance learning and skill acquisition and boost focus through brain stimulation techniques. For instance, early research finds that brain- zapping caps appear to boost memory for at least one month (Berkeley, 2022). This could one day be used at home to enhance memory functions [emphasis mine]. They can further enable new ways to interact with the many digital devices we use in everyday life, transforming the way we work, live and interact. One example is the Sound Awareness wristband developed by a Stanford team (Neosensory, 2022) which enables individuals to “hear” by converting sound into tactile feedback, so that sound impaired individuals can perceive spoken words through their skin. Takagi and Nishimoto (2023) analyzed the brain scans taken through Magnetic Resonance Imaging (MRI) as individuals were shown thousands of images. They then trained a generative AI tool called Stable Diffusion2 on the brain scan data of the study’s participants, thus creating images that roughly corresponded to the real images shown. While this does not correspond to reading the mind of people, at least not yet, and some limitations of the study have been highlighted (Parshall, 2023), it nevertheless represents an important step towards developing the capability to interface human thoughts with computers [emphasis mine], via brain data interpretation.

While the above examples may sound somewhat like science fiction, the recent uptake of generative Artificial Intelligence applications and of large language models such as ChatGPT or Bard, demonstrates that the seemingly impossible can quickly become an everyday reality. At present, anyone can purchase online electroencephalogram (EEG) devices for a few hundred dollars [emphasis mine], to measure the electrical activity of their brain for meditation, gaming, or other purposes. [pp. 14-15]

This is very impressive achievement. Some of the research cited was published earlier this year (2023). The extraordinary speed is a testament to the efforts by the authors and their teams. It’s also a testament to how quickly the field is moving.

I’m glad to see the mention of and focus on consumer neurotechnology. (While the authors don’t speculate, I am free to do so.) Consumer neurotechnology could be viewed as one of the steps toward normalizing a cyborg future for all of us. Yes, we have books, television programmes, movies, and video games, which all normalize the idea but the people depicted have been severely injured and require the augmentation. With consumer neurotechnology, you have easily accessible devices being used to enhance people who aren’t injured, they just want to be ‘better’.

This phrase seemed particularly striking “… an important step towards developing the capability to interface human thoughts with computers” in light of some claims made by the Australian military in my June 13, 2023 posting “Mind-controlled robots based on graphene: an Australian research story.” (My posting has an embedded video demonstrating the Brain Robotic Interface (BRI) in action. Also, see the paragraph below the video for my ‘measured’ response.)

There’s no mention of the military in the report which seems more like a deliberate rather than inadvertent omission given the importance of military innovation where technology is concerned.

This section gives a good overview of government initiatives (in the report it’s followed by a table of the programmes),

Thanks to the promises it holds, neurotechnology has garnered significant attention from both governments and the private sector and is considered by many as an investment priority. According to the International Brain Initiative (IBI), brain research funding has become increasingly important over the past ten years, leading to a rise in large-scale state-led programs aimed at advancing brain intervention technologies(International Brain Initiative, 2021). Since 2013, initiatives such as the United States’ Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and the European Union’s Human Brain Project (HBP), as well as major national initiatives in China, Japan and South Korea have been launched with significant funding support from the respective governments. The Canadian Brain Research Strategy, initially operated as a multi- stakeholder coalition on brain research, is also actively seeking funding support from the government to transform itself into a national research initiative (Canadian Brain Research Strategy, 2022). A similar proposal is also seen in the case of the Australian Brain Alliance, calling for the establishment of an Australian Brain Initiative (Australian Academy of Science, n.d.). [pp. 15-16]

Privacy

There are some concerns such as these,

Beyond the medical realm, research suggests that emotional responses of consumers
related to preferences and risks can be concurrently tracked by neurotechnology, such
as neuroimaging and that neural data can better predict market-level outcomes than
traditional behavioral data (Karmarkar and Yoon, 2016). As such, neural data is
increasingly sought after in the consumer market for purposes such as digital
phenotyping4, neurogaming 5,and neuromarketing6 (UNESCO, 2021). This surge in demand gives rise to risks like hacking, unauthorized data reuse, extraction of privacy-sensitive information, digital surveillance, criminal exploitation of data, and other forms of abuse. These risks prompt the question of whether neural data needs distinct definition and safeguarding measures.

These issues are particularly relevant today as a wide range of electroencephalogram (EEG) headsets that can be used at home are now available in consumer markets for purposes that range from meditation assistance to controlling electronic devices through the mind. Imagine an individual is using one of these devices to play a neurofeedback game, which records the person’s brain waves during the game. Without the person being aware, the system can also identify the patterns associated with an undiagnosed mental health condition, such as anxiety. If the game company sells this data to third parties, e.g. health insurance providers, this may lead to an increase of insurance fees based on undisclosed information. This hypothetical situation would represent a clear violation of mental privacy and of unethical use of neural data.

Another example is in the field of advertising, where companies are increasingly interested in using neuroimaging to better understand consumers’ responses to their products or advertisements, a practice known as neuromarketing. For instance, a company might use neural data to determine which advertisements elicit the most positive emotional responses in consumers. While this can help companies improve their marketing strategies, it raises significant concerns about mental privacy. Questions arise in relation to consumers being aware or not that their neural data is being used, and in the extent to which this can lead to manipulative advertising practices that unfairly exploit unconscious preferences. Such potential abuses underscore the need for explicit consent and rigorous data protection measures in the use of neurotechnology for neuromarketing purposes. [pp. 21-22]

Legalities

Some countries already have laws and regulations regarding neurotechnology data,

At the national level, only a few countries have enacted laws and regulations to protect mental integrity or have included neuro-data in personal data protection laws (UNESCO, University of Milan-Bicocca (Italy) and State University of New York – Downstate Health Sciences University, 2023). Examples are the constitutional reform undertaken by Chile (Republic of Chile, 2021), the Charter for the responsible development of neurotechnologies of the Government of France (Government of France, 2022), and the Digital Rights Charter of the Government of Spain (Government of Spain, 2021). They propose different approaches to the regulation and protection of human rights in relation to neurotechnology. Countries such as the UK are also examining under which circumstances neural data may be considered as a special category of data under the general data protection framework (i.e. UK’s GDPR) (UK’s Information Commissioner’s Office, 2023) [p. 24]

As you can see, these are recent laws. There doesn’t seem to be any attempt here in Canada even though there is an act being reviewed in Parliament that could conceivably include neural data. This is from my May 1, 2023 posting,

Bill C-27 (Digital Charter Implementation Act, 2022) is what I believe is called an omnibus bill as it includes three different pieces of proposed legislation (the Consumer Privacy Protection Act [CPPA], the Artificial Intelligence and Data Act [AIDA], and the Personal Information and Data Protection Tribunal Act [PIDPTA]). [emphasis added July 11, 2023] You can read the Innovation, Science and Economic Development (ISED) Canada summary here or a detailed series of descriptions of the act here on the ISED’s Canada’s Digital Charter webpage.

My focus at the time was artificial intelligence and, now, after reading this UNESCO report and briefly looking at the Innovation, Science and Economic Development (ISED) Canada summary and a detailed series of descriptions of the act on ISED’s Canada’s Digital Charter webpage, I don’t see anything that specifies neural data but it’s not excluded either.

IP5 patents

Here’s the explanation (the footnote is included at the end of the excerpt),

IP5 patents represent a subset of overall patents filed worldwide, which have the
characteristic of having been filed in at least one top intellectual property offices (IPO)
worldwide (the so called IP5, namely the Chinese National Intellectual Property
Administration, CNIPA (formerly SIPO); the European Patent Office, EPO; the Japan
Patent Office, JPO; the Korean Intellectual Property Office, KIPO; and the United States
Patent and Trademark Office, USPTO) as well as another country, which may or may not be an IP5. This signals their potential applicability worldwide, as their inventiveness and industrial viability have been validated by at least two leading IPOs. This gives these patents a sort of “quality” check, also since patenting inventions is costly and if applicants try to protect the same invention in several parts of the world, this normally mirrors that the applicant has expectations about their importance and expected value. If we were to conduct the same analysis using information about individually considered patent applied worldwide, i.e. without filtering for quality nor considering patent families, we would risk conducting a biased analysis based on duplicated data. Also, as patentability standards vary across countries and IPOs, and what matters for patentability is the existence (or not) of prior art in the IPO considered, we would risk mixing real innovations with patents related to catching up phenomena in countries that are not at the forefront of the technology considered.

9 The five IP offices (IP5) is a forum of the five largest intellectual property offices in the world that was set up to improve the efficiency of the examination process for patents worldwide. The IP5 Offices together handle about 80% of the world’s patent applications, and 95% of all work carried out under the Patent Cooperation Treaty (PCT), see http://www.fiveipoffices.org. (Dernis et al., 2015) [p. 31]

AI assistance on this report

As noted earlier I have next to no experience with the analytical tools having not attempted this kind of work in several years. Here’s an example of what they were doing,

We utilize a combination of text embeddings based on Bidirectional Encoder
Representations from Transformer (BERT), dimensionality reduction, and hierarchical
clustering inspired by the BERTopic methodology 12 to identify latent themes within
research literature. Latent themes or topics in the context of topic modeling represent
clusters of words that frequently appear together within a collection of documents (Blei, 2012). These groupings are not explicitly labeled but are inferred through computational analysis examining patterns in word usage. These themes are ‘hidden’ within the text, only to be revealed through this analysis. …

We further utilize OpenAI’s GPT-4 model to enrich our understanding of topics’ keywords and to generate topic labels (OpenAI, 2023), thus supplementing expert review of the broad interdisciplinary corpus. Recently, GPT-4 has shown impressive results in medical contexts across various evaluations (Nori et al., 2023), making it a useful tool to enhance the information obtained from prior analysis stages, and to complement them. The automated process enhances the evaluation workflow, effectively emphasizing neuroscience themes pertinent to potential neurotechnology patents. Notwithstanding existing concerns about hallucinations (Lee, Bubeck and Petro, 2023) and errors in generative AI models, this methodology employs the GPT-4 model for summarization and interpretation tasks, which significantly mitigates the likelihood of hallucinations. Since the model is constrained to the context provided by the keyword collections, it limits the potential for fabricating information outside of the specified boundaries, thereby enhancing the accuracy and reliability of the output. [pp. 33-34]

I couldn’t resist adding the ChatGPT paragraph given all of the recent hoopla about it.

Multimodal neuromodulation and neuromorphic computing patents

I think this gives a pretty good indication of the activity on the patent front,

The largest, coherent topic, termed “multimodal neuromodulation,” comprises 535
patents detailing methodologies for deep or superficial brain stimulation designed to
address neurological and psychiatric ailments. These patented technologies interact with various points in neural circuits to induce either Long-Term Potentiation (LTP) or Long-Term Depression (LTD), offering treatment for conditions such as obsession, compulsion, anxiety, depression, Parkinson’s disease, and other movement disorders. The modalities encompass implanted deep-brain stimulators (DBS), Transcranial Magnetic Stimulation (TMS), and transcranial Direct Current Stimulation (tDCS). Among the most representative documents for this cluster are patents with titles: Electrical stimulation of structures within the brain or Systems and methods for enhancing or optimizing neural stimulation therapy for treating symptoms of Parkinson’s disease and or other movement disorders. [p.65]

Given my longstanding interest in memristors, which (I believe) have to a large extent helped to stimulate research into neuromorphic computing, this had to be included. Then, there was the brain-computer interfaces cluster,

A cluster identified as “Neuromorphic Computing” consists of 366 patents primarily
focused on devices designed to mimic human neural networks for efficient and adaptable computation. The principal elements of these inventions are resistive memory cells and artificial synapses. They exhibit properties similar to the neurons and synapses in biological brains, thus granting these devices the ability to learn and modulate responses based on rewards, akin to the adaptive cognitive capabilities of the human brain.

The primary technology classes associated with these patents fall under specific IPC
codes, representing the fields of neural network models, analog computers, and static
storage structures. Essentially, these classifications correspond to technologies that are key to the construction of computers and exhibit cognitive functions similar to human brain processes.

Examples for this cluster include neuromorphic processing devices that leverage
variations in resistance to store and process information, artificial synapses exhibiting
spike-timing dependent plasticity, and systems that allow event-driven learning and
reward modulation within neuromorphic computers.

In relation to neurotechnology as a whole, the “neuromorphic computing” cluster holds significant importance. It embodies the fusion of neuroscience and technology, thereby laying the basis for the development of adaptive and cognitive computational systems. Understanding this specific cluster provides a valuable insight into the progressing domain of neurotechnology, promising potential advancements across diverse fields, including artificial intelligence and healthcare.

The “Brain-Computer Interfaces” cluster, consisting of 146 patents, embodies a key aspect of neurotechnology that focuses on improving the interface between the brain and external devices. The technology classification codes associated with these patents primarily refer to methods or devices for treatment or protection of eyes and ears, devices for introducing media into, or onto, the body, and electric communication techniques, which are foundational elements of brain-computer interface (BCI) technologies.

Key patents within this cluster include a brain-computer interface apparatus adaptable to use environment and method of operating thereof, a double closed circuit brain-machine interface system, and an apparatus and method of brain-computer interface for device controlling based on brain signal. These inventions mainly revolve around the concept of using brain signals to control external devices, such as robotic arms, and improving the classification performance of these interfaces, even after long periods of non-use.

The inventions described in these patents improve the accuracy of device control, maintain performance over time, and accommodate multiple commands, thus significantly enhancing the functionality of BCIs.

Other identified technologies include systems for medical image analysis, limb rehabilitation, tinnitus treatment, sleep optimization, assistive exoskeletons, and advanced imaging techniques, among others. [pp. 66-67]

Having sections on neuromorphic computing and brain-computer interface patents in immediate proximity led to more speculation on my part. Imagine how much easier it would be to initiate a BCI connection if it’s powered with a neuromorphic (brainlike) computer/device. [ETA July 21, 2023: Following on from that thought, it might be more than just easier to initiate a BCI connection. Could a brainlike computer become part of your brain? Why not? it’s been successfully argued that a robotic wheelchair was part of someone’s body, see my January 30, 2013 posting and scroll down about 40% of the way.)]

Neurotech policy debates

The report concludes with this,

Neurotechnology is a complex and rapidly evolving technological paradigm whose
trajectories have the power to shape people’s identity, autonomy, privacy, sentiments,
behaviors and overall well-being, i.e. the very essence of what it means to be human.

Designing and implementing careful and effective norms and regulations ensuring that neurotechnology is developed and deployed in an ethical manner, for the good of
individuals and for society as a whole, call for a careful identification and characterization of the issues at stake. This entails shedding light on the whole neurotechnology ecosystem, that is what is being developed, where and by whom, and also understanding how neurotechnology interacts with other developments and technological trajectories, especially AI. Failing to do so may result in ineffective (at best) or distorted policies and policy decisions, which may harm human rights and human dignity.

Addressing the need for evidence in support of policy making, the present report offers first time robust data and analysis shedding light on the neurotechnology landscape worldwide. To this end, its proposes and implements an innovative approach that leverages artificial intelligence and deep learning on data from scientific publications and paten[t]s to identify scientific and technological developments in the neurotech space. The methodology proposed represents a scientific advance in itself, as it constitutes a quasi- automated replicable strategy for the detection and documentation of neurotechnology- related breakthroughs in science and innovation, to be repeated over time to account for the evolution of the sector. Leveraging this approach, the report further proposes an IPC-based taxonomy for neurotechnology which allows for a structured framework to the exploration of neurotechnology, to enable future research, development and analysis. The innovative methodology proposed is very flexible and can in fact be leveraged to investigate different emerging technologies, as they arise.

In terms of technological trajectories, we uncover a shift in the neurotechnology industry, with greater emphasis being put on computer and medical technologies in recent years, compared to traditionally dominant trajectories related to biotechnology and pharmaceuticals. This shift warrants close attention from policymakers, and calls for attention in relation to the latest (converging) developments in the field, especially AI and related methods and applications and neurotechnology.

This is all the more important and the observed growth and specialization patterns are unfolding in the context of regulatory environments that, generally, are either not existent or not fit for purpose. Given the sheer implications and impact of neurotechnology on the very essence of human beings, this lack of regulation poses key challenges related to the possible infringement of mental integrity, human dignity, personal identity, privacy, freedom of thought, and autonomy, among others. Furthermore, issues surrounding accessibility and the potential for neurotech enhancement applications triggers significant concerns, with far-reaching implications for individuals and societies. [pp. 72-73]

Last words about the report

Informative, readable, and thought-provoking. And, it helped broaden my understanding of neurotechnology.

Future endeavours?

I’m hopeful that one of these days one of these groups (UNESCO, Canadian Science Policy Centre, or ???) will tackle the issue of business bankruptcy in the neurotechnology sector. It has already occurred as noted in my ““Going blind when your neural implant company flirts with bankruptcy [long read]” April 5, 2022 posting. That story opens with a woman going blind in a New York subway when her neural implant fails. It’s how she found out the company, which supplied her implant was going out of business.

In my July 7, 2023 posting about the UNESCO July 2023 dialogue on neurotechnology, I’ve included information on Neuralink (one of Elon Musk’s companies) and its approval (despite some investigations) by the US Food and Drug Administration to start human clinical trials. Scroll down about 75% of the way to the “Food for thought” subhead where you will find stories about allegations made against Neuralink.

The end

If you want to know more about the field, the report offers a seven-page bibliography and there’s a lot of material here where you can start with this December 3, 2019 posting “Neural and technological inequalities” which features an article mentioning a discussion between two scientists. Surprisingly (to me), the source article is in Fast Company (a leading progressive business media brand), according to their tagline)..

I have two categories you may want to check: Human Enhancement and Neuromorphic Engineering. There are also a number of tags: neuromorphic computing, machine/flesh, brainlike computing, cyborgs, neural implants, neuroprosthetics, memristors, and more.

Should you have any observations or corrections, please feel free to leave them in the Comments section of this posting.

Neuromorphic engineering: an overview

In a February 13, 2023 essay, Michael Berger who runs the Nanowerk website provides an overview of brainlike (neuromorphic) engineering.

This essay is the most extensive piece I’ve seen on Berger’s website and it covers everything from the reasons why scientists are so interested in mimicking the human brain to specifics about memristors. Here are a few excerpts (Note: Links have been removed),

Neuromorphic engineering is a cutting-edge field that focuses on developing computer hardware and software systems inspired by the structure, function, and behavior of the human brain. The ultimate goal is to create computing systems that are significantly more energy-efficient, scalable, and adaptive than conventional computer systems, capable of solving complex problems in a manner reminiscent of the brain’s approach.

This interdisciplinary field draws upon expertise from various domains, including neuroscience, computer science, electronics, nanotechnology, and materials science. Neuromorphic engineers strive to develop computer chips and systems incorporating artificial neurons and synapses, designed to process information in a parallel and distributed manner, akin to the brain’s functionality.

Key challenges in neuromorphic engineering encompass developing algorithms and hardware capable of performing intricate computations with minimal energy consumption, creating systems that can learn and adapt over time, and devising methods to control the behavior of artificial neurons and synapses in real-time.

Neuromorphic engineering has numerous applications in diverse areas such as robotics, computer vision, speech recognition, and artificial intelligence. The aspiration is that brain-like computing systems will give rise to machines better equipped to tackle complex and uncertain tasks, which currently remain beyond the reach of conventional computers.

It is essential to distinguish between neuromorphic engineering and neuromorphic computing, two related but distinct concepts. Neuromorphic computing represents a specific application of neuromorphic engineering, involving the utilization of hardware and software systems designed to process information in a manner akin to human brain function.

One of the major obstacles in creating brain-inspired computing systems is the vast complexity of the human brain. Unlike traditional computers, the brain operates as a nonlinear dynamic system that can handle massive amounts of data through various input channels, filter information, store key information in short- and long-term memory, learn by analyzing incoming and stored data, make decisions in a constantly changing environment, and do all of this while consuming very little power.

The Human Brain Project [emphasis mine], a large-scale research project launched in 2013, aims to create a comprehensive, detailed, and biologically realistic simulation of the human brain, known as the Virtual Brain. One of the goals of the project is to develop new brain-inspired computing technologies, such as neuromorphic computing.

The Human Brain Project has been funded by the European Union (1B Euros over 10 years starting in 2013 and sunsetting in 2023). From the Human Brain Project Media Invite,

The final Human Brain Project Summit 2023 will take place in Marseille, France, from March 28-31, 2023.

As the ten-year European Flagship Human Brain Project (HBP) approaches its conclusion in September 2023, the final HBP Summit will highlight the scientific achievements of the project at the interface of neuroscience and technology and the legacy that it will leave for the brain research community. …

One last excerpt from the essay,

Neuromorphic computing is a radical reimagining of computer architecture at the transistor level, modeled after the structure and function of biological neural networks in the brain. This computing paradigm aims to build electronic systems that attempt to emulate the distributed and parallel computation of the brain by combining processing and memory in the same physical location.

This is unlike traditional computing, which is based on von Neumann systems consisting of three different units: processing unit, I/O unit, and storage unit. This stored program architecture is a model for designing computers that uses a single memory to store both data and instructions, and a central processing unit to execute those instructions. This design, first proposed by mathematician and computer scientist John von Neumann, is widely used in modern computers and is considered to be the standard architecture for computer systems and relies on a clear distinction between memory and processing.

I found the diagram Berger Included with von Neumann’s design contrasted with a neuromorphic design illuminating,

A graphical comparison of the von Neumann and Neuromorphic architecture. Left: The von Neumann architecture used in traditional computers. The red lines depict the data communication bottleneck in the von Neumann architecture. Right: A graphical representation of a general neuromorphic architecture. In this architecture, the processing and memory is decentralized across different neuronal units(the yellow nodes) and synapses(the black lines connecting the nodes), creating a naturally parallel computing environment via the mesh-like structure. (Source: DOI: 10.1109/IS.2016.7737434) [downloaded from https://www.nanowerk.com/spotlight/spotid=62353.php]

Berger offers a very good overview and I recommend reading his February 13, 2023 essay on neuromorphic engineering with one proviso, Note: A link has been removed,

Many researchers in this field see memristors as a key device component for neuromorphic engineering. Memristor – or memory resistor – devices are non-volatile nanoelectronic memory devices that were first theorized [emphasis mine] by Leon Chua in the 1970’s. However, it was some thirty years later that the first practical device was fabricated in 2008 by a group led by Stanley Williams [sometimes cited as R. Stanley Williams] at HP Research Labs.

Chua wasn’t the first as he, himself, has noted. Chua arrived at his theory independently in the 1970s but Bernard Widrow theorized what he called a ‘memistor’ in the 1960s. In fact “Memristors: they are older than you think” is a May 22, 2012 posting which featured an article “Two centuries of memristors” by Themistoklis Prodromakis, Christofer Toumazou and Leon Chua published in Nature Materials.

Most of us try to get it right but we don’t always succeed. It’s always good practice to read everyone (including me) with a little skepticism.

Save energy with neuromorphic (brainlike) hardware

It seems the appetite for computing power is bottomless, which presents a problem in a world where energy resources are increasingly constrained. A May 24, 2022 news item on ScienceDaily announces research into neuromorphic computing which hints the energy efficiency long promised by the technology may be realized in the foreseeable future,

For the first time TU Graz’s [Graz University of Technology; Austria] Institute of Theoretical Computer Science and Intel Labs demonstrated experimentally that a large neural network can process sequences such as sentences while consuming four to sixteen times less energy while running on neuromorphic hardware than non-neuromorphic hardware. The new research based on Intel Labs’ Loihi neuromorphic research chip that draws on insights from neuroscience to create chips that function similar to those in the biological brain.

Rich Uhlig, managing director of Intel Labs, holds one of Intel’s Nahuku boards, each of which contains 8 to 32 Intel Loihi neuromorphic chips. Intel’s latest neuromorphic system, Pohoiki Beach, is made up of multiple Nahuku boards and contains 64 Loihi chips. Pohoiki Beach was introduced in July 2019. (Credit: Tim Herman/Intel Corporation)

A May 24, 2022 Graz University of Technology (TU Graz) press release (also on EurekAlert), which originated the news item, delves further into the research, Note: Links have been removed,

The research was funded by The Human Brain Project (HBP), one of the largest research projects in the world with more than 500 scientists and engineers across Europe studying the human brain. The results of the research are published in the research paper “Memory for AI Applications in Spike-based Neuromorphic Hardware” [sic] (DOI 10.1038/s42256-022-00480-w) which in published in Nature Machine Intelligence.  

Human brain as a role model

Smart machines and intelligent computers that can autonomously recognize and infer objects and relationships between different objects are the subjects of worldwide artificial intelligence (AI) research. Energy consumption is a major obstacle on the path to a broader application of such AI methods. It is hoped that neuromorphic technology will provide a push in the right direction. Neuromorphic technology is modelled after the human brain, which is highly efficient in using energy. To process information, its hundred billion neurons consume only about 20 watts, not much more energy than an average energy-saving light bulb.

In the research, the group focused on algorithms that work with temporal processes. For example, the system had to answer questions about a previously told story and grasp the relationships between objects or people from the context. The hardware tested consisted of 32 Loihi chips.

Loihi research chip: up to sixteen times more energy-efficient than non-neuromorphic hardware

“Our system is four to sixteen times more energy-efficient than other AI models on conventional hardware,” says Philipp Plank, a doctoral student at TU Graz’s Institute of Theoretical Computer Science. Plank expects further efficiency gains as these models are migrated to the next generation of Loihi hardware, which significantly improves the performance of chip-to-chip communication.

“Intel’s Loihi research chips promise to bring gains in AI, especially by lowering their high energy cost,“ said Mike Davies, director of Intel’s Neuromorphic Computing Lab. “Our work with TU Graz provides more evidence that neuromorphic technology can improve the energy efficiency of today’s deep learning workloads by re-thinking their implementation from the perspective of biology.”

Mimicking human short-term memory

In their neuromorphic network, the group reproduced a presumed memory mechanism of the brain, as Wolfgang Maass, Philipp Plank’s doctoral supervisor at the Institute of Theoretical Computer Science, explains: “Experimental studies have shown that the human brain can store information for a short period of time even without neural activity, namely in so-called ‘internal variables’ of neurons. Simulations suggest that a fatigue mechanism of a subset of neurons is essential for this short-term memory.”

Direct proof is lacking because these internal variables cannot yet be measured, but it does mean that the network only needs to test which neurons are currently fatigued to reconstruct what information it has previously processed. In other words, previous information is stored in the non-activity of neurons, and non-activity consumes the least energy.

Symbiosis of recurrent and feed-forward network

The researchers link two types of deep learning networks for this purpose. Feedback neural networks are responsible for “short-term memory.” Many such so-called recurrent modules filter out possible relevant information from the input signal and store it. A feed-forward network then determines which of the relationships found are very important for solving the task at hand. Meaningless relationships are screened out, the neurons only fire in those modules where relevant information has been found. This process ultimately leads to energy savings.

“Recurrent neural structures are expected to provide the greatest gains for applications running on neuromorphic hardware in the future,” said Davies. “Neuromorphic hardware like Loihi is uniquely suited to facilitate the fast, sparse and unpredictable patterns of network activity that we observe in the brain and need for the most energy efficient AI applications.”

This research was financially supported by Intel and the European Human Brain Project, which connects neuroscience, medicine, and brain-inspired technologies in the EU. For this purpose, the project is creating a permanent digital research infrastructure, EBRAINS. This research work is anchored in the Fields of Expertise Human and Biotechnology and Information, Communication & Computing, two of the five Fields of Expertise of TU Graz.

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

A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware by Arjun Rao, Philipp Plank, Andreas Wild & Wolfgang Maass. Nature Machine Intelligence (2022) DOI: https://doi.org/10.1038/s42256-022-00480-w Published: 19 May 2022

This paper is behind a paywall.

For anyone interested in the EBRAINS project, here’s a description from their About page,

EBRAINS provides digital tools and services which can be used to address challenges in brain research and brain-inspired technology development. Its components are designed with, by, and for researchers. The tools assist scientists to collect, analyse, share, and integrate brain data, and to perform modelling and simulation of brain function.

EBRAINS’ goal is to accelerate the effort to understand human brain function and disease.

This EBRAINS research infrastructure is the entry point for researchers to discover EBRAINS services. The services are being developed and powered by the EU-funded Human Brain Project.

You can register to use the EBRAINS research infrastructure HERE

One last note, the Human Brain Project is a major European Union (EU)-funded science initiative (1B Euros) announced in 2013 and to be paid out over 10 years.

Human Brain Project: update

The European Union’s Human Brain Project was announced in January 2013. It, along with the Graphene Flagship, had won a multi-year competition for the extraordinary sum of one million euros each to be paid out over a 10-year period. (My January 28, 2013 posting gives the details available at the time.)

At a little more than half-way through the project period, Ed Yong, in his July 22, 2019 article for The Atlantic, offers an update (of sorts),

Ten years ago, a neuroscientist said that within a decade he could simulate a human brain. Spoiler: It didn’t happen.

On July 22, 2009, the neuroscientist Henry Markram walked onstage at the TEDGlobal conference in Oxford, England, and told the audience that he was going to simulate the human brain, in all its staggering complexity, in a computer. His goals were lofty: “It’s perhaps to understand perception, to understand reality, and perhaps to even also understand physical reality.” His timeline was ambitious: “We can do it within 10 years, and if we do succeed, we will send to TED, in 10 years, a hologram to talk to you.” …

It’s been exactly 10 years. He did not succeed.

One could argue that the nature of pioneers is to reach far and talk big, and that it’s churlish to single out any one failed prediction when science is so full of them. (Science writers joke that breakthrough medicines and technologies always seem five to 10 years away, on a rolling window.) But Markram’s claims are worth revisiting for two reasons. First, the stakes were huge: In 2013, the European Commission awarded his initiative—the Human Brain Project (HBP)—a staggering 1 billion euro grant (worth about $1.42 billion at the time). Second, the HBP’s efforts, and the intense backlash to them, exposed important divides in how neuroscientists think about the brain and how it should be studied.

Markram’s goal wasn’t to create a simplified version of the brain, but a gloriously complex facsimile, down to the constituent neurons, the electrical activity coursing along them, and even the genes turning on and off within them. From the outset, the criticism to this approach was very widespread, and to many other neuroscientists, its bottom-up strategy seemed implausible to the point of absurdity. The brain’s intricacies—how neurons connect and cooperate, how memories form, how decisions are made—are more unknown than known, and couldn’t possibly be deciphered in enough detail within a mere decade. It is hard enough to map and model the 302 neurons of the roundworm C. elegans, let alone the 86 billion neurons within our skulls. “People thought it was unrealistic and not even reasonable as a goal,” says the neuroscientist Grace Lindsay, who is writing a book about modeling the brain.
And what was the point? The HBP wasn’t trying to address any particular research question, or test a specific hypothesis about how the brain works. The simulation seemed like an end in itself—an overengineered answer to a nonexistent question, a tool in search of a use. …

Markram seems undeterred. In a recent paper, he and his colleague Xue Fan firmly situated brain simulations within not just neuroscience as a field, but the entire arc of Western philosophy and human civilization. And in an email statement, he told me, “Political resistance (non-scientific) to the project has indeed slowed us down considerably, but it has by no means stopped us nor will it.” He noted the 140 people still working on the Blue Brain Project, a recent set of positive reviews from five external reviewers, and its “exponentially increasing” ability to “build biologically accurate models of larger and larger brain regions.”

No time frame, this time, but there’s no shortage of other people ready to make extravagant claims about the future of neuroscience. In 2014, I attended TED’s main Vancouver conference and watched the opening talk, from the MIT Media Lab founder Nicholas Negroponte. In his closing words, he claimed that in 30 years, “we are going to ingest information. …

I’m happy to see the update. As I recall, there was murmuring almost immediately about the Human Brain Project (HBP). I never got details but it seemed that people were quite actively unhappy about the disbursements. Of course, this kind of uproar is not unusual when great sums of money are involved and the Graphene Flagship also had its rocky moments.

As for Yong’s contribution, I’m glad he’s debunking some of the hype and glory associated with the current drive to colonize the human brain and other efforts (e.g. genetics) which they often claim are the ‘future of medicine’.

To be fair. Yong is focused on the brain simulation aspect of the HBP (and Markram’s efforts in the Blue Brain Project) but there are other HBP efforts, as well, even if brain simulation seems to be the HBP’s main interest.

After reading the article, I looked up Henry Markram’s Wikipedia entry and found this,

In 2013, the European Union funded the Human Brain Project, led by Markram, to the tune of $1.3 billion. Markram claimed that the project would create a simulation of the entire human brain on a supercomputer within a decade, revolutionising the treatment of Alzheimer’s disease and other brain disorders. Less than two years into it, the project was recognised to be mismanaged and its claims overblown, and Markram was asked to step down.[7][8]

On 8 October 2015, the Blue Brain Project published the first digital reconstruction and simulation of the micro-circuitry of a neonatal rat somatosensory cortex.[9]

I also looked up the Human Brain Project and, talking about their other efforts, was reminded that they have a neuromorphic computing platform, SpiNNaker (mentioned here in a January 24, 2019 posting; scroll down about 50% of the way). For anyone unfamiliar with the term, neuromorphic computing/engineering is what scientists call the effort to replicate the human brain’s ability to synthesize and process information in computing processors.

In fact, there was some discussion in 2013 that the Human Brain Project and the Graphene Flagship would have some crossover projects, e.g., trying to make computers more closely resemble human brains in terms of energy use and processing power.

The Human Brain Project’s (HBP) Silicon Brains webpage notes this about their neuromorphic computing platform,

Neuromorphic computing implements aspects of biological neural networks as analogue or digital copies on electronic circuits. The goal of this approach is twofold: Offering a tool for neuroscience to understand the dynamic processes of learning and development in the brain and applying brain inspiration to generic cognitive computing. Key advantages of neuromorphic computing compared to traditional approaches are energy efficiency, execution speed, robustness against local failures and the ability to learn.

Neuromorphic Computing in the HBP

In the HBP the neuromorphic computing Subproject carries out two major activities: Constructing two large-scale, unique neuromorphic machines and prototyping the next generation neuromorphic chips.

The large-scale neuromorphic machines are based on two complementary principles. The many-core SpiNNaker machine located in Manchester [emphasis mine] (UK) connects 1 million ARM processors with a packet-based network optimized for the exchange of neural action potentials (spikes). The BrainScaleS physical model machine located in Heidelberg (Germany) implements analogue electronic models of 4 Million neurons and 1 Billion synapses on 20 silicon wafers. Both machines are integrated into the HBP collaboratory and offer full software support for their configuration, operation and data analysis.

The most prominent feature of the neuromorphic machines is their execution speed. The SpiNNaker system runs at real-time, BrainScaleS is implemented as an accelerated system and operates at 10,000 times real-time. Simulations at conventional supercomputers typical run factors of 1000 slower than biology and cannot access the vastly different timescales involved in learning and development ranging from milliseconds to years.

Recent research in neuroscience and computing has indicated that learning and development are a key aspect for neuroscience and real world applications of cognitive computing. HBP is the only project worldwide addressing this need with dedicated novel hardware architectures.

I’ve highlighted Manchester because that’s a very important city where graphene is concerned. The UK’s National Graphene Institute is housed at the University of Manchester where graphene was first isolated in 2004 by two scientists, Andre Geim and Konstantin (Kostya) Novoselov. (For their effort, they were awarded the Nobel Prize for physics in 2010.)

Getting back to the HBP (and the Graphene Flagship for that matter), the funding should be drying up sometime around 2023 and I wonder if it will be possible to assess the impact.