Tag Archives: neurons

Better neuroprostheses for brain diseases and mental illneses

I don’t often get news releases from Sweden but I do on occasion and, sometimes, they even come in their original Swedish versions. In this case, Lund University sent me an English language version about their latest work making brain implants (neural prostheses) safer and effective. From a Sept. 29, 2015 Lund University news release (also on EurekAlert),

Neurons thrive and grow in a new type of nanowire material developed by researchers in Nanophysics and Ophthalmology at Lund University in Sweden. In time, the results might improve both neural and retinal implants, and reduce the risk of them losing their effectiveness over time, which is currently a problem

By implanting electrodes in the brain tissue one can stimulate or capture signals from different areas of the brain. These types of brain implants, or neuro-prostheses as they are sometimes called, are used to treat Parkinson’s disease and other neurological diseases.

They are currently being tested in other areas, such as depression, severe cases of autism, obsessive-compulsive disorders and paralysis. Another research track is to determine whether retinal implants are able to replace light-sensitive cells that die in cases of Retinitis Pigmentosa and other eye diseases.

However, there are severe drawbacks associated with today’s implants. One problem is that the body interprets the implants as foreign objects, resulting in an encapsulation of the electrode, which in turn leads to loss of signal.

One of the researchers explains the approach adopted by the research team (from the news release),

“Our nanowire structure prevents the cells that usually encapsulate the electrodes – glial cells – from doing so”, says Christelle Prinz, researcher in Nanophysics at Lund University in Sweden, who developed this technique together with Maria Thereza Perez, a researcher in Ophthalmology.

“I was very pleasantly surprised by these results. In previous in-vitro experiments, the glial cells usually attach strongly to the electrodes”, she says.

To avoid this, the researchers have developed a small substrate where regions of super thin nanowires are combined with flat regions. While neurons grow and extend processes on the nanowires, the glial cells primarily occupy the flat regions in between.

“The different types of cells continue to interact. This is necessary for the neurons to survive because the glial cells provide them with important molecules.”

So far, tests have only been done with cultured cells (in vitro) but hopefully they will soon be able to continue with experiments in vivo.

The substrate is made from the semiconductor material gallium phosphide where each outgrowing nanowire has a diameter of only 80 nanometres (billionths of a metre).

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

Support of Neuronal Growth Over Glial Growth and Guidance of Optic Nerve Axons by Vertical Nanowire Arrays by Gaëlle Piret, Maria-Thereza Perez, and Christelle N. Prinz. ACS Appl. Mater. Interfaces, 2015, 7 (34), pp 18944–18948 DOI: 10.1021/acsami.5b03798 Publication Date (Web): August 11, 2015

Copyright © 2015 American Chemical Society

This paper appears to be open access as I was able to link to the PDF version.

Nanoscale imaging of a mouse brain

Researchers have developed a new brain imaging tool they would like to use as a founding element for a national brain observatory. From a July 30, 2015 news item on Azonano,

A new imaging tool developed by Boston scientists could do for the brain what the telescope did for space exploration.

In the first demonstration of how the technology works, published July 30 in the journal Cell, the researchers look inside the brain of an adult mouse at a scale previously unachievable, generating images at a nanoscale resolution. The inventors’ long-term goal is to make the resource available to the scientific community in the form of a national brain observatory.

A July 30, 2015 Cell Press news release on EurekAlert, which originated the news item, expands on the theme,

“I’m a strong believer in bottom up-science, which is a way of saying that I would prefer to generate a hypothesis from the data and test it,” says senior study author Jeff Lichtman, of Harvard University. “For people who are imagers, being able to see all of these details is wonderful and we’re getting an opportunity to peer into something that has remained somewhat intractable for so long. It’s about time we did this, and it is what people should be doing about things we don’t understand.”

The researchers have begun the process of mining their imaging data by looking first at an area of the brain that receives sensory information from mouse whiskers, which help the animals orient themselves and are even more sensitive than human fingertips. The scientists used a program called VAST, developed by co-author Daniel Berger of Harvard and the Massachusetts Institute of Technology, to assign different colors and piece apart each individual “object” (e.g., neuron, glial cell, blood vessel cell, etc.).

“The complexity of the brain is much more than what we had ever imagined,” says study first author Narayanan “Bobby” Kasthuri, of the Boston University School of Medicine. “We had this clean idea of how there’s a really nice order to how neurons connect with each other, but if you actually look at the material it’s not like that. The connections are so messy that it’s hard to imagine a plan to it, but we checked and there’s clearly a pattern that cannot be explained by randomness.”

The researchers see great potential in the tool’s ability to answer questions about what a neurological disorder actually looks like in the brain, as well as what makes the human brain different from other animals and different between individuals. Who we become is very much a product of the connections our neurons make in response to various life experiences. To be able to compare the physical neuron-to-neuron connections in an infant, a mathematical genius, and someone with schizophrenia would be a leap in our understanding of how our brains shape who we are (or vice versa).

The cost and data storage demands for this type of research are still high, but the researchers expect expenses to drop over time (as has been the case with genome sequencing). To facilitate data sharing, the scientists are now partnering with Argonne National Laboratory with the hopes of creating a national brain laboratory that neuroscientists around the world can access within the next few years.

“It’s bittersweet that there are many scientists who think this is a total waste of time as well as a big investment in money and effort that could be better spent answering questions that are more proximal,” Lichtman says. “As long as data is showing you things that are unexpected, then you’re definitely doing the right thing. And we are certainly far from being out of the surprise element. There’s never a time when we look at this data that we don’t see something that we’ve never seen before.”

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

Saturated Reconstruction of a Volume of Neocortex by Narayanan Kasthuri, Kenneth Jeffrey Hayworth, Daniel Raimund Berger, Richard Lee Schalek, José Angel Conchello, Seymour Knowles-Barley, Dongil Lee, Amelio Vázquez-Reina, Verena Kaynig, Thouis Raymond Jones, Mike Roberts, Josh Lyskowski Morgan, Juan Carlos Tapia, H. Sebastian Seung, William Gray Roncal, Joshua Tzvi Vogelstein, Randal Burns, Daniel Lewis Sussman, Carey Eldin Priebe, Hanspeter Pfister, Jeff William Lichtman. Cell Volume 162, Issue 3, p648–661, 30 July 2015 DOI: http://dx.doi.org/10.1016/j.cell.2015.06.054

This appears to be an open access paper.

Metallic nanoflowers produce neuron-like fractals

I was a bit surprised to find that this University of Oregon story was about a patent. Here’s more from a July 28, 2015 news item on Azonano,

Richard Taylor’s vision of using artificial fractal-based implants to restore sight to the blind — part of a far-reaching concept that won an innovation award this year from the White House — is now covered under a broad U.S. patent.

The patent goes far beyond efforts to use the emerging technology to restore eyesight. It covers all fractal-designed electronic implants that link signaling activity with nerves for any purpose in animal and human biology.

Fractals are objects with irregular curves or shapes. “They are a trademark building block of nature,” said Taylor, a professor of physics and director of the Materials Science Institute at the University of Oregon [UO]. “In math, that property is self-similarity. Trees, clouds, rivers, galaxies, lungs and neurons are fractals. What we hope to do is adapt the technology to nature’s geometry.”

Named in U.S. patent 9079017 are Taylor, the UO, Taylor’s research collaborator Simon Brown, and Brown’s home institution, the University of Canterbury in New Zealand.

A July 28, 2015 University of Oregon news release (also on EurekAlert) by Jim Barlow, which originated the news item, continues the patent celebration,

“We’re very delighted,” Taylor said. “The U.S. Patent and Trademark Office has recognized the novelty and utility of our general concept, but there is a lot to do. We want to get all of the fundamental science sorted out. We’re looking at least another couple of years of basic science before moving forward.”

The patent solidifies the relationship between the two universities, said Charles Williams, associate vice president for innovation at the UO. “This is still in the very early days. This project has attracted national attention, awards and grants.

“We hope to engage the right set of partners to develop the technology over time as the concept moves into potentially vast forms of medical applications,” Williams added. “Dr. Taylor’s interdisciplinary science is a hallmark of the creativity at the University of Oregon and a great example of the international research collaborations that our faculty engage in every day.”

Here’s an image illustrating the ‘fractal neurons’,


Caption: Retinal neurons, outlined in yellow, attach to and follows branches of a fractal interconnect. Such connections, says University of Oregon physicist Richard Taylor, could some day help to treat eye diseases such as macular degeneration. Credit: Courtesy of Richard Taylor

The news release goes on to describe the ‘fractal approach’ to eye implants which is markedly different from the implants entering the marketplace,

Taylor raised the idea of a fractal-based approach to treat eye diseases in a 2011 article in Physics World, writing that it could overcome problems associated with efforts to insert photodiodes behind the eyes. Current chip technology doesn’t allow sufficient connections with neurons.

“The wiring — the neurons — in the retina is fractal, but the chips are not fractal,” Taylor said. His vision, based on research with Brown, is to grow nanoflowers seeded from nanoparticles of metals that self assemble in a natural process, producing fractals that mimic and communicate with neurons.

It is conceivable, Taylor said, that fractal interconnects — as the implants are called in the patent — could be shaped so they network with like-shaped neurons to address narrow needs, such as a feedback loop for the sensation of touch from a prosthetic arm or leg to the brain.

Such implants would overcome the biological rejection of implants with smooth surfaces or those randomly patterned that have been developed in a trial-and-error approach to link to neurons.

Once perfected, he said, the implants would generate an electrical field that would fool a sea of glial cells that insulate and protect neurons from foreign invaders. Fractal interconnects would allow electrical signals to operate in “a safety zone biologically” that avoids toxicity issues.

“The patent covers any generic interface for connecting any electronics to any nerve,” Taylor said, adding that fractal interconnects are not electrodes. “Our interface is multifunctional. The primary thing is to get the electrical field into the system so that reaches the neurons and induces the signal.”

Taylor’s proposal for using fractal-based technology earned the top prize in a contest held by the innovation company InnoCentive. Taylor was honored in April [2015] at a meeting of the White House Office of Science and Technology Policy.

The competition was sponsored by a collaboration of science philanthropies including the Research Corporation for Science Advancement, the Gordon and Betty Moore Foundation, the W.M. Keck Foundation, the Kavli Foundation, the Templeton Foundation and the Burroughs Wellcome Fund.

You can find out more about InnoCentive here. As for other types of artificial eye implants, the latest here is a June 30, 2015 post titled, Clinical trial for bionic eye (artificial retinal implant) shows encouraging results (safety and efficacy).

On the verge of controlling neurons by wireless?

Scientists have controlled a mouse’s neurons with a wireless device (and unleashed some paranoid fantasies? well, mine if no one else’s) according to a July 16, 2015 news item on Nanowerk (Note: A link has been removed),

A study showed that scientists can wirelessly determine the path a mouse walks with a press of a button. Researchers at the Washington University School of Medicine, St. Louis, and University of Illinois, Urbana-Champaign, created a remote controlled, next-generation tissue implant that allows neuroscientists to inject drugs and shine lights on neurons deep inside the brains of mice. The revolutionary device is described online in the journal Cell (“Wireless Optofluidic Systems for Programmable In Vivo Pharmacology and Optogenetics”). Its development was partially funded by the [US] National Institutes of Health [NIH].

The researchers have made an image/illustration of the probe available,

Mind Bending Probe Scientists used soft materials to create a brain implant a tenth the width of a human hair that can wirelessly control neurons with lights and drugs. Courtesy of Jeong lab, University of Colorado Boulder.

A July 16, 2015 US NIH National Institute of Neurological Disorders and Stroke news release, which originated the news item, describes the study and notes that instructions for building the implant are included in the published study,

“It unplugs a world of possibilities for scientists to learn how brain circuits work in a more natural setting.” said Michael R. Bruchas, Ph.D., associate professor of anesthesiology and neurobiology at Washington University School of Medicine and a senior author of the study.

The Bruchas lab studies circuits that control a variety of disorders including stress, depression, addiction, and pain. Typically, scientists who study these circuits have to choose between injecting drugs through bulky metal tubes and delivering lights through fiber optic cables. Both options require surgery that can damage parts of the brain and introduce experimental conditions that hinder animals’ natural movements.

To address these issues, Jae-Woong Jeong, Ph.D., a bioengineer formerly at the University of Illinois at Urbana-Champaign, worked with Jordan G. McCall, Ph.D., a graduate student in the Bruchas lab, to construct a remote controlled, optofluidic implant. The device is made out of soft materials that are a tenth the diameter of a human hair and can simultaneously deliver drugs and lights.

“We used powerful nano-manufacturing strategies to fabricate an implant that lets us penetrate deep inside the brain with minimal damage,” said John A. Rogers, Ph.D., professor of materials science and engineering, University of Illinois at Urbana-Champaign and a senior author. “Ultra-miniaturized devices like this have tremendous potential for science and medicine.”

With a thickness of 80 micrometers and a width of 500 micrometers, the optofluidic implant is thinner than the metal tubes, or cannulas, scientists typically use to inject drugs. When the scientists compared the implant with a typical cannula they found that the implant damaged and displaced much less brain tissue.

The scientists tested the device’s drug delivery potential by surgically placing it into the brains of mice. In some experiments, they showed that they could precisely map circuits by using the implant to inject viruses that label cells with genetic dyes. In other experiments, they made mice walk in circles by injecting a drug that mimics morphine into the ventral tegmental area (VTA), a region that controls motivation and addiction.

The researchers also tested the device’s combined light and drug delivery potential when they made mice that have light-sensitive VTA neurons stay on one side of a cage by commanding the implant to shine laser pulses on the cells. The mice lost the preference when the scientists directed the device to simultaneously inject a drug that blocks neuronal communication. In all of the experiments, the mice were about three feet away from the command antenna.

“This is the kind of revolutionary tool development that neuroscientists need to map out brain circuit activity,” said James Gnadt, Ph.D., program director at the NIH’s National Institute of Neurological Disorders and Stroke (NINDS).  “It’s in line with the goals of the NIH’s BRAIN Initiative.”

The researchers fabricated the implant using semi-conductor computer chip manufacturing techniques. It has room for up to four drugs and has four microscale inorganic light-emitting diodes. They installed an expandable material at the bottom of the drug reservoirs to control delivery. When the temperature on an electric heater beneath the reservoir rose then the bottom rapidly expanded and pushed the drug out into the brain.

“We tried at least 30 different prototypes before one finally worked,” said Dr. McCall.

“This was truly an interdisciplinary effort,” said Dr. Jeong, who is now an assistant professor of electrical, computer, and energy engineering at University of Colorado Boulder. “We tried to engineer the implant to meet some of neurosciences greatest unmet needs.”

In the study, the scientists provide detailed instructions for manufacturing the implant.

“A tool is only good if it’s used,” said Dr. Bruchas. “We believe an open, crowdsourcing approach to neuroscience is a great way to understand normal and healthy brain circuitry.”

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

Wireless Optofluidic Systems for Programmable In Vivo Pharmacology and Optogenetics by Jae-Woong Jeong, Jordan G. McCall, Gunchul Shin, Yihui Zhang, Ream Al-Hasani, Minku Kim, Shuo Li, Joo Yong Sim, Kyung-In Jang, Yan Shi, Daniel Y. Hong, Yuhao Liu, Gavin P. Schmitz, Li Xia, Zhubin He, Paul Gamble, Wilson Z. Ray, Yonggang Huang, Michael R. Bruchas, and John A. Rogers.  Cell, July 16, 2015. DOI: 10.1016/j.cell.2015.06.058

This paper is behind a paywall.

I last wrote about wireless activation of neurons in a May 28, 2014 posting which featured research at the University of Massachusetts Medical School.

Is it time to invest in a ‘brain chip’ company?

This story take a few twists and turns. First, ‘brain chips’ as they’re sometimes called would allow, theoretically, computers to learn and function like human brains. (Note: There’s another type of ‘brain chip’ which could be implanted in human brains to help deal with diseases such as Parkinson’s and Alzheimer’s. *Today’s [June 26, 2015] earlier posting about an artificial neuron points at some of the work being done in this areas.*)

Returning to the ‘brain ship’ at hand. Second, there’s a company called BrainChip, which has one patent and another pending for, yes, a ‘brain chip’.

The company, BrainChip, founded in Australia and now headquartered in California’s Silicon Valley, recently sparked some investor interest in Australia. From an April 7, 2015 article by Timna Jacks for the Australian Financial Review,

Former mining stock Aziana Limited has whet Australian investors’ appetite for science fiction, with its share price jumping 125 per cent since it announced it was acquiring a US-based tech company called BrainChip, which promises artificial intelligence through a microchip that replicates the neural system of the human brain.

Shares in the company closed at 9¢ before the Easter long weekend, having been priced at just 4¢ when the backdoor listing of BrainChip was announced to the market on March 18.

Creator of the patented digital chip, Peter Van Der Made told The Australian Financial Review the technology has the capacity to learn autonomously, due to its composition of 10,000 biomimic neurons, which, through a process known as synaptic time-dependent plasticity, can form memories and associations in the same way as a biological brain. He said it works 5000 times faster and uses a thousandth of the power of the fastest computers available today.

Mr Van Der Made is inviting technology partners to license the technology for their own chips and products, and is donating the technology to university laboratories in the US for research.

The Netherlands-born Australian, now based in southern California, was inspired to create the brain-like chip in 2004, after working at the IBM Internet Security Systems for two years, where he was chief scientist for behaviour analysis security systems. …

A June 23, 2015 article by Tony Malkovic on phys.org provide a few more details about BrainChip and about the deal,

Mr Van der Made and the company, also called BrainChip, are now based in Silicon Valley in California and he returned to Perth last month as part of the company’s recent merger and listing on the Australian Stock Exchange.

He says BrainChip has the ability to learn autonomously, evolve and associate information and respond to stimuli like a brain.

Mr Van der Made says the company’s chip technology is more than 5,000 faster than other technologies, yet uses only 1/1,000th of the power.

“It’s a hardware only solution, there is no software to slow things down,” he says.

“It doesn’t executes instructions, it learns and supplies what it has learnt to new information.

“BrainChip is on the road to position itself at the forefront of artificial intelligence,” he says.

“We have a clear advantage, at least 10 years, over anybody else in the market, that includes IBM.”

BrainChip is aiming at the global semiconductor market involving almost anything that involves a microprocessor.

You can find out more about the company, BrainChip here. The site does have a little more information about the technology,

Spiking Neuron Adaptive Processor (SNAP)

BrainChip’s inventor, Peter van der Made, has created an exciting new Spiking Neural Networking technology that has the ability to learn autonomously, evolve and associate information just like the human brain. The technology is developed as a digital design containing a configurable “sea of biomimic neurons’.

The technology is fast, completely digital, and consumes very low power, making it feasible to integrate large networks into portable battery-operated products, something that has never been possible before.

BrainChip neurons autonomously learn through a process known as STDP (Synaptic Time Dependent Plasticity). BrainChip’s fully digital neurons process input spikes directly in hardware. Sensory neurons convert physical stimuli into spikes. Learning occurs when the input is intense, or repeating through feedback and this is directly correlated to the way the brain learns.

Computing Artificial Neural Networks (ANNs)

The brain consists of specialized nerve cells that communicate with one another. Each such nerve cell is called a Neuron,. The inputs are memory nodes called synapses. When the neuron associates information, it produces a ‘spike’ or a ‘spike train’. Each spike is a pulse that triggers a value in the next synapse. Synapses store values, similar to the way a computer stores numbers. In combination, these values determine the function of the neural network. Synapses acquire values through learning.

In Artificial Neural Networks (ANNs) this complex function is generally simplified to a static summation and compare function, which severely limits computational power. BrainChip has redefined how neural networks work, replicating the behaviour of the brain. BrainChip’s artificial neurons are completely digital, biologically realistic resulting in increased computational power, high speed and extremely low power consumption.

The Problem with Artificial Neural Networks

Standard ANNs, running on computer hardware are processed sequentially; the processor runs a program that defines the neural network. This consumes considerable time and because these neurons are processed sequentially, all this delayed time adds up resulting in a significant linear decline in network performance with size.

BrainChip neurons are all mapped in parallel. Therefore the performance of the network is not dependent on the size of the network providing a clear speed advantage. So because there is no decline in performance with network size, learning also takes place in parallel within each synapse, making STDP learning very fast.

A hardware solution

BrainChip’s digital neural technology is the only custom hardware solution that is capable of STDP learning. The hardware requires no coding and has no software as it evolves learning through experience and user direction.

The BrainChip neuron is unique in that it is completely digital, behaves asynchronously like an analog neuron, and has a higher level of biological realism. It is more sophisticated than software neural models and is many orders of magnitude faster. The BrainChip neuron consists entirely of binary logic gates with no traditional CPU core. Hence, there are no ‘programming’ steps. Learning and training takes the place of programming and coding. Like of a child learning a task for the first time.

Software ‘neurons’, to compromise for limited processing power, are simplified to a point where they do not resemble any of the features of a biological neuron. This is due to the sequential nature of computers, whereby all data has to pass through a central processor in chunks of 16, 32 or 64 bits. In contrast, the brain’s network is parallel and processes the equivalent of millions of data bits simultaneously.

A significantly faster technology

Performing emulation in digital hardware has distinct advantages over software. As software is processed sequentially, one instruction at a time, Software Neural Networks perform slower with increasing size. Parallel hardware does not have this problem and maintains the same speed no matter how large the network is. Another advantage of hardware is that it is more power efficient by several orders of magnitude.

The speed of the BrainChip device is unparalleled in the industry.

For large neural networks a GPU (Graphics Processing Unit) is ~70 times faster than the Intel i7 executing a similar size neural network. The BrainChip neural network is faster still and takes far fewer CPU (Central Processing Unit) cycles, with just a little communication overhead, which means that the CPU is available for other tasks. The BrainChip network also responds much faster than a software network accelerating the performance of the entire system.

The BrainChip network is completely parallel, with no sequential dependencies. This means that the network does not slow down with increasing size.

Endorsed by the neuroscience community

A number of the world’s pre-eminent neuroscientists have endorsed the technology and are agreeing to joint develop projects.

BrainChip has the potential to become the de facto standard for all autonomous learning technology and computer products.


BrainChip’s autonomous learning technology patent was granted on the 21st September 2008 (Patent number US 8,250,011 “Autonomous learning dynamic artificial neural computing device and brain inspired system”). BrainChip is the only company in the world to have achieved autonomous learning in a network of Digital Neurons without any software.

A prototype Spiking Neuron Adaptive Processor was designed as a ‘proof of concept’ chip.

The first tests were completed at the end of 2007 and this design was used as the foundation for the US patent application which was filed in 2008. BrainChip has also applied for a continuation-in-part patent filed in 2012, the “Method and System for creating Dynamic Neural Function Libraries”, US Patent Application 13/461,800 which is pending.

Van der Made doesn’t seem to have published any papers on this work and the description of the technology provided on the website is frustratingly vague. There are many acronyms for processes but no mention of what this hardware might be. For example, is it based on a memristor or some kind of atomic ionic switch or something else altogether?

It would be interesting to find out more but, presumably, van der Made, wishes to withhold details. There are many companies following the same strategy while pursuing what they view as a business advantage.

* Artificial neuron link added June 26, 2015 at 1017 hours PST.

Gold and your neurons

Should you need any electrode implants for your neurons at some point in the future, it’s possible they could be coated with gold. Researchers at the Lawrence Livermore National Laboratory (LLNL) and at the University of California at Davis (UC Davis) have discovered that electrodes covered in nanoporous gold could prevent scarring (from a May 5, 2015 news item on Azonano),

A team of researchers from Lawrence Livermore and UC Davis have found that covering an implantable neural electrode with nanoporous gold could eliminate the risk of scar tissue forming over the electrode’s surface.

The team demonstrated that the nanostructure of nanoporous gold achieves close physical coupling of neurons by maintaining a high neuron-to-astrocyte surface coverage ratio. Close physical coupling between neurons and the electrode plays a crucial role in recording fidelity of neural electrical activity.

An April 30, 2015 LLNL news release, which originated the news item, details the scarring issue and offers more information about the proposed solution,

Neural interfaces (e.g., implantable electrodes or multiple-electrode arrays) have emerged as transformative tools to monitor and modify neural electrophysiology, both for fundamental studies of the nervous system, and to diagnose and treat neurological disorders. These interfaces require low electrical impedance to reduce background noise and close electrode-neuron coupling for enhanced recording fidelity.

Designing neural interfaces that maintain close physical coupling of neurons to an electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. An important obstacle in maintaining robust neuron-electrode coupling is the encapsulation of the electrode by scar tissue.

Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface, which is an obstacle to reliable neuron−electrode coupling.

However, the team found that nanoporous gold, produced by an alloy corrosion process, is a promising candidate to reduce scar tissue formation on the electrode surface solely through topography by taking advantage of its tunable length scale.

“Our results show that nanoporous gold topography, not surface chemistry, reduces astrocyte surface coverage,” said Monika Biener, one of the LLNL authors of the paper.

Nanoporous gold has attracted significant interest for its use in electrochemical sensors, catalytic platforms, fundamental structure−property studies at the nanoscale and tunable drug release. It also features high effective surface area, tunable pore size, well-defined conjugate chemistry, high electrical conductivity and compatibility with traditional fabrication techniques.

“We found that nanoporous gold reduces scar coverage but also maintains high neuronal coverage in an in vitro neuron-glia co-culture model,” said Juergen Biener, the other LLNL author of the paper. “More broadly, the study demonstrates a novel surface for supporting neuronal cultures without the use of culture medium supplements to reduce scar overgrowth.”

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

Nanoporous Gold as a Neural Interface Coating: Effects of Topography, Surface Chemistry, and Feature Size by Christopher A. R. Chapman, Hao Chen, Marianna Stamou, Juergen Biener, Monika M. Biener, Pamela J. Lein, and Erkin Seker. ACS Appl. Mater. Interfaces, 2015, 7 (13), pp 7093–7100 DOI: 10.1021/acsami.5b00410 Publication Date (Web): February 23, 2015

Copyright © 2015 American Chemical Society

This paper is behind a paywall.

The researchers have provided this image to illustrate their work,

The image depicts a neuronal network growing on a novel nanotextured gold electrode coating. The topographical cues presented by the coating preferentially favor spreading of neurons as opposed to scar tissue. This feature has the potential to enhance the performance of neural interfaces. Image by Ryan Chen/LLNL.

The image depicts a neuronal network growing on a novel nanotextured gold electrode coating. The topographical cues presented by the coating preferentially favor spreading of neurons as opposed to scar tissue. This feature has the potential to enhance the performance of neural interfaces. Image by Ryan Chen/LLNL.

Centralized depot (Wikipedia style) for data on neurons

The decades worth of data that has been collected about the billions of neurons in the brain is astounding. To help scientists make sense of this “brain big data,” researchers at Carnegie Mellon University have used data mining to create http://www.neuroelectro.org, a publicly available website that acts like Wikipedia, indexing physiological information about neurons.

opens a March 30, 2015 news item on ScienceDaily (Note: A link has been removed),

The site will help to accelerate the advance of neuroscience research by providing a centralized resource for collecting and comparing data on neuronal function. A description of the data available and some of the analyses that can be performed using the site are published online by the Journal of Neurophysiology

A March 30, 2015 Carnegie Mellon University news release on EurekAlert, which originated the news item, describes, in more detail,  the endeavour and what the scientists hope to achieve,

The neurons in the brain can be divided into approximately 300 different types based on their physical and functional properties. Researchers have been studying the function and properties of many different types of neurons for decades. The resulting data is scattered across tens of thousands of papers in the scientific literature. Researchers at Carnegie Mellon turned to data mining to collect and organize these data in a way that will make possible, for the first time, new methods of analysis.

“If we want to think about building a brain or re-engineering the brain, we need to know what parts we’re working with,” said Nathan Urban, interim provost and director of Carnegie Mellon’s BrainHubSM neuroscience initiative. “We know a lot about neurons in some areas of the brain, but very little about neurons in others. To accelerate our understanding of neurons and their functions, we need to be able to easily determine whether what we already know about some neurons can be applied to others we know less about.”

Shreejoy J. Tripathy, who worked in Urban’s lab when he was a graduate student in the joint Carnegie Mellon/University of Pittsburgh Center for the Neural Basis of Cognition (CNBC) Program in Neural Computation, selected more than 10,000 published papers that contained physiological data describing how neurons responded to various inputs. He used text mining algorithms to “read” each of the papers. The text mining software found the portions of each paper that identified the type of neuron studied and then isolated the electrophysiological data related to the properties of that neuronal type. It also retrieved information about how each of the experiments in the literature was completed, and corrected the data to account for any differences that might be caused by the format of the experiment. Overall, Tripathy, who is now a postdoc at the University of British Columbia, was able to collect and standardize data for approximately 100 different types of neurons, which he published on the website http://www.neuroelectro.org.

Since the data on the website was collected using text mining, the researchers realized that it was likely to contain errors related to extraction and standardization. Urban and his group validated much of the data, but they also created a mechanism that allows site users to flag data for further evaluation. Users also can contribute new data with minimal intervention from site administrators, similar to Wikipedia.

“It’s a dynamic environment in which people can collect, refine and add data,” said Urban, who is the Dr. Frederick A. Schwertz Distinguished Professor of Life Sciences and a member of the CNBC. “It will be a useful resource to people doing neuroscience research all over the world.”

Ultimately, the website will help researchers find groups of neurons that share the same physiological properties, which could provide a better understanding of how a neuron functions. For example, if a researcher finds that a type of neuron in the brain’s neocortex fires spontaneously, they can look up other neurons that fire spontaneously and access research papers that address this type of neuron. Using that information, they can quickly form hypotheses about whether or not the same mechanisms are at play in both the newly discovered and previously studied neurons.

To demonstrate how neuroelectro.org could be used, the researchers compared the electrophysiological data from more than 30 neuron types that had been most heavily studied in the literature. These included pyramidal neurons in the hippocampus, which are responsible for memory, and dopamine neurons in the midbrain, thought to be responsible for reward-seeking behaviors and addiction, among others. The site was able to find many expected similarities between the different types of neurons, and some similarities that were a surprise to researchers. Those surprises represent promising areas for future research.

In ongoing work, the Carnegie Mellon researchers are comparing the data on neuroelectro.org with other kinds of data, including data on neurons’ patterns of gene expression. For example, Urban’s group is using another publicly available resource, the Allen Brain Atlas, to find whether groups of neurons with similar electrical function have similar gene expression.

“It would take a lot of time, effort and money to determine both the physiological properties of a neuron and its gene expression,” Urban said. “Our website will help guide this research, making it much more efficient.”

The researchers have produced a brief video describing neurons and their project,

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

Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types by Shreejoy J Tripathy, Shawn D. Burton, Matthew Geramita, Richard C. Gerkin, and Nathaniel N. Urban. Journal of Neurophysiology Published 25 March 2015 DOI: 10.1152/jn.00237.2015

This paper is behind a paywall.

Brain-on-a-chip 2014 survey/overview

Michael Berger has written another of his Nanowerk Spotlight articles focussing on neuromorphic engineering and the concept of a brain-on-a-chip bringing it up-to-date April 2014 style.

It’s a topic he and I have been following (separately) for years. Berger’s April 4, 2014 Brain-on-a-chip Spotlight article provides a very welcome overview of the international neuromorphic engineering effort (Note: Links have been removed),

Constructing realistic simulations of the human brain is a key goal of the Human Brain Project, a massive European-led research project that commenced in 2013.

The Human Brain Project is a large-scale, scientific collaborative project, which aims to gather all existing knowledge about the human brain, build multi-scale models of the brain that integrate this knowledge and use these models to simulate the brain on supercomputers. The resulting “virtual brain” offers the prospect of a fundamentally new and improved understanding of the human brain, opening the way for better treatments for brain diseases and for novel, brain-like computing technologies.

Several years ago, another European project named FACETS (Fast Analog Computing with Emergent Transient States) completed an exhaustive study of neurons to find out exactly how they work, how they connect to each other and how the network can ‘learn’ to do new things. One of the outcomes of the project was PyNN, a simulator-independent language for building neuronal network models.

Scientists have great expectations that nanotechnologies will bring them closer to the goal of creating computer systems that can simulate and emulate the brain’s abilities for sensation, perception, action, interaction and cognition while rivaling its low power consumption and compact size – basically a brain-on-a-chip. Already, scientists are working hard on laying the foundations for what is called neuromorphic engineering – a new interdisciplinary discipline that includes nanotechnologies and whose goal is to design artificial neural systems with physical architectures similar to biological nervous systems.

Several research projects funded with millions of dollars are at work with the goal of developing brain-inspired computer architectures or virtual brains: DARPA’s SyNAPSE, the EU’s BrainScaleS (a successor to FACETS), or the Blue Brain project (one of the predecessors of the Human Brain Project) at Switzerland’s EPFL [École Polytechnique Fédérale de Lausanne].

Berger goes on to describe the raison d’être for neuromorphic engineering (attempts to mimic biological brains),

Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment. In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations. Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications – but useful and practical implementations do not yet exist.

Researchers are mostly interested in emulating neural plasticity (aka synaptic plasticity), from Berger’s April 4, 2014 article,

Independent from military-inspired research like DARPA’s, nanotechnology researchers in France have developed a hybrid nanoparticle-organic transistor that can mimic the main functionalities of a synapse. This organic transistor, based on pentacene and gold nanoparticles and termed NOMFET (Nanoparticle Organic Memory Field-Effect Transistor), has opened the way to new generations of neuro-inspired computers, capable of responding in a manner similar to the nervous system  (read more: “Scientists use nanotechnology to try building computers modeled after the brain”).

One of the key components of any neuromorphic effort, and its starting point, is the design of artificial synapses. Synapses dominate the architecture of the brain and are responsible for massive parallelism, structural plasticity, and robustness of the brain. They are also crucial to biological computations that underlie perception and learning. Therefore, a compact nanoelectronic device emulating the functions and plasticity of biological synapses will be the most important building block of brain-inspired computational systems.

In 2011, a team at Stanford University demonstrates a new single element nanoscale device, based on the successfully commercialized phase change material technology, emulating the functionality and the plasticity of biological synapses. In their work, the Stanford team demonstrated a single element electronic synapse with the capability of both the modulation of the time constant and the realization of the different synaptic plasticity forms while consuming picojoule level energy for its operation (read more: “Brain-inspired computing with nanoelectronic programmable synapses”).

Berger does mention memristors but not in any great detail in this article,

Researchers have also suggested that memristor devices are capable of emulating the biological synapses with properly designed CMOS neuron components. A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. It has the special property that its resistance can be programmed (resistor) and subsequently remains stored (memory).

One research project already demonstrated that a memristor can connect conventional circuits and support a process that is the basis for memory and learning in biological systems (read more: “Nanotechnology’s road to artificial brains”).

You can find a number of memristor articles here including these: Memristors have always been with us from June 14, 2013; How to use a memristor to create an artificial brain from Feb. 26, 2013; Electrochemistry of memristors in a critique of the 2008 discovery from Sept. 6, 2012; and many more (type ‘memristor’ into the blog search box and you should receive many postings or alternatively, you can try ‘artificial brains’ if you want everything I have on artificial brains).

Getting back to Berger’s April 4, 2014 article, he mentions one more approach and this one stands out,

A completely different – and revolutionary – human brain model has been designed by researchers in Japan who introduced the concept of a new class of computer which does not use any circuit or logic gate. This artificial brain-building project differs from all others in the world. It does not use logic-gate based computing within the framework of Turing. The decision-making protocol is not a logical reduction of decision rather projection of frequency fractal operations in a real space, it is an engineering perspective of Gödel’s incompleteness theorem.

Berger wrote about this work in much more detail in a Feb. 10, 2014 Nanowerk Spotlight article titled: Brain jelly – design and construction of an organic, brain-like computer, (Note: Links have been removed),

In a previous Nanowerk Spotlight we reported on the concept of a full-fledged massively parallel organic computer at the nanoscale that uses extremely low power (“Will brain-like evolutionary circuit lead to intelligent computers?”). In this work, the researchers created a process of circuit evolution similar to the human brain in an organic molecular layer. This was the first time that such a brain-like ‘evolutionary’ circuit had been realized.

The research team, led by Dr. Anirban Bandyopadhyay, a senior researcher at the Advanced Nano Characterization Center at the National Institute of Materials Science (NIMS) in Tsukuba, Japan, has now finalized their human brain model and introduced the concept of a new class of computer which does not use any circuit or logic gate.

In a new open-access paper published online on January 27, 2014, in Information (“Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System”), Bandyopadhyay and his team now describe the fundamental computing principle of a frequency fractal brain like computer.

“Our artificial brain-building project differs from all others in the world for several reasons,” Bandyopadhyay explains to Nanowerk. He lists the four major distinctions:
1) We do not use logic gate based computing within the framework of Turing, our decision-making protocol is not a logical reduction of decision rather projection of frequency fractal operations in a real space, it is an engineering perspective of Gödel’s incompleteness theorem.
2) We do not need to write any software, the argument and basic phase transition for decision-making, ‘if-then’ arguments and the transformation of one set of arguments into another self-assemble and expand spontaneously, the system holds an astronomically large number of ‘if’ arguments and its associative ‘then’ situations.
3) We use ‘spontaneous reply back’, via wireless communication using a unique resonance band coupling mode, not conventional antenna-receiver model, since fractal based non-radiative power management is used, the power expense is negligible.
4) We have carried out our own single DNA, single protein molecule and single brain microtubule neurophysiological study to develop our own Human brain model.

I encourage people to read Berger’s articles on this topic as they provide excellent information and links to much more. Curiously (mind you, it is easy to miss something), he does not mention James Gimzewski’s work at the University of California at Los Angeles (UCLA). Working with colleagues from the National Institute for Materials Science in Japan, Gimzewski published a paper about “two-, three-terminal WO3-x-based nanoionic devices capable of a broad range of neuromorphic and electrical functions”. You can find out more about the paper in my Dec. 24, 2012 posting titled: Synaptic electronics.

As for the ‘brain jelly’ paper, here’s a link to and a citation for it,

Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System by Subrata Ghoshemail, Krishna Aswaniemail, Surabhi Singhemail, Satyajit Sahuemail, Daisuke Fujitaemail and Anirban Bandyopadhyay. Information 2014, 5(1), 28-100; doi:10.3390/info5010028

It’s an open access paper.

As for anyone who’s curious about why the US BRAIN initiative ((Brain Research through Advancing Innovative Neurotechnologies, also referred to as the Brain Activity Map Project) is not mentioned, I believe that’s because it’s focussed on biological brains exclusively at this point (you can check its Wikipedia entry to confirm).

Anirban Bandyopadhyay was last mentioned here in a January 16, 2014 posting titled: Controversial theory of consciousness confirmed (maybe) in  the context of a presentation in Amsterdam, Netherlands.

Listening to an individual brain cell using a carbon nanotube ‘harpoon’

Apparently, the prime motivation for listening to individual neurons or brain cells is to “better understand the computational complexity of the brain,” according to a June 20,  2013 news item on Azonano,

The new brain cell spear is a millimeter long, only a few nanometers wide and harnesses the superior electromechanical properties of carbon nanotubes to capture electrical signals from individual neurons.

“To our knowledge, this is the first time scientists have used carbon nanotubes to record signals from individual neurons, what we call intracellular recordings, in brain slices or intact brains of vertebrates,” said Bruce Donald, a professor of computer science and biochemistry at Duke University who helped developed the probe.

The June 19, 2013 Duke University news release by Ashley Yeager, which originated the news item, provides some insight into the current state of the art and how this new technique is an improvement,

Currently, they use two main types of electrodes, metal and glass, to record signals from brain cells. Metal electrodes record spikes from a population of brain cells and work well in live animals. Glass electrodes also measure spikes, as well as the computations individual cells perform, but are delicate and break easily.”The new carbon nanotubes combine the best features of both metal and glass electrodes. They record well both inside and outside brain cells, and they are quite flexible. Because they won’t shatter, scientists could use them to record signals from individual brain cells of live animals,” said Duke neurobiologist Michael Platt, who was not involved in the study.

This is not the first time researchers have tried to use carbon nanotubes for this purpose, from the news release,

In the past, other scientists have experimented with carbon nanotube probes. But the electrodes were thick, causing tissue damage, or they were short, limiting how far they could penetrate into brain tissue. They could not probe inside individual neurons.

To change this, Donald began working on a harpoon-like carbon-nanotube probe with Duke neurobiologist Richard Mooney five years ago. The two met during their first year at Yale in the 1976, kept in touch throughout graduate school and began meeting to talk about their research after they both came to Duke.

Mooney told Donald about his work recording brain signals from live zebra finches and mice. The work was challenging, he said, because the probes and machinery to do the studies were large and bulky on the small head of a mouse or bird.

With Donald’s expertise in nanotechnology and robotics and Mooney’s in neurobiology, the two thought they could work together to shrink the machinery and improve the probes with nano-materials.

To make the probe, graduate student Inho Yoon and Duke physicist Gleb Finkelstein used the tip of an electrochemically sharpened tungsten wire as the base and extended it with self-entangled multi-wall carbon nanotubes to create a millimeter-long rod. The scientists then sharpened the nanotubes into a tiny harpoon using a focused ion beam at North Carolina State University.

Yoon then took the nano-harpoon to Mooney’s lab and jabbed it into slices of mouse brain tissue and then into the brains of anesthetized mice. The results show that the probe transmits brain signals as well as, and sometimes better than, conventional glass electrodes and is less likely to break off in the tissue. The new probe also penetrates individual neurons, recording the signals of a single cell rather than the nearest population of them.

Based on the results, the team has applied for a patent on the nano-harpoon.  Platt said scientists might use the probes in a range of applications, from basic science to human brain-computer interfaces and brain prostheses.

Donald said the new probe makes advances in those directions, but the insulation layers, electrical recording abilities and geometry of the device still need improvement.

The research paper is available in the open access journal PLoS ONE,

Intracellular Neural Recording with Pure Carbon Nanotube Probes by Inho Yoon, Kosuke Hamaguchi, Ivan V. Borzenets, Gleb Finkelstein, Richard Mooney, and Bruce R. Donald. 2013. PLOS ONE. DOI: 10.1371/journal.pone.0065715

As for calling this a ‘harpoon’, these carbon nanotube probes really do resemble harpoons,

This image, taken with a scanning electron microscope, shows a new brain electrode that tapers to a point as thick as a single carbon nanotube. Credit: Inho Yoon and Bruce Donald, Duke.  [downloaded from http://today.duke.edu/2013/06/brainharpoon]

This image, taken with a scanning electron microscope, shows a new brain electrode that tapers to a point as thick as a single carbon nanotube. Credit: Inho Yoon and Bruce Donald, Duke. [downloaded from http://today.duke.edu/2013/06/brainharpoon]

You can compare it to this harpoon from The Specialists Prop House, Traditional harpoon page,

[downloaded from The Specialists Prop House, Traditional harpoon page, http://thespecialistsltd.com/traditional-harpoon]

[downloaded from The Specialists Prop House, Traditional harpoon page, http://thespecialistsltd.com/traditional-harpoon]

I have written about some of the neuroscience work coming out of Duke University in the past, e.g., my March 4, 2013 posting about Miguel Nicolelis’ work on brain-to-brain communication.

Memristors and dogs

They’ve managed to recreate Pavlov’s classic experiment with dogs and feeding bells using an electronic circuit and teaching it to respond to a stimulus just as the dogs learned to respond. From the May 8, 2012 news item on Science Daily,

The bell rings and the dog starts drooling. Such a reaction was part of studies performed by Ivan Pavlov, a famous Russian psychologist and physiologist and winner of the Nobel Prize for Physiology and Medicine in 1904. His experiment, nowadays known as “Pavlov’s Dog,” is ever since considered as a milestone for implicit learning processes. By using specific electronic components scientists form the Technical Faculty and the Memory Research at the Kiel University together with the Forschungszentrum Jülich were now able to mimic the behavior of Pavlov`s dog.

I found this image on the May 8, 2012 news release webpage at the University of Kiel (Germany) website,

The experiment called “Pavlov’s Dog” shows that acoustic stimulations can cause physical reactions. Scientists of Kiel University redesigned this mental learning process. Source: Kohlstedt

Also from the May 8, 2012 news release on the University of Kiel website,

“We used memristive devices in order to mimic the associative behaviour of Pavlov’s dog in form of an electronic circuit”, explains Professor Hermann Kohlstedt, head of the working group Nanoelectronics at the University of Kiel.

Memristors are a class of electronic circuit elements which have only been available to scientists in an adequate quality for a few years. They exhibit a memory characteristic in form of hysteretic current-voltage curves consisting of high and low resistance branches. In dependence on the prior charge flow through the device these resistances can vary. Scientists try to use this memory effect in order to create networks that are similar to neuronal connections between synapses. “In the long term, our goal is to copy the synaptic plasticity onto electronic circuits. We might even be able to recreate cognitive skills electronically”, says Kohlstedt. The collaborating scientific working groups in Kiel and Jülich have taken a small step toward this goal.

The project set-up consisted of the following: two electrical impulses were linked via a memristive device to a comparator. The two pulses represent the food and the bell in Pavlov’s experiment. A comparator is a device that compares two voltages or currents and generates an output when a given level has been reached. In this case, it produces the output signal (representing saliva) when the threshold value is reached. In addition, the memristive element also has a threshold voltage that is defined by physical and chemical mechanisms in the nano-electronic device. Below this threshold value the memristive device behaves like any ordinary linear resistor. However, when the threshold value is exceeded, a hysteretic (changed) current-voltage characteristic will appear.

“During the experimental investigation, the food for the dog (electrical impulse 1) resulted in an output signal of the comparator, which could be defined as salivation. Unlike to impulse 1, the ring of the bell (electrical impulse 2) was set in such a way that the compartor’s output stayed unaffected – meaning no salivation”, describes Dr. Martin Ziegler, scientist at the Kiel University and the first-author of the publication. After applying both impulses simultaneously to the memristive device, the threshold value was exceeded. The working group had activated the memristive memory function. Multiple repetitions led to an associative learning process within the circuit – similar to Pavlov’s dogs. “From this moment on, we had only to apply electrical impulse 2 (bell) and the comparator generated an output signal, equivalent to salivation”, says Ziegler and is very pleased with these results. Electrical impulse 1 (feed) triggers the same reaction as it did before the learning. Hence, the electric circuit shows a behaviour that is termed classical conditioning in the field of psychology. Beyond that, the scientists were able to prove that the electrical circuit is able to unlearn a particular behaviour if both impulses were not longer applied simultaneously.

My most recent posting (and I have many) on memristors is from April 19, 2012 where I mentioned an artificial synapse developed with them at the University of Michigan and also noted that HP Labs has claimed it will be releasing ‘memristor-based’ products in2013.

The May 8, 2012 news item on Science Daily includes the full citation for the team’s paper and a link to it (the paper is behind a paywall).