Tag Archives: neurons

New path to viable memristor/neuristor?

I first stumbled onto memristors and the possibility of brain-like computing sometime in 2008 (around the time that R. Stanley Williams and his team at HP Labs first published the results of their research linking Dr. Leon Chua’s memristor theory to their attempts to shrink computer chips). In the almost 10 years since, scientists have worked hard to utilize memristors in the field of neuromorphic (brain-like) engineering/computing.

A January 22, 2018 news item on phys.org describes the latest work,

When it comes to processing power, the human brain just can’t be beat.

Packed within the squishy, football-sized organ are somewhere around 100 billion neurons. At any given moment, a single neuron can relay instructions to thousands of other neurons via synapses—the spaces between neurons, across which neurotransmitters are exchanged. There are more than 100 trillion synapses that mediate neuron signaling in the brain, strengthening some connections while pruning others, in a process that enables the brain to recognize patterns, remember facts, and carry out other learning tasks, at lightning speeds.

Researchers in the emerging field of “neuromorphic computing” have attempted to design computer chips that work like the human brain. Instead of carrying out computations based on binary, on/off signaling, like digital chips do today, the elements of a “brain on a chip” would work in an analog fashion, exchanging a gradient of signals, or “weights,” much like neurons that activate in various ways depending on the type and number of ions that flow across a synapse.

In this way, small neuromorphic chips could, like the brain, efficiently process millions of streams of parallel computations that are currently only possible with large banks of supercomputers. But one significant hangup on the way to such portable artificial intelligence has been the neural synapse, which has been particularly tricky to reproduce in hardware.

Now engineers at MIT [Massachusetts Institute of Technology] have designed an artificial synapse in such a way that they can precisely control the strength of an electric current flowing across it, similar to the way ions flow between neurons. The team has built a small chip with artificial synapses, made from silicon germanium. In simulations, the researchers found that the chip and its synapses could be used to recognize samples of handwriting, with 95 percent accuracy.

A January 22, 2018 MIT news release by Jennifer Chua (also on EurekAlert), which originated the news item, provides more detail about the research,

The design, published today [January 22, 2018] in the journal Nature Materials, is a major step toward building portable, low-power neuromorphic chips for use in pattern recognition and other learning tasks.

The research was led by Jeehwan Kim, the Class of 1947 Career Development Assistant Professor in the departments of Mechanical Engineering and Materials Science and Engineering, and a principal investigator in MIT’s Research Laboratory of Electronics and Microsystems Technology Laboratories. His co-authors are Shinhyun Choi (first author), Scott Tan (co-first author), Zefan Li, Yunjo Kim, Chanyeol Choi, and Hanwool Yeon of MIT, along with Pai-Yu Chen and Shimeng Yu of Arizona State University.

Too many paths

Most neuromorphic chip designs attempt to emulate the synaptic connection between neurons using two conductive layers separated by a “switching medium,” or synapse-like space. When a voltage is applied, ions should move in the switching medium to create conductive filaments, similarly to how the “weight” of a synapse changes.

But it’s been difficult to control the flow of ions in existing designs. Kim says that’s because most switching mediums, made of amorphous materials, have unlimited possible paths through which ions can travel — a bit like Pachinko, a mechanical arcade game that funnels small steel balls down through a series of pins and levers, which act to either divert or direct the balls out of the machine.

Like Pachinko, existing switching mediums contain multiple paths that make it difficult to predict where ions will make it through. Kim says that can create unwanted nonuniformity in a synapse’s performance.

“Once you apply some voltage to represent some data with your artificial neuron, you have to erase and be able to write it again in the exact same way,” Kim says. “But in an amorphous solid, when you write again, the ions go in different directions because there are lots of defects. This stream is changing, and it’s hard to control. That’s the biggest problem — nonuniformity of the artificial synapse.”

A perfect mismatch

Instead of using amorphous materials as an artificial synapse, Kim and his colleagues looked to single-crystalline silicon, a defect-free conducting material made from atoms arranged in a continuously ordered alignment. The team sought to create a precise, one-dimensional line defect, or dislocation, through the silicon, through which ions could predictably flow.

To do so, the researchers started with a wafer of silicon, resembling, at microscopic resolution, a chicken-wire pattern. They then grew a similar pattern of silicon germanium — a material also used commonly in transistors — on top of the silicon wafer. Silicon germanium’s lattice is slightly larger than that of silicon, and Kim found that together, the two perfectly mismatched materials can form a funnel-like dislocation, creating a single path through which ions can flow.

The researchers fabricated a neuromorphic chip consisting of artificial synapses made from silicon germanium, each synapse measuring about 25 nanometers across. They applied voltage to each synapse and found that all synapses exhibited more or less the same current, or flow of ions, with about a 4 percent variation between synapses — a much more uniform performance compared with synapses made from amorphous material.

They also tested a single synapse over multiple trials, applying the same voltage over 700 cycles, and found the synapse exhibited the same current, with just 1 percent variation from cycle to cycle.

“This is the most uniform device we could achieve, which is the key to demonstrating artificial neural networks,” Kim says.

Writing, recognized

As a final test, Kim’s team explored how its device would perform if it were to carry out actual learning tasks — specifically, recognizing samples of handwriting, which researchers consider to be a first practical test for neuromorphic chips. Such chips would consist of “input/hidden/output neurons,” each connected to other “neurons” via filament-based artificial synapses.

Scientists believe such stacks of neural nets can be made to “learn.” For instance, when fed an input that is a handwritten ‘1,’ with an output that labels it as ‘1,’ certain output neurons will be activated by input neurons and weights from an artificial synapse. When more examples of handwritten ‘1s’ are fed into the same chip, the same output neurons may be activated when they sense similar features between different samples of the same letter, thus “learning” in a fashion similar to what the brain does.

Kim and his colleagues ran a computer simulation of an artificial neural network consisting of three sheets of neural layers connected via two layers of artificial synapses, the properties of which they based on measurements from their actual neuromorphic chip. They fed into their simulation tens of thousands of samples from a handwritten recognition dataset commonly used by neuromorphic designers, and found that their neural network hardware recognized handwritten samples 95 percent of the time, compared to the 97 percent accuracy of existing software algorithms.

The team is in the process of fabricating a working neuromorphic chip that can carry out handwriting-recognition tasks, not in simulation but in reality. Looking beyond handwriting, Kim says the team’s artificial synapse design will enable much smaller, portable neural network devices that can perform complex computations that currently are only possible with large supercomputers.

“Ultimately we want a chip as big as a fingernail to replace one big supercomputer,” Kim says. “This opens a stepping stone to produce real artificial hardware.”

This research was supported in part by the National Science Foundation.

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

SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations by Shinhyun Choi, Scott H. Tan, Zefan Li, Yunjo Kim, Chanyeol Choi, Pai-Yu Chen, Hanwool Yeon, Shimeng Yu, & Jeehwan Kim. Nature Materials (2018) doi:10.1038/s41563-017-0001-5 Published online: 22 January 2018

This paper is behind a paywall.

For the curious I have included a number of links to recent ‘memristor’ postings here,

January 22, 2018: Memristors at Masdar

January 3, 2018: Mott memristor

August 24, 2017: Neuristors and brainlike computing

June 28, 2017: Dr. Wei Lu and bio-inspired ‘memristor’ chips

May 2, 2017: Predicting how a memristor functions

December 30, 2016: Changing synaptic connectivity with a memristor

December 5, 2016: The memristor as computing device

November 1, 2016: The memristor as the ‘missing link’ in bioelectronic medicine?

You can find more by using ‘memristor’ as the search term in the blog search function or on the search engine of your choice.

A bioengineered robot hand with its own nervous system: machine/flesh and a job opening

A November 14, 2017 news item on phys.org announces a grant for a research project which will see engineered robot hands combined with regenerative medicine to imbue neuroprosthetic hands with the sense of touch,

The sense of touch is often taken for granted. For someone without a limb or hand, losing that sense of touch can be devastating. While highly sophisticated prostheses with complex moving fingers and joints are available to mimic almost every hand motion, they remain frustratingly difficult and unnatural for the user. This is largely because they lack the tactile experience that guides every movement. This void in sensation results in limited use or abandonment of these very expensive artificial devices. So why not make a prosthesis that can actually “feel” its environment?

That is exactly what an interdisciplinary team of scientists from Florida Atlantic University and the University of Utah School of Medicine aims to do. They are developing a first-of-its-kind bioengineered robotic hand that will grow and adapt to its environment. This “living” robot will have its own peripheral nervous system directly linking robotic sensors and actuators. FAU’s College of Engineering and Computer Science is leading the multidisciplinary team that has received a four-year, $1.3 million grant from the National Institute of Biomedical Imaging and Bioengineering of the [US] National Institutes of Health for a project titled “Virtual Neuroprosthesis: Restoring Autonomy to People Suffering from Neurotrauma.”

A November14, 2017 Florida Atlantic University (FAU) news release by Gisele Galoustian, which originated the news item, goes into more detail,

With expertise in robotics, bioengineering, behavioral science, nerve regeneration, electrophysiology, microfluidic devices, and orthopedic surgery, the research team is creating a living pathway from the robot’s touch sensation to the user’s brain to help amputees control the robotic hand. A neuroprosthesis platform will enable them to explore how neurons and behavior can work together to regenerate the sensation of touch in an artificial limb.

At the core of this project is a cutting-edge robotic hand and arm developed in the BioRobotics Laboratory in FAU’s College of Engineering and Computer Science. Just like human fingertips, the robotic hand is equipped with numerous sensory receptors that respond to changes in the environment. Controlled by a human, it can sense pressure changes, interpret the information it is receiving and interact with various objects. It adjusts its grip based on an object’s weight or fragility. But the real challenge is figuring out how to send that information back to the brain using living residual neural pathways to replace those that have been damaged or destroyed by trauma.

“When the peripheral nerve is cut or damaged, it uses the rich electrical activity that tactile receptors create to restore itself. We want to examine how the fingertip sensors can help damaged or severed nerves regenerate,” said Erik Engeberg, Ph.D., principal investigator, an associate professor in FAU’s Department of Ocean and Mechanical Engineering, and director of FAU’s BioRobotics Laboratory. “To accomplish this, we are going to directly connect these living nerves in vitro and then electrically stimulate them on a daily basis with sensors from the robotic hand to see how the nerves grow and regenerate while the hand is operated by limb-absent people.”

For the study, the neurons will not be kept in conventional petri dishes. Instead, they will be placed in  biocompatible microfluidic chambers that provide a nurturing environment mimicking the basic function of living cells. Sarah E. Du, Ph.D., co-principal investigator, an assistant professor in FAU’s Department of Ocean and Mechanical Engineering, and an expert in the emerging field of microfluidics, has developed these tiny customized artificial chambers with embedded micro-electrodes. The research team will be able to stimulate the neurons with electrical impulses from the robot’s hand to help regrowth after injury. They will morphologically and electrically measure in real-time how much neural tissue has been restored.

Jianning Wei, Ph.D., co-principal investigator, an associate professor of biomedical science in FAU’s Charles E. Schmidt College of Medicine, and an expert in neural damage and regeneration, will prepare the neurons in vitro, observe them grow and see how they fare and regenerate in the aftermath of injury. This “virtual” method will give the research team multiple opportunities to test and retest the nerves without any harm to subjects.

Using an electroencephalogram (EEG) to detect electrical activity in the brain, Emmanuelle Tognoli, Ph.D., co-principal investigator, associate research professor in FAU’s Center for Complex Systems and Brain Sciences in the Charles E. Schmidt College of Science, and an expert in electrophysiology and neural, behavioral, and cognitive sciences, will examine how the tactile information from the robotic sensors is passed onto the brain to distinguish scenarios with successful or unsuccessful functional restoration of the sense of touch. Her objective: to understand how behavior helps nerve regeneration and how this nerve regeneration helps the behavior.

Once the nerve impulses from the robot’s tactile sensors have gone through the microfluidic chamber, they are sent back to the human user manipulating the robotic hand. This is done with a special device that converts the signals coming from the microfluidic chambers into a controllable pressure at a cuff placed on the remaining portion of the amputated person’s arm. Users will know if they are squeezing the object too hard or if they are losing their grip.

Engeberg also is working with Douglas T. Hutchinson, M.D., co-principal investigator and a professor in the Department of Orthopedics at the University of Utah School of Medicine, who specializes in hand and orthopedic surgery. They are developing a set of tasks and behavioral neural indicators of performance that will ultimately reveal how to promote a healthy sensation of touch in amputees and limb-absent people using robotic devices. The research team also is seeking a post-doctoral researcher with multi-disciplinary experience to work on this breakthrough project.

Here’s more about the job opportunity from the FAU BioRobotics Laboratory job posting, (I checked on January 30, 2018 and it seems applications are still being accepted.)

Post-doctoral Opportunity

Dated Posted: Oct. 13, 2017

The BioRobotics Lab at Florida Atlantic University (FAU) invites applications for a NIH NIBIB-funded Postdoctoral position to develop a Virtual Neuroprosthesis aimed at providing a sense of touch in amputees and limb-absent people.

Candidates should have a Ph.D. in one of the following degrees: mechanical engineering, electrical engineering, biomedical engineering, bioengineering or related, with interest and/or experience in transdisciplinary work at the intersection of robotic hands, biology, and biomedical systems. Prior experience in the neural field will be considered an advantage, though not a necessity. Underrepresented minorities and women are warmly encouraged to apply.

The postdoctoral researcher will be co-advised across the department of Mechanical Engineering and the Center for Complex Systems & Brain Sciences through an interdisciplinary team whose expertise spans Robotics, Microfluidics, Behavioral and Clinical Neuroscience and Orthopedic Surgery.

The position will be for one year with a possibility of extension based on performance. Salary will be commensurate with experience and qualifications. Review of applications will begin immediately and continue until the position is filled.

The application should include:

  1. a cover letter with research interests and experiences,
  2. a CV, and
  3. names and contact information for three professional references.

Qualified candidates can contact Erik Engeberg, Ph.D., Associate Professor, in the FAU Department of Ocean and Mechanical Engineering at eengeberg@fau.edu. Please reference AcademicKeys.com in your cover letter when applying for or inquiring about this job announcement.

You can find the apply button on this page. Good luck!

Art in the details: A look at the role of art in science—a Sept. 19, 2017 Café Scientifique event in Vancouver, Canada

The Sept. 19, 2017 Café Scientifique event, “Art in the Details A look at the role of art in science,” in Vancouver seems to be part of a larger neuroscience and the arts program at the University of British Columbia. First, the details about the Sept. 13, 2017 event from the eventful Vancouver webpage,

Café Scientifique – Art in the Details: A look at the role of art in science

Art in the Details: A look at the role of art in science With so much beauty in the natural world, why does the misconception that art and science are vastly different persist? Join us for discussion and dessert as we hear from artists, researchers and academic professionals about the role art has played in scientific research – from the formative work of Santiago Ramon Y Cajal to modern imaging, and beyond – and how it might help shape scientific understanding in the future. September 19th, 2017  7:00 – 9:00 pm (doors open at 6:45pm)  TELUS World of Science [also known as Science World], 1455 Quebec St., Vancouver, BC V6A 3Z7 Free Admission [emphasis mine] Experts Dr Carol-Ann Courneya Associate Professor in the Department of Cellular and Physiological Science and Assistant Dean of Student Affairs, Faculty of Medicine, University of British Columbia   Dr Jason Snyder  Assistant Professor, Department of Psychology, University of British Columbia http://snyderlab.com/   Dr Steven Barnes Instructor and Assistant Head—Undergraduate Affairs, Department of Psychology, University of British Columbia http://stevenjbarnes.com/   Moderated By   Bruce Claggett Senior Managing Editor, NEWS 1130   This evening event is presented in collaboration with the Djavad Mowafaghian Centre for Brain Health. Please note: this is a private, adult-oriented event and TELUS World of Science will be closed during this discussion.

The Art in the Details event page on the Science World website provides a bit more information about the speakers (mostly in the form of links to their webpage),,

Experts

Dr Carol-Ann Courneya
Associate Professor in the Department of Cellular and Physiological Science and Assistant Dean of Student Affairs, Faculty of Medicine, University of British Columbia

Dr Jason Snyder 

Assistant Professor, Department of Psychology, University of British Columbi

Dr Steven Barnes

Instructor, Department of Psychology, University of British Columbia

Moderated By  

Bruce Claggett

Senior Managing Editor, NEWS 1130

Should you click though to obtain tickets from either the eventful Vancouver or Science World websites, you’ll find the event is sold out but perhaps the organizers will include a waitlist.

Even if you can’t get a ticket, there’s an exhibition of Santiago Ramon Y Cajal’s work (from the Djavad Mowafaghian Centre for Brain Health’s Beautiful brain’s webpage),

Drawings of Santiago Ramón y Cajal to be shown at UBC

Santiago Ramón y Cajal, injured Purkinje neurons, 1914, ink and pencil on paper. Courtesy of Instituto Cajal (CSIC).

Pictured: Santiago Ramón y Cajal, injured Purkinje neurons, 1914, ink and pencil on paper. Courtesy of Instituto Cajal (CSIC).

The Beautiful Brain is the first North American museum exhibition to present the extraordinary drawings of Santiago Ramón y Cajal (1852–1934), a Spanish pathologist, histologist and neuroscientist renowned for his discovery of neuron cells and their structure, for which he was awarded the Nobel Prize in Physiology and Medicine in 1906. Known as the father of modern neuroscience, Cajal was also an exceptional artist. He combined scientific and artistic skills to produce arresting drawings with extraordinary scientific and aesthetic qualities.

A century after their completion, Cajal’s drawings are still used in contemporary medical publications to illustrate important neuroscience principles, and continue to fascinate artists and visual art audiences. Eighty of Cajal’s drawings will be accompanied by a selection of contemporary neuroscience visualizations by international scientists. The Morris and Helen Belkin Art Gallery exhibition will also include early 20th century works that imaged consciousness, including drawings from Annie Besant’s Thought Forms (1901) and Charles Leadbeater’s The Chakras (1927), as well as abstract works by Lawren Harris that explored his interest in spirituality and mysticism.

After countless hours at the microscope, Cajal was able to perceive that the brain was made up of individual nerve cells or neurons rather than a tangled single web, which was only decisively proven by electron microscopy in the 1950s and is the basis of neuroscience today. His speculative drawings stemmed from an understanding of aesthetics in their compressed detail and lucid composition, as he laboured to clearly represent matter and processes that could not be seen.

In a special collaboration with the Morris and Helen Belkin Art Gallery and the VGH & UBC Hospital Foundation this project will encourage meaningful dialogue amongst artists, curators, scientists and scholars on concepts of neuroplasticity and perception. Public and Academic programs will address the emerging field of art and neuroscience and engage interdisciplinary research of scholars from the sciences and humanities alike.

“This is an incredible opportunity for the neuroscience and visual arts communities at the University and Vancouver,” says Dr. Brian MacVicar, who has been working diligently with Director Scott Watson at the Morris and Helen Belkin Art Gallery and with his colleagues at the University of Minnesota for the past few years to bring this exhibition to campus. “Without Cajal’s impressive body of work, our understanding of the anatomy of the brain would not be so well-formed; Cajal’s legacy has been of critical importance to neuroscience teaching and research over the past century.”

A book published by Abrams accompanies the exhibition, containing full colour reproductions of all 80 of the exhibition drawings, commentary on each of the works and essays on Cajal’s life and scientific contributions, artistic roots and achievements and contemporary neuroscience imaging techniques.

Cajal’s work will be on display at the Morris and Helen Belkin Art Gallery from September 5 to December 3, 2017.

Join the UBC arts and neuroscience communities for a free symposium and dance performance celebrating The Beautiful Brain at UBC on September 7. [link removed]

The Beautiful Brain: The Drawings of Santiago Ramón y Cajal was developed by the Frederick R. Weisman Art Museum, University of Minnesota with the Instituto Cajal. The exhibition at the Morris and Helen Belkin Art Gallery, University British Columbia is presented in partnership with the Djavad Mowafaghian Centre for Brain Health with support from the VGH & UBC Hospital Foundation. We gratefully acknowledge the generous support of the Canada Council for the Arts, the British Columbia Arts Council and Belkin Curator’s Forum members.

The Morris and Helen Belkin Art Gallery’s Beautiful Brain webpage has a listing of upcoming events associated with the exhibition as well as instructions on how to get there (if you click on About),

SEMINAR & READING GROUP: Plasticity at SFU Vancouver and 221A: Wednesdays, October 4, 18, November 1, 15 and 21 at 7 pm

CONVERSATION with Anthony Phillips and Timothy Taylor: Wednesday, October 11, 2017 at 7 pm

LECTURE with Catherine Malabou at the Liu Institute: Thursday, November 23 at 6 pm

CONCERT with UBC Contemporary Players: Friday, December 1 at 2 pm

Cajal was also an exceptional artist and studied as a teenager at the Academy of Arts in Huesca, Spain. He combined scientific and artistic skills to produce arresting drawings with extraordinary scientific and aesthetic qualities. A century after their completion, his drawings are still used in contemporary medical publications to illustrate important neuroscience principles, and continue to fascinate artists and visual art audiences. Eighty of Cajal’s drawings are accompanied by a selection of contemporary neuroscience visualizations by international scientists.

Organizationally, this seems a little higgledy piggledy with the Cafe Scientifique event found on some sites, the Belkin Gallery events found on one site, and no single listing of everything on any one site for the Beautiful Brain. Please let me know if you find something I’ve missed.

Carbon nanotubes to repair nerve fibres (cyborg brains?)

Can cyborg brains be far behind now that researchers are looking at ways to repair nerve fibers with carbon nanotubes (CNTs)? A June 26, 2017 news item on ScienceDaily describes the scheme using carbon nanotubes as a material for repairing nerve fibers,

Carbon nanotubes exhibit interesting characteristics rendering them particularly suited to the construction of special hybrid devices — consisting of biological issue and synthetic material — planned to re-establish connections between nerve cells, for instance at spinal level, lost on account of lesions or trauma. This is the result of a piece of research published on the scientific journal Nanomedicine: Nanotechnology, Biology, and Medicine conducted by a multi-disciplinary team comprising SISSA (International School for Advanced Studies), the University of Trieste, ELETTRA Sincrotrone and two Spanish institutions, Basque Foundation for Science and CIC BiomaGUNE. More specifically, researchers have investigated the possible effects on neurons of the interaction with carbon nanotubes. Scientists have proven that these nanomaterials may regulate the formation of synapses, specialized structures through which the nerve cells communicate, and modulate biological mechanisms, such as the growth of neurons, as part of a self-regulating process. This result, which shows the extent to which the integration between nerve cells and these synthetic structures is stable and efficient, highlights the great potentialities of carbon nanotubes as innovative materials capable of facilitating neuronal regeneration or in order to create a kind of artificial bridge between groups of neurons whose connection has been interrupted. In vivo testing has actually already begun.

The researchers have included a gorgeous image to illustrate their work,

Caption: Scientists have proven that these nanomaterials may regulate the formation of synapses, specialized structures through which the nerve cells communicate, and modulate biological mechanisms, such as the growth of neurons, as part of a self-regulating process. Credit: Pixabay

A June 26, 2017 SISSA press release (also on EurekAlert), which originated the news item, describes the work in more detail while explaining future research needs,

“Interface systems, or, more in general, neuronal prostheses, that enable an effective re-establishment of these connections are under active investigation” explain Laura Ballerini (SISSA) and Maurizio Prato (UniTS-CIC BiomaGUNE), coordinating the research project. “The perfect material to build these neural interfaces does not exist, yet the carbon nanotubes we are working on have already proved to have great potentialities. After all, nanomaterials currently represent our best hope for developing innovative strategies in the treatment of spinal cord injuries”. These nanomaterials are used both as scaffolds, a supportive framework for nerve cells, and as means of interfaces releasing those signals that empower nerve cells to communicate with each other.

Many aspects, however, still need to be addressed. Among them, the impact on neuronal physiology of the integration of these nanometric structures with the cell membrane. “Studying the interaction between these two elements is crucial, as it might also lead to some undesired effects, which we ought to exclude”. Laura Ballerini explains: “If, for example, the mere contact provoked a vertiginous rise in the number of synapses, these materials would be essentially unusable”. “This”, Maurizio Prato adds, “is precisely what we have investigated in this study where we used pure carbon nanotubes”.

The results of the research are extremely encouraging: “First of all we have proved that nanotubes do not interfere with the composition of lipids, of cholesterol in particular, which make up the cellular membrane in neurons. Membrane lipids play a very important role in the transmission of signals through the synapses. Nanotubes do not seem to influence this process, which is very important”.

There is more, however. The research has also highlighted the fact that the nerve cells growing on the substratum of nanotubes, thanks to this interaction, develop and reach maturity very quickly, eventually reaching a condition of biological homeostasis. “Nanotubes facilitate the full growth of neurons and the formation of new synapses. This growth, however, is not indiscriminate and unlimited since, as we proved, after a few weeks a physiological balance is attained. Having established the fact that this interaction is stable and efficient is an aspect of fundamental importance”. Maurizio Prato and Laura Ballerini conclude as follows: “We are proving that carbon nanotubes perform excellently in terms of duration, adaptability and mechanical compatibility with the tissue. Now we know that their interaction with the biological material, too, is efficient. Based on this evidence, we are already studying the in vivo application, and preliminary results appear to be quite promising also in terms of recovery of the lost neurological functions”.

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

Sculpting neurotransmission during synaptic development by 2D nanostructured interfaces by Niccolò Paolo Pampaloni, Denis Scaini, Fabio Perissinotto, Susanna Bosi, Maurizio Prato, Laura Ballerini. Nanomedicine: Nanotechnology, Biology and Medicine, DOI: http://dx.doi.org/10.1016/j.nano.2017.01.020 Published online: May 25, 2017

This paper is open access.

Brain stuff: quantum entanglement and a multi-dimensional universe

I have two brain news bits, one about neural networks and quantum entanglement and another about how the brain operates on more than three dimensions.

Quantum entanglement and neural networks

A June 13, 2017 news item on phys.org describes how machine learning can be used to solve problems in physics (Note: Links have been removed),

Machine learning, the field that’s driving a revolution in artificial intelligence, has cemented its role in modern technology. Its tools and techniques have led to rapid improvements in everything from self-driving cars and speech recognition to the digital mastery of an ancient board game.

Now, physicists are beginning to use machine learning tools to tackle a different kind of problem, one at the heart of quantum physics. In a paper published recently in Physical Review X, researchers from JQI [Joint Quantum Institute] and the Condensed Matter Theory Center (CMTC) at the University of Maryland showed that certain neural networks—abstract webs that pass information from node to node like neurons in the brain—can succinctly describe wide swathes of quantum systems.

An artist’s rendering of a neural network with two layers. At the top is a real quantum system, like atoms in an optical lattice. Below is a network of hidden neurons that capture their interactions (Credit: E. Edwards/JQI)

A June 12, 2017 JQI news release by Chris Cesare, which originated the news item, describes how neural networks can represent quantum entanglement,

Dongling Deng, a JQI Postdoctoral Fellow who is a member of CMTC and the paper’s first author, says that researchers who use computers to study quantum systems might benefit from the simple descriptions that neural networks provide. “If we want to numerically tackle some quantum problem,” Deng says, “we first need to find an efficient representation.”

On paper and, more importantly, on computers, physicists have many ways of representing quantum systems. Typically these representations comprise lists of numbers describing the likelihood that a system will be found in different quantum states. But it becomes difficult to extract properties or predictions from a digital description as the number of quantum particles grows, and the prevailing wisdom has been that entanglement—an exotic quantum connection between particles—plays a key role in thwarting simple representations.

The neural networks used by Deng and his collaborators—CMTC Director and JQI Fellow Sankar Das Sarma and Fudan University physicist and former JQI Postdoctoral Fellow Xiaopeng Li—can efficiently represent quantum systems that harbor lots of entanglement, a surprising improvement over prior methods.

What’s more, the new results go beyond mere representation. “This research is unique in that it does not just provide an efficient representation of highly entangled quantum states,” Das Sarma says. “It is a new way of solving intractable, interacting quantum many-body problems that uses machine learning tools to find exact solutions.”

Neural networks and their accompanying learning techniques powered AlphaGo, the computer program that beat some of the world’s best Go players last year (link is external) (and the top player this year (link is external)). The news excited Deng, an avid fan of the board game. Last year, around the same time as AlphaGo’s triumphs, a paper appeared that introduced the idea of using neural networks to represent quantum states (link is external), although it gave no indication of exactly how wide the tool’s reach might be. “We immediately recognized that this should be a very important paper,” Deng says, “so we put all our energy and time into studying the problem more.”

The result was a more complete account of the capabilities of certain neural networks to represent quantum states. In particular, the team studied neural networks that use two distinct groups of neurons. The first group, called the visible neurons, represents real quantum particles, like atoms in an optical lattice or ions in a chain. To account for interactions between particles, the researchers employed a second group of neurons—the hidden neurons—which link up with visible neurons. These links capture the physical interactions between real particles, and as long as the number of connections stays relatively small, the neural network description remains simple.

Specifying a number for each connection and mathematically forgetting the hidden neurons can produce a compact representation of many interesting quantum states, including states with topological characteristics and some with surprising amounts of entanglement.

Beyond its potential as a tool in numerical simulations, the new framework allowed Deng and collaborators to prove some mathematical facts about the families of quantum states represented by neural networks. For instance, neural networks with only short-range interactions—those in which each hidden neuron is only connected to a small cluster of visible neurons—have a strict limit on their total entanglement. This technical result, known as an area law, is a research pursuit of many condensed matter physicists.

These neural networks can’t capture everything, though. “They are a very restricted regime,” Deng says, adding that they don’t offer an efficient universal representation. If they did, they could be used to simulate a quantum computer with an ordinary computer, something physicists and computer scientists think is very unlikely. Still, the collection of states that they do represent efficiently, and the overlap of that collection with other representation methods, is an open problem that Deng says is ripe for further exploration.

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

Quantum Entanglement in Neural Network States by Dong-Ling Deng, Xiaopeng Li, and S. Das Sarma. Phys. Rev. X 7, 021021 – Published 11 May 2017

This paper is open access.

Blue Brain and the multidimensional universe

Blue Brain is a Swiss government brain research initiative which officially came to life in 2006 although the initial agreement between the École Politechnique Fédérale de Lausanne (EPFL) and IBM was signed in 2005 (according to the project’s Timeline page). Moving on, the project’s latest research reveals something astounding (from a June 12, 2017 Frontiers Publishing press release on EurekAlert),

For most people, it is a stretch of the imagination to understand the world in four dimensions but a new study has discovered structures in the brain with up to eleven dimensions – ground-breaking work that is beginning to reveal the brain’s deepest architectural secrets.

Using algebraic topology in a way that it has never been used before in neuroscience, a team from the Blue Brain Project has uncovered a universe of multi-dimensional geometrical structures and spaces within the networks of the brain.

The research, published today in Frontiers in Computational Neuroscience, shows that these structures arise when a group of neurons forms a clique: each neuron connects to every other neuron in the group in a very specific way that generates a precise geometric object. The more neurons there are in a clique, the higher the dimension of the geometric object.

“We found a world that we had never imagined,” says neuroscientist Henry Markram, director of Blue Brain Project and professor at the EPFL in Lausanne, Switzerland, “there are tens of millions of these objects even in a small speck of the brain, up through seven dimensions. In some networks, we even found structures with up to eleven dimensions.”

Markram suggests this may explain why it has been so hard to understand the brain. “The mathematics usually applied to study networks cannot detect the high-dimensional structures and spaces that we now see clearly.”

If 4D worlds stretch our imagination, worlds with 5, 6 or more dimensions are too complex for most of us to comprehend. This is where algebraic topology comes in: a branch of mathematics that can describe systems with any number of dimensions. The mathematicians who brought algebraic topology to the study of brain networks in the Blue Brain Project were Kathryn Hess from EPFL and Ran Levi from Aberdeen University.

“Algebraic topology is like a telescope and microscope at the same time. It can zoom into networks to find hidden structures – the trees in the forest – and see the empty spaces – the clearings – all at the same time,” explains Hess.

In 2015, Blue Brain published the first digital copy of a piece of the neocortex – the most evolved part of the brain and the seat of our sensations, actions, and consciousness. In this latest research, using algebraic topology, multiple tests were performed on the virtual brain tissue to show that the multi-dimensional brain structures discovered could never be produced by chance. Experiments were then performed on real brain tissue in the Blue Brain’s wet lab in Lausanne confirming that the earlier discoveries in the virtual tissue are biologically relevant and also suggesting that the brain constantly rewires during development to build a network with as many high-dimensional structures as possible.

When the researchers presented the virtual brain tissue with a stimulus, cliques of progressively higher dimensions assembled momentarily to enclose high-dimensional holes, that the researchers refer to as cavities. “The appearance of high-dimensional cavities when the brain is processing information means that the neurons in the network react to stimuli in an extremely organized manner,” says Levi. “It is as if the brain reacts to a stimulus by building then razing a tower of multi-dimensional blocks, starting with rods (1D), then planks (2D), then cubes (3D), and then more complex geometries with 4D, 5D, etc. The progression of activity through the brain resembles a multi-dimensional sandcastle that materializes out of the sand and then disintegrates.”

The big question these researchers are asking now is whether the intricacy of tasks we can perform depends on the complexity of the multi-dimensional “sandcastles” the brain can build. Neuroscience has also been struggling to find where the brain stores its memories. “They may be ‘hiding’ in high-dimensional cavities,” Markram speculates.

###

About Blue Brain

The aim of the Blue Brain Project, a Swiss brain initiative founded and directed by Professor Henry Markram, is to build accurate, biologically detailed digital reconstructions and simulations of the rodent brain, and ultimately, the human brain. The supercomputer-based reconstructions and simulations built by Blue Brain offer a radically new approach for understanding the multilevel structure and function of the brain. http://bluebrain.epfl.ch

About Frontiers

Frontiers is a leading community-driven open-access publisher. By taking publishing entirely online, we drive innovation with new technologies to make peer review more efficient and transparent. We provide impact metrics for articles and researchers, and merge open access publishing with a research network platform – Loop – to catalyse research dissemination, and popularize research to the public, including children. Our goal is to increase the reach and impact of research articles and their authors. Frontiers has received the ALPSP Gold Award for Innovation in Publishing in 2014. http://www.frontiersin.org.

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

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function by Michael W. Reimann, Max Nolte, Martina Scolamiero, Katharine Turner, Rodrigo Perin, Giuseppe Chindemi, Paweł Dłotko, Ran Levi, Kathryn Hess, and Henry Markram. Front. Comput. Neurosci., 12 June 2017 | https://doi.org/10.3389/fncom.2017.00048

This paper is open access.

Hacking the human brain with a junction-based artificial synaptic device

Earlier today I published a piece featuring Dr. Wei Lu’s work on memristors and the movement to create an artificial brain (my June 28, 2017 posting: Dr. Wei Lu and bio-inspired ‘memristor’ chips). For this posting I’m featuring a non-memristor (if I’ve properly understood the technology) type of artificial synapse. From a June 28, 2017 news item on Nanowerk,

One of the greatest challenges facing artificial intelligence development is understanding the human brain and figuring out how to mimic it.

Now, one group reports in ACS Nano (“Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device”) that they have developed an artificial synapse capable of simulating a fundamental function of our nervous system — the release of inhibitory and stimulatory signals from the same “pre-synaptic” terminal.

Unfortunately, the American Chemical Society news release on EurekAlert, which originated the news item, doesn’t provide too much more detail,

The human nervous system is made up of over 100 trillion synapses, structures that allow neurons to pass electrical and chemical signals to one another. In mammals, these synapses can initiate and inhibit biological messages. Many synapses just relay one type of signal, whereas others can convey both types simultaneously or can switch between the two. To develop artificial intelligence systems that better mimic human learning, cognition and image recognition, researchers are imitating synapses in the lab with electronic components. Most current artificial synapses, however, are only capable of delivering one type of signal. So, Han Wang, Jing Guo and colleagues sought to create an artificial synapse that can reconfigurably send stimulatory and inhibitory signals.

The researchers developed a synaptic device that can reconfigure itself based on voltages applied at the input terminal of the device. A junction made of black phosphorus and tin selenide enables switching between the excitatory and inhibitory signals. This new device is flexible and versatile, which is highly desirable in artificial neural networks. In addition, the artificial synapses may simplify the design and functions of nervous system simulations.

Here’s how I concluded that this is not a memristor-type device (from the paper [first paragraph, final sentence]; a link and citation will follow; Note: Links have been removed)),

The conventional memristor-type [emphasis mine](14-20) and transistor-type(21-25) artificial synapses can realize synaptic functions in a single semiconductor device but lacks the ability [emphasis mine] to dynamically reconfigure between excitatory and inhibitory responses without the addition of a modulating terminal.

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

Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device by
He Tian, Xi Cao, Yujun Xie, Xiaodong Yan, Andrew Kostelec, Don DiMarzio, Cheng Chang, Li-Dong Zhao, Wei Wu, Jesse Tice, Judy J. Cha, Jing Guo, and Han Wang. ACS Nano, Article ASAP DOI: 10.1021/acsnano.7b03033 Publication Date (Web): June 28, 2017

Copyright © 2017 American Chemical Society

This paper is behind a paywall.

Dr. Wei Lu and bio-inspired ‘memristor’ chips

It’s been a while since I’ve featured Dr. Wei Lu’s work here. This April  15, 2010 posting features Lu’s most relevant previous work.) Here’s his latest ‘memristor’ work , from a May 22, 2017 news item on Nanowerk (Note: A link has been removed),

Inspired by how mammals see, a new “memristor” computer circuit prototype at the University of Michigan has the potential to process complex data, such as images and video orders of magnitude, faster and with much less power than today’s most advanced systems.

Faster image processing could have big implications for autonomous systems such as self-driving cars, says Wei Lu, U-M professor of electrical engineering and computer science. Lu is lead author of a paper on the work published in the current issue of Nature Nanotechnology (“Sparse coding with memristor networks”).

Lu’s next-generation computer components use pattern recognition to shortcut the energy-intensive process conventional systems use to dissect images. In this new work, he and his colleagues demonstrate an algorithm that relies on a technique called “sparse coding” to coax their 32-by-32 array of memristors to efficiently analyze and recreate several photos.

A May 22, 2017 University of Michigan news release (also on EurekAlert), which originated the news item, provides more information about memristors and about the research,

Memristors are electrical resistors with memory—advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. In a conventional computer, logic and memory functions are located at different parts of the circuit.

“The tasks we ask of today’s computers have grown in complexity,” Lu said. “In this ‘big data’ era, computers require costly, constant and slow communications between their processor and memory to retrieve large amounts data. This makes them large, expensive and power-hungry.”

But like neural networks in a biological brain, networks of memristors can perform many operations at the same time, without having to move data around. As a result, they could enable new platforms that process a vast number of signals in parallel and are capable of advanced machine learning. Memristors are good candidates for deep neural networks, a branch of machine learning, which trains computers to execute processes without being explicitly programmed to do so.

“We need our next-generation electronics to be able to quickly process complex data in a dynamic environment. You can’t just write a program to do that. Sometimes you don’t even have a pre-defined task,” Lu said. “To make our systems smarter, we need to find ways for them to process a lot of data more efficiently. Our approach to accomplish that is inspired by neuroscience.”

A mammal’s brain is able to generate sweeping, split-second impressions of what the eyes take in. One reason is because they can quickly recognize different arrangements of shapes. Humans do this using only a limited number of neurons that become active, Lu says. Both neuroscientists and computer scientists call the process “sparse coding.”

“When we take a look at a chair we will recognize it because its characteristics correspond to our stored mental picture of a chair,” Lu said. “Although not all chairs are the same and some may differ from a mental prototype that serves as a standard, each chair retains some of the key characteristics necessary for easy recognition. Basically, the object is correctly recognized the moment it is properly classified—when ‘stored’ in the appropriate category in our heads.”

Image of a memristor chip Image of a memristor chip Similarly, Lu’s electronic system is designed to detect the patterns very efficiently—and to use as few features as possible to describe the original input.

In our brains, different neurons recognize different patterns, Lu says.

“When we see an image, the neurons that recognize it will become more active,” he said. “The neurons will also compete with each other to naturally create an efficient representation. We’re implementing this approach in our electronic system.”

The researchers trained their system to learn a “dictionary” of images. Trained on a set of grayscale image patterns, their memristor network was able to reconstruct images of famous paintings and photos and other test patterns.

If their system can be scaled up, they expect to be able to process and analyze video in real time in a compact system that can be directly integrated with sensors or cameras.

The project is titled “Sparse Adaptive Local Learning for Sensing and Analytics.” Other collaborators are Zhengya Zhang and Michael Flynn of the U-M Department of Electrical Engineering and Computer Science, Garrett Kenyon of the Los Alamos National Lab and Christof Teuscher of Portland State University.

The work is part of a $6.9 million Unconventional Processing of Signals for Intelligent Data Exploitation project that aims to build a computer chip based on self-organizing, adaptive neural networks. It is funded by the [US] Defense Advanced Research Projects Agency [DARPA].

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

Sparse coding with memristor networks by Patrick M. Sheridan, Fuxi Cai, Chao Du, Wen Ma, Zhengya Zhang, & Wei D. Lu. Nature Nanotechnology (2017) doi:10.1038/nnano.2017.83 Published online 22 May 2017

This paper is behind a paywall.

For the interested, there are a number of postings featuring memristors here (just use ‘memristor’ as your search term in the blog search engine). You might also want to check out ‘neuromorphic engineeering’ and ‘neuromorphic computing’ and ‘artificial brain’.

Predicting how a memristor functions

An April 3, 2017 news item on Nanowerk announces a new memristor development (Note: A link has been removed),

Researchers from the CNRS [Centre national de la recherche scientifique; France] , Thales, and the Universities of Bordeaux, Paris-Sud, and Evry have created an artificial synapse capable of learning autonomously. They were also able to model the device, which is essential for developing more complex circuits. The research was published in Nature Communications (“Learning through ferroelectric domain dynamics in solid-state synapses”)

An April 3, 2017 CNRS press release, which originated the news item, provides a nice introduction to the memristor concept before providing a few more details about this latest work (Note: A link has been removed),

One of the goals of biomimetics is to take inspiration from the functioning of the brain [also known as neuromorphic engineering or neuromorphic computing] in order to design increasingly intelligent machines. This principle is already at work in information technology, in the form of the algorithms used for completing certain tasks, such as image recognition; this, for instance, is what Facebook uses to identify photos. However, the procedure consumes a lot of energy. Vincent Garcia (Unité mixte de physique CNRS/Thales) and his colleagues have just taken a step forward in this area by creating directly on a chip an artificial synapse that is capable of learning. They have also developed a physical model that explains this learning capacity. This discovery opens the way to creating a network of synapses and hence intelligent systems requiring less time and energy.

Our brain’s learning process is linked to our synapses, which serve as connections between our neurons. The more the synapse is stimulated, the more the connection is reinforced and learning improved. Researchers took inspiration from this mechanism to design an artificial synapse, called a memristor. This electronic nanocomponent consists of a thin ferroelectric layer sandwiched between two electrodes, and whose resistance can be tuned using voltage pulses similar to those in neurons. If the resistance is low the synaptic connection will be strong, and if the resistance is high the connection will be weak. This capacity to adapt its resistance enables the synapse to learn.

Although research focusing on these artificial synapses is central to the concerns of many laboratories, the functioning of these devices remained largely unknown. The researchers have succeeded, for the first time, in developing a physical model able to predict how they function. This understanding of the process will make it possible to create more complex systems, such as a series of artificial neurons interconnected by these memristors.

As part of the ULPEC H2020 European project, this discovery will be used for real-time shape recognition using an innovative camera1 : the pixels remain inactive, except when they see a change in the angle of vision. The data processing procedure will require less energy, and will take less time to detect the selected objects. The research involved teams from the CNRS/Thales physics joint research unit, the Laboratoire de l’intégration du matériau au système (CNRS/Université de Bordeaux/Bordeaux INP), the University of Arkansas (US), the Centre de nanosciences et nanotechnologies (CNRS/Université Paris-Sud), the Université d’Evry, and Thales.

 

Image synapse


© Sören Boyn / CNRS/Thales physics joint research unit.

Artist’s impression of the electronic synapse: the particles represent electrons circulating through oxide, by analogy with neurotransmitters in biological synapses. The flow of electrons depends on the oxide’s ferroelectric domain structure, which is controlled by electric voltage pulses.


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

Learning through ferroelectric domain dynamics in solid-state synapses by Sören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi, & Vincent Garcia. Nature Communications 8, Article number: 14736 (2017) doi:10.1038/ncomms14736 Published online: 03 April 2017

This paper is open access.

Thales or Thales Group is a French company, from its Wikipedia entry (Note: Links have been removed),

Thales Group (French: [talɛs]) is a French multinational company that designs and builds electrical systems and provides services for the aerospace, defence, transportation and security markets. Its headquarters are in La Défense[2] (the business district of Paris), and its stock is listed on the Euronext Paris.

The company changed its name to Thales (from the Greek philosopher Thales,[3] pronounced [talɛs] reflecting its pronunciation in French) from Thomson-CSF in December 2000 shortly after the £1.3 billion acquisition of Racal Electronics plc, a UK defence electronics group. It is partially state-owned by the French government,[4] and has operations in more than 56 countries. It has 64,000 employees and generated €14.9 billion in revenues in 2016. The Group is ranked as the 475th largest company in the world by Fortune 500 Global.[5] It is also the 10th largest defence contractor in the world[6] and 55% of its total sales are military sales.[4]

The ULPEC (Ultra-Low Power Event-Based Camera) H2020 [Horizon 2020 funded) European project can be found here,

The long term goal of ULPEC is to develop advanced vision applications with ultra-low power requirements and ultra-low latency. The output of the ULPEC project is a demonstrator connecting a neuromorphic event-based camera to a high speed ultra-low power consumption asynchronous visual data processing system (Spiking Neural Network with memristive synapses). Although ULPEC device aims to reach TRL 4, it is a highly application-oriented project: prospective use cases will b…

Finally, for anyone curious about Thales, the philosopher (from his Wikipedia entry), Note: Links have been removed,

Thales of Miletus (/ˈθeɪliːz/; Greek: Θαλῆς (ὁ Μῑλήσιος), Thalēs; c. 624 – c. 546 BC) was a pre-Socratic Greek/Phoenician philosopher, mathematician and astronomer from Miletus in Asia Minor (present-day Milet in Turkey). He was one of the Seven Sages of Greece. Many, most notably Aristotle, regard him as the first philosopher in the Greek tradition,[1][2] and he is otherwise historically recognized as the first individual in Western civilization known to have entertained and engaged in scientific philosophy.[3][4]

What is a multiregional brain-on-a-chip?

In response to having created a multiregional brain-on-a-chip, there’s an explanation from the team at Harvard University (which answers my question) in a Jan. 13, 2017 Harvard John A. Paulson School of Engineering and Applied Sciences news release (also on EurekAlert) by Leah Burrows,

Harvard University researchers have developed a multiregional brain-on-a-chip that models the connectivity between three distinct regions of the brain. The in vitro model was used to extensively characterize the differences between neurons from different regions of the brain and to mimic the system’s connectivity.

“The brain is so much more than individual neurons,” said Ben Maoz, co-first author of the paper and postdoctoral fellow in the Disease Biophysics Group in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). “It’s about the different types of cells and the connectivity between different regions of the brain. When modeling the brain, you need to be able to recapitulate that connectivity because there are many different diseases that attack those connections.”

“Roughly twenty-six percent of the US healthcare budget is spent on neurological and psychiatric disorders,” said Kit Parker, the Tarr Family Professor of Bioengineering and Applied Physics Building at SEAS and Core Faculty Member of the Wyss Institute for Biologically Inspired Engineering at Harvard University. “Tools to support the development of therapeutics to alleviate the suffering of these patients is not only the human thing to do, it is the best means of reducing this cost.”

Researchers from the Disease Biophysics Group at SEAS and the Wyss Institute modeled three regions of the brain most affected by schizophrenia — the amygdala, hippocampus and prefrontal cortex.

They began by characterizing the cell composition, protein expression, metabolism, and electrical activity of neurons from each region in vitro.

“It’s no surprise that neurons in distinct regions of the brain are different but it is surprising just how different they are,” said Stephanie Dauth, co-first author of the paper and former postdoctoral fellow in the Disease Biophysics Group. “We found that the cell-type ratio, the metabolism, the protein expression and the electrical activity all differ between regions in vitro. This shows that it does make a difference which brain region’s neurons you’re working with.”

Next, the team looked at how these neurons change when they’re communicating with one another. To do that, they cultured cells from each region independently and then let the cells establish connections via guided pathways embedded in the chip.

The researchers then measured cell composition and electrical activity again and found that the cells dramatically changed when they were in contact with neurons from different regions.

“When the cells are communicating with other regions, the cellular composition of the culture changes, the electrophysiology changes, all these inherent properties of the neurons change,” said Maoz. “This shows how important it is to implement different brain regions into in vitro models, especially when studying how neurological diseases impact connected regions of the brain.”

To demonstrate the chip’s efficacy in modeling disease, the team doped different regions of the brain with the drug Phencyclidine hydrochloride — commonly known as PCP — which simulates schizophrenia. The brain-on-a-chip allowed the researchers for the first time to look at both the drug’s impact on the individual regions as well as its downstream effect on the interconnected regions in vitro.

The brain-on-a-chip could be useful for studying any number of neurological and psychiatric diseases, including drug addiction, post traumatic stress disorder, and traumatic brain injury.

“To date, the Connectome project has not recognized all of the networks in the brain,” said Parker. “In our studies, we are showing that the extracellular matrix network is an important part of distinguishing different brain regions and that, subsequently, physiological and pathophysiological processes in these brain regions are unique. This advance will not only enable the development of therapeutics, but fundamental insights as to how we think, feel, and survive.”

Here’s an image from the researchers,

Caption: Image of the in vitro model showing three distinct regions of the brain connected by axons. Credit: Disease Biophysics Group/Harvard University

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

Neurons derived from different brain regions are inherently different in vitro: A novel multiregional brain-on-a-chip by Stephanie Dauth, Ben M Maoz, Sean P Sheehy, Matthew A Hemphill, Tara Murty, Mary Kate Macedonia, Angie M Greer, Bogdan Budnik, Kevin Kit Parker. Journal of Neurophysiology Published 28 December 2016 Vol. no. [?] , DOI: 10.1152/jn.00575.2016

This paper is behind a paywall and they haven’t included the vol. no. in the citation I’ve found.

Using melanin in bioelectronic devices

Brazilian researchers are working with melanin to make biosensors and other bioelectronic devices according to a Dec. 20, 2016 news item on phys.org,

Bioelectronics, sometimes called the next medical frontier, is a research field that combines electronics and biology to develop miniaturized implantable devices capable of altering and controlling electrical signals in the human body. Large corporations are increasingly interested: a joint venture in the field has recently been announced by Alphabet, Google’s parent company, and pharmaceutical giant GlaxoSmithKline (GSK).

One of the challenges that scientists face in developing bioelectronic devices is identifying and finding ways to use materials that conduct not only electrons but also ions, as most communication and other processes in the human organism use ionic biosignals (e.g., neurotransmitters). In addition, the materials must be biocompatible.

Resolving this challenge is one of the motivations for researchers at São Paulo State University’s School of Sciences (FC-UNESP) at Bauru in Brazil. They have succeeded in developing a novel route to more rapidly synthesize and to enable the use of melanin, a polymeric compound that pigments the skin, eyes and hair of mammals and is considered one of the most promising materials for use in miniaturized implantable devices such as biosensors.

A Dec. 14, 2016 FAPESP (São Paulo Research Foundation) press release, which originated the news item, further describes both the research and a recent meeting where the research was shared (Note: A link has been removed),

Some of the group’s research findings were presented at FAPESP Week Montevideo during a round-table session on materials science and engineering.

The symposium was organized by the Montevideo Group Association of Universities (AUGM), Uruguay’s University of the Republic (UdelaR) and FAPESP and took place on November 17-18 at UdelaR’s campus in Montevideo. Its purpose was to strengthen existing collaborations and establish new partnerships among South American scientists in a range of knowledge areas. Researchers and leaders of institutions in Uruguay, Brazil, Argentina, Chile and Paraguay attended the meeting.

“All the materials that have been tested to date for applications in bioelectronics are entirely synthetic,” said Carlos Frederico de Oliveira Graeff, a professor at UNESP Bauru and principal investigator for the project, in an interview given to Agência FAPESP.

“One of the great advantages of melanin is that it’s a totally natural compound and biocompatible with the human body: hence its potential use in electronic devices that interface with brain neurons, for example.”

Application challenges

According to Graeff, the challenges of using melanin as a material for the development of bioelectronic devices include the fact that like other carbon-based materials, such as graphene, melanin is not easily dispersible in an aqueous medium, a characteristic that hinders its application in thin-film production.

Furthermore, the conventional process for synthesizing melanin is complex: several steps are hard to control, it can last up to 56 days, and it can result in disorderly structures.

In a series of studies performed in recent years at the Center for Research and Development of Functional Materials (CDFM), where Graeff is a leading researcher and which is one of the Research, Innovation and Dissemination Centers (RIDCs) funded by FAPESP, he and his collaborators managed to obtain biosynthetic melanin with good dispersion in water and a strong resemblance to natural melanin using a novel synthesis route.

The process developed by the group at CDMF takes only a few hours and is based on changes in parameters such as temperature and the application of oxygen pressure to promote oxidation of the material.

By applying oxygen pressure, the researchers were able to increase the density of carboxyl groups, which are organic functional groups consisting of a carbon atom double bonded to an oxygen atom and single bonded to a hydroxyl group (oxygen + hydrogen). This enhances solubility and facilitates the suspension of biosynthetic melanin in water.

“The production of thin films of melanin with high homogeneity and quality is made far easier by these characteristics,” Graeff said.

By increasing the density of carboxyl groups, the researchers were also able to make biosynthetic melanin more similar to the biological compound.

In living organisms, an enzyme that participates in the synthesis of melanin facilitates the production of carboxylic acids. The new melanin synthesis route enabled the researchers to mimic the role of this enzyme chemically while increasing carboxyl group density.

“We’ve succeeded in obtaining a material that’s very close to biological melanin by chemical synthesis and in producing high-quality film for use in bioelectronic devices,” Graeff said.

Through collaboration with colleagues at research institutions in Canada [emphasis mine], the Brazilian researchers have begun using the material in a series of applications, including electrical contacts, pH sensors and photovoltaic cells.

More recently, they have embarked on an attempt to develop a transistor, a semiconductor device used to amplify or switch electronic signals and electrical power.

“Above all, we aim to produce transistors precisely in order to enhance this coupling of electronics with biological systems,” Graeff said.

I’m glad to have gotten some information about the work in South America. It’s one of FrogHeart’s shortcomings that I have so little about the research in that area of the world. I believe this is largely due to my lack of Spanish language skills. Perhaps one day there’ll be a universal translator that works well. In the meantime, it was a surprise to see Canada mentioned in this piece. I wonder which Canadian research institutions are involved with this research in South America.