Tag Archives: neurons

Replicating brain’s neural networks with 3D nanoprinting

An announcement about European Union funding for a project to reproduce neural networks by 3D nanoprinting can be found in a June 10, 2016 news item on Nanowerk,

The MESO-BRAIN consortium has received a prestigious award of €3.3million in funding from the European Commission as part of its Future and Emerging Technology (FET) scheme. The project aims to develop three-dimensional (3D) human neural networks with specific biological architecture, and the inherent ability to interrogate the network’s brain-like activity both electrophysiologically and optically. It is expected that the MESO-BRAIN will facilitate a better understanding of human disease progression, neuronal growth and enable the development of large-scale human cell-based assays to test the modulatory effects of pharmacological and toxicological compounds on neural network activity. The use of more physiologically relevant human models will increase drug screening efficiency and reduce the need for animal testing.

A June 9, 2016 Institute of Photonic Sciences (ICFO) press release (also on EurekAlert), which originated the news item, provides more detail,

About the MESO-BRAIN project

The MESO-BRAIN project’s cornerstone will use human induced pluripotent stem cells (iPSCs) that have been differentiated into neurons upon a defined and reproducible 3D scaffold to support the development of human neural networks that emulate brain activity. The structure will be based on a brain cortical module and will be unique in that it will be designed and produced using nanoscale 3D-laser-printed structures incorporating nano-electrodes to enable downstream electrophysiological analysis of neural network function. Optical analysis will be conducted using cutting-edge light sheet-based, fast volumetric imaging technology to enable cellular resolution throughout the 3D network. The MESO-BRAIN project will allow for a comprehensive and detailed investigation of neural network development in health and disease.

Prof Edik Rafailov, Head of the MESO-BRAIN project (Aston University) said: “What we’re proposing to achieve with this project has, until recently, been the stuff of science fiction. Being able to extract and replicate neural networks from the brain through 3D nanoprinting promises to change this. The MESO-BRAIN project has the potential to revolutionise the way we are able to understand the onset and development of disease and discover treatments for those with dementia or brain injuries. We cannot wait to get started!”

The MESO-BRAIN project will launch in September 2016 and research will be conducted over three years.

About the MESO-BRAIN consortium

Each of the consortium partners have been chosen for the highly specific skills & knowledge that they bring to this project. These include technologies and expertise in stem cells, photonics, physics, 3D nanoprinting, electrophysiology, molecular biology, imaging and commercialisation.

Aston University (UK) Aston Institute of Photonic Technologies (School of Engineering and Applied Science) is one of the largest photonic groups in UK and an internationally recognised research centre in the fields of lasers, fibre-optics, high-speed optical communications, nonlinear and biomedical photonics. The Cell & Tissue Biomedical Research Group (Aston Research Centre for Healthy Ageing) combines collective expertise in genetic manipulation, tissue engineering and neuronal modelling with the electrophysiological and optical analysis of human iPSC-derived neural networks. Axol Bioscience Ltd. (UK) was founded to fulfil the unmet demand for high quality, clinically relevant human iPSC-derived cells for use in biomedical research and drug discovery. The Laser Zentrum Hannover (Germany) is a leading research organisation in the fields of laser development, material processing, laser medicine, and laser-based nanotechnologies. The Neurophysics Group (Physics Department) at University of Barcelona (Spain) are experts in combing experiments with theoretical and computational modelling to infer functional connectivity in neuronal circuits. The Institute of Photonic Sciences (ICFO) (Spain) is a world-leading research centre in photonics with expertise in several microscopy techniques including light sheet imaging. KITE Innovation (UK) helps to bridge the gap between the academic and business sectors in supporting collaboration, enterprise, and knowledge-based business development.

For anyone curious about the FET funding scheme, there’s this from the press release,

Horizon 2020 aims to ensure Europe produces world-class science by removing barriers to innovation through funding programmes such as the FET. The FET (Open) funds forward-looking collaborations between advanced multidisciplinary science and cutting-edge engineering for radically new future technologies. The published success rate is below 1.4%, making it amongst the toughest in the Horizon 2020 suite of funding schemes. The MESO-BRAIN proposal scored a perfect 5/5.

You can find out more about the MESO-BRAIN project on its ICFO webpage.

They don’t say anything about it but I can’t help wondering if the scientists aren’t also considering the possibility of creating an artificial brain.

Memristor-based electronic synapses for neural networks

Caption: Neuron connections in biological neural networks. Credit: MIPT press office

Caption: Neuron connections in biological neural networks. Credit: MIPT press office

Russian scientists have recently published a paper about neural networks and electronic synapses based on ‘thin film’ memristors according to an April 19, 2016 news item on Nanowerk,

A team of scientists from the Moscow Institute of Physics and Technology (MIPT) have created prototypes of “electronic synapses” based on ultra-thin films of hafnium oxide (HfO2). These prototypes could potentially be used in fundamentally new computing systems.

An April 20, 2016 MIPT press release (also on EurekAlert), which originated the news item (the date inconsistency likely due to timezone differences) explains the connection between thin films and memristors,

The group of researchers from MIPT have made HfO2-based memristors measuring just 40×40 nm2. The nanostructures they built exhibit properties similar to biological synapses. Using newly developed technology, the memristors were integrated in matrices: in the future this technology may be used to design computers that function similar to biological neural networks.

Memristors (resistors with memory) are devices that are able to change their state (conductivity) depending on the charge passing through them, and they therefore have a memory of their “history”. In this study, the scientists used devices based on thin-film hafnium oxide, a material that is already used in the production of modern processors. This means that this new lab technology could, if required, easily be used in industrial processes.

“In a simpler version, memristors are promising binary non-volatile memory cells, in which information is written by switching the electric resistance – from high to low and back again. What we are trying to demonstrate are much more complex functions of memristors – that they behave similar to biological synapses,” said Yury Matveyev, the corresponding author of the paper, and senior researcher of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, commenting on the study.

The press release offers a description of biological synapses and their relationship to learning and memory,

A synapse is point of connection between neurons, the main function of which is to transmit a signal (a spike – a particular type of signal, see fig. 2) from one neuron to another. Each neuron may have thousands of synapses, i.e. connect with a large number of other neurons. This means that information can be processed in parallel, rather than sequentially (as in modern computers). This is the reason why “living” neural networks are so immensely effective both in terms of speed and energy consumption in solving large range of tasks, such as image / voice recognition, etc.

Over time, synapses may change their “weight”, i.e. their ability to transmit a signal. This property is believed to be the key to understanding the learning and memory functions of thebrain.

From the physical point of view, synaptic “memory” and “learning” in the brain can be interpreted as follows: the neural connection possesses a certain “conductivity”, which is determined by the previous “history” of signals that have passed through the connection. If a synapse transmits a signal from one neuron to another, we can say that it has high “conductivity”, and if it does not, we say it has low “conductivity”. However, synapses do not simply function in on/off mode; they can have any intermediate “weight” (intermediate conductivity value). Accordingly, if we want to simulate them using certain devices, these devices will also have to have analogous characteristics.

The researchers have provided an illustration of a biological synapse,

Fig.2 The type of electrical signal transmitted by neurons (a “spike”). The red lines are various other biological signals, the black line is the averaged signal. Source: MIPT press office

Fig.2 The type of electrical signal transmitted by neurons (a “spike”). The red lines are various other biological signals, the black line is the averaged signal. Source: MIPT press office

Now, the press release ties the memristor information together with the biological synapse information to describe the new work at the MIPT,

As in a biological synapse, the value of the electrical conductivity of a memristor is the result of its previous “life” – from the moment it was made.

There is a number of physical effects that can be exploited to design memristors. In this study, the authors used devices based on ultrathin-film hafnium oxide, which exhibit the effect of soft (reversible) electrical breakdown under an applied external electric field. Most often, these devices use only two different states encoding logic zero and one. However, in order to simulate biological synapses, a continuous spectrum of conductivities had to be used in the devices.

“The detailed physical mechanism behind the function of the memristors in question is still debated. However, the qualitative model is as follows: in the metal–ultrathin oxide–metal structure, charged point defects, such as vacancies of oxygen atoms, are formed and move around in the oxide layer when exposed to an electric field. It is these defects that are responsible for the reversible change in the conductivity of the oxide layer,” says the co-author of the paper and researcher of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, Sergey Zakharchenko.

The authors used the newly developed “analogue” memristors to model various learning mechanisms (“plasticity”) of biological synapses. In particular, this involved functions such as long-term potentiation (LTP) or long-term depression (LTD) of a connection between two neurons. It is generally accepted that these functions are the underlying mechanisms of  memory in the brain.

The authors also succeeded in demonstrating a more complex mechanism – spike-timing-dependent plasticity, i.e. the dependence of the value of the connection between neurons on the relative time taken for them to be “triggered”. It had previously been shown that this mechanism is responsible for associative learning – the ability of the brain to find connections between different events.

To demonstrate this function in their memristor devices, the authors purposefully used an electric signal which reproduced, as far as possible, the signals in living neurons, and they obtained a dependency very similar to those observed in living synapses (see fig. 3).

Fig.3. The change in conductivity of memristors depending on the temporal separation between "spikes"(rigth) and thr change in potential of the neuron connections in biological neural networks. Source: MIPT press office

Fig.3. The change in conductivity of memristors depending on the temporal separation between “spikes”(rigth) and thr change in potential of the neuron connections in biological neural networks. Source: MIPT press office

These results allowed the authors to confirm that the elements that they had developed could be considered a prototype of the “electronic synapse”, which could be used as a basis for the hardware implementation of artificial neural networks.

“We have created a baseline matrix of nanoscale memristors demonstrating the properties of biological synapses. Thanks to this research, we are now one step closer to building an artificial neural network. It may only be the very simplest of networks, but it is nevertheless a hardware prototype,” said the head of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, Andrey Zenkevich.

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

Crossbar Nanoscale HfO2-Based Electronic Synapses by Yury Matveyev, Roman Kirtaev, Alena Fetisova, Sergey Zakharchenko, Dmitry Negrov and Andrey Zenkevich. Nanoscale Research Letters201611:147 DOI: 10.1186/s11671-016-1360-6

Published: 15 March 2016

This is an open access paper.

Graphene Flagship high points

The European Union’s Graphene Flagship project has provided a series of highlights in place of an overview for the project’s ramp-up phase (in 2013 the Graphene Flagship was announced as one of two winners of a science competition, the other winner was the Human Brain Project, with two prizes of 1B Euros for each project). Here are the highlights from the April 19, 2016 Graphene Flagship press release,

Graphene and Neurons – the Best of Friends

Flagship researchers have shown that it is possible to interface untreated graphene with neuron cells whilst maintaining the integrity of these vital cells [1]. This result is a significant first step towards using graphene to produce better deep brain implants which can both harness and control the brain.

Graphene and Neurons

This paper emerged from the Graphene Flagship Work Package Health and Environment. Prof. Prato, the WP leader from the University of Trieste in Italy, commented that “We are currently involved in frontline research in graphene technology towards biomedical applications, exploring the interactions between graphene nano- and micro-sheets with the sophisticated signalling machinery of nerve cells. Our work is a first step in that direction.”

[1] Fabbro A., et al., Graphene-Based Interfaces do not Alter Target Nerve Cells. ACS Nano, 10 (1), 615 (2016).

Pressure Sensing with Graphene: Quite a Squeeze

The Graphene Flagship developed a small, robust, highly efficient squeeze film pressure sensor [2]. Pressure sensors are present in most mobile handsets and by replacing current sensor membranes with a graphene membrane they allow the sensor to decrease in size and significantly increase its responsiveness and lifetime.

Discussing this work which emerged from the Graphene Flagship Work Package Sensors is the paper’s lead author, Robin Dolleman from the Technical University of Delft in The Netherlands “After spending a year modelling various systems the idea of the squeeze-film pressure sensor was formed. Funding from the Graphene Flagship provided the opportunity to perform the experiments and we obtained very good results. We built a squeeze-film pressure sensor from 31 layers of graphene, which showed a 45 times higher response than silicon based devices, while reducing the area of the device by a factor of 25. Currently, our work is focused on obtaining similar results on monolayer graphene.”


[2] Dolleman R. J. et al., Graphene Squeeze-Film Pressure Sensors. Nano Lett., 16, 568 (2016)

Frictionless Graphene

Image caption: A graphene nanoribbon was anchored at the tip of a atomic force microscope and dragged over a gold surface. The observed friction force was extremely low.

Image caption: A graphene nanoribbon was anchored at the tip of a atomic force microscope and dragged over a gold surface. The observed friction force was extremely low.

Research done within the Graphene Flagship, has observed the onset of superlubricity in graphene nanoribbons sliding on a surface, unravelling the role played by ribbon size and elasticity [3]. This important finding opens up the development potential of nanographene frictionless coatings. This research lead by the Graphene Flagship Work Package Nanocomposites also involved researchers from Work Package Materials and Work Package Health and the Environment, a shining example of the inter-disciplinary, cross-collaborative approach to research undertaken within the Graphene Flagship. Discussing this further is the Work Package Nanocomposites Leader, Dr Vincenzo Palermo from CNR National Research Council, Italy “Strengthening the collaboration and interactions with other Flagship Work Packages created added value through a strong exchange of materials, samples and information”.

[3] Kawai S., et al., Superlubricity of graphene nanoribbons on gold surfaces. Science. 351, 6276, 957 (2016) 

​Graphene Paddles Forward

Work undertaken within the Graphene Flagship saw Spanish automotive interiors specialist, and Flagship partner, Grupo Antolin SA work in collaboration with Roman Kayaks to develop an innovative kayak that incorporates graphene into its thermoset polymeric matrices. The use of graphene and related materials results in a significant increase in both impact strength and stiffness, improving the resistance to breakage in critical areas of the boat. Pushing the graphene canoe well beyond the prototype demonstration bubble, Roman Kayaks chose to use the K-1 kayak in the Canoe Marathon World Championships held in September in Gyor, Hungary where the Graphene Canoe was really put through its paces.

Talking further about this collaboration from the Graphene Flagship Work Package Production is the WP leader, Dr Ken Teo from Aixtron Ltd., UK “In the Graphene Flagship project, Work Package Production works as a technology enabler for real-world applications. Here we show the worlds first K-1 kayak (5.2 meters long), using graphene related materials developed by Grupo Antolin. We are very happy to see that graphene is creating value beyond traditional industries.” 

​Graphene Production – a Kitchen Sink Approach

Researchers from the Graphene Flagship have devised a way of producing large quantities of graphene by separating graphite flakes in liquids with a rotating tool that works in much the same way as a kitchen blender [4]. This paves the way to mass production of high quality graphene at a low cost.

The method was produced within the Graphene Flagship Work Package Production and is talked about further here by the WP deputy leader, Prof. Jonathan Coleman from Trinity College Dublin, Ireland “This technique produced graphene at higher rates than most other methods, and produced sheets of 2D materials that will be useful in a range of applications, from printed electronics to energy generation.” 

[4] Paton K.R., et al., Scalable production of large quantities of defect-free few-layer graphene by shear exfoliation in liquids. Nat. Mater. 13, 624 (2014).

Flexible Displays – Rolled Up in your Pocket

Working with researchers from the Graphene Flagship the Flagship partner, FlexEnable, demonstrated the world’s first flexible display with graphene incorporated into its pixel backplane. Combined with an electrophoretic imaging film, the result is a low-power, durable display suitable for use in many and varied environments.

Emerging from the Graphene Flagship Work Package Flexible Electronics this illustrates the power of collaboration.  Talking about this is the WP leader Dr Henrik Sandberg from the VTT Technical Research Centre of Finland Ltd., Finland “Here we show the power of collaboration. To deliver these flexible demonstrators and prototypes we have seen materials experts working together with components manufacturers and system integrators. These devices will have a potential impact in several emerging fields such as wearables and the Internet of Things.”

​Fibre-Optics Data Boost from Graphene

A team of researches from the Graphene Flagship have demonstrated high-performance photo detectors for infrared fibre-optic communication systems based on wafer-scale graphene [5]. This can increase the amount of information transferred whilst at the same time make the devises smaller and more cost effective.

Discussing this work which emerged from the Graphene Flagship Work Package Optoelectronics is the paper’s lead author, Daniel Schall from AMO, Germany “Graphene has outstanding properties when it comes to the mobility of its electric charge carriers, and this can increase the speed at which electronic devices operate.”

[5] Schall D., et al., 50 GBit/s Photodetectors Based on Wafer-Scale Graphene for Integrated Silicon Photonic Communication Systems. ACS Photonics. 1 (9), 781 (2014)

​Rechargeable Batteries with Graphene

A number of different research groups within the Graphene Flagship are working on rechargeable batteries. One group has developed a graphene-based rechargeable battery of the lithium-ion type used in portable electronic devices [6]. Graphene is incorporated into the battery anode in the form of a spreadable ink containing a suspension of graphene nanoflakes giving an increased energy efficiency of 20%. A second group of researchers have demonstrated a lithium-oxygen battery with high energy density, efficiency and stability [7]. They produced a device with over 90% efficiency that may be recharged more than 2,000 times. Their lithium-oxygen cell features a porous, ‘fluffy’ electrode made from graphene together with additives that alter the chemical reactions at work in the battery.

Graphene Flagship researchers show how the 2D material graphene can improve the energy capacity, efficiency and stability of lithium-oxygen batteries.

Both devices were developed in different groups within the Graphene Flagship Work Package Energy and speaking of the technology further is Prof. Clare Grey from Cambridge University, UK “What we’ve achieved is a significant advance for this technology, and suggests whole new areas for research – we haven’t solved all the problems inherent to this chemistry, but our results do show routes forward towards a practical device”.

[6] Liu T., et al. Cycling Li-O2 batteries via LiOH formation and decomposition. Science. 350, 6260, 530 (2015)

[7] Hassoun J., et al., An Advanced Lithium-Ion Battery Based on a Graphene Anode and a Lithium Iron Phosphate Cathode. Nano Lett., 14 (8), 4901 (2014)

Graphene – What and Why?

Graphene is a two-dimensional material formed by a single atom-thick layer of carbon, with the carbon atoms arranged in a honeycomb-like lattice. This transparent, flexible material has a number of unique properties. For example, it is 100 times stronger than steel, and conducts electricity and heat with great efficiency.

A number of practical applications for graphene are currently being developed. These include flexible and wearable electronics and antennas, sensors, optoelectronics and data communication systems, medical and bioengineering technologies, filtration, super-strong composites, photovoltaics and energy storage.

Graphene and Beyond

The Graphene Flagship also covers other layered materials, as well as hybrids formed by combining graphene with these complementary materials, or with other materials and structures, ranging from polymers, to metals, cement, and traditional semiconductors such as silicon. Graphene is just the first of thousands of possible single layer materials. The Flagship plans to accelerate their journey from laboratory to factory floor.

Especially exciting is the possibility of stacking monolayers of different elements to create materials not found in nature, with properties tailored for specific applications. Such composite layered materials could be combined with other nanomaterials, such as metal nanoparticles, in order to further enhance their properties and uses.​

Graphene – the Fruit of European Scientific Excellence

Europe, North America and Asia are all active centres of graphene R&D, but Europe has special claim to be at the centre of this activity. The ground-breaking experiments on graphene recognised in the award of the 2010 Nobel Prize in Physics were conducted by European physicists, Andre Geim and Konstantin Novoselov, both at Manchester University. Since then, graphene research in Europe has continued apace, with major public funding for specialist centres, and the stimulation of academic-industrial partnerships devoted to graphene and related materials. It is European scientists and engineers who as part of the Graphene Flagship are closely coordinating research efforts, and accelerating the transfer of layered materials from the laboratory to factory floor.

For anyone who would like links to the published papers, you can check out an April 20, 2016 news item featuring the Graphene Flagship highlights on Nanowerk.

3D microtopographic scaffolds for transplantation and generation of reprogrammed human neurons

Should this technology prove successful once they start testing on people, the stated goal is to use it for the treatment of human neurodegenerative disorders such as Parkinson’s disease.  But, I can’t help wondering if they might also consider constructing an artificial brain.

Getting back to the 3D scaffolds for neurons, a March 17, 2016 US National Institutes of Health (NIH) news release (also on EurekAlert), makes the announcement,

National Institutes of Health-funded scientists have developed a 3D micro-scaffold technology that promotes reprogramming of stem cells into neurons, and supports growth of neuronal connections capable of transmitting electrical signals. The injection of these networks of functioning human neural cells — compared to injecting individual cells — dramatically improved their survival following transplantation into mouse brains. This is a promising new platform that could make transplantation of neurons a viable treatment for a broad range of human neurodegenerative disorders.

Previously, transplantation of neurons to treat neurodegenerative disorders, such as Parkinson’s disease, had very limited success due to poor survival of neurons that were injected as a solution of individual cells. The new research is supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), part of NIH.

“Working together, the stem cell biologists and the biomaterials experts developed a system capable of shuttling neural cells through the demanding journey of transplantation and engraftment into host brain tissue,” said Rosemarie Hunziker, Ph.D., director of the NIBIB Program in Tissue Engineering and Regenerative Medicine. “This exciting work was made possible by the close collaboration of experts in a wide range of disciplines.”

The research was performed by researchers from Rutgers University, Piscataway, New Jersey, departments of Biomedical Engineering, Neuroscience and Cell Biology, Chemical and Biochemical Engineering, and the Child Health Institute; Stanford University School of Medicine’s Institute of Stem Cell Biology and Regenerative Medicine, Stanford, California; the Human Genetics Institute of New Jersey, Piscataway; and the New Jersey Center for Biomaterials, Piscataway. The results are reported in the March 17, 2016 issue of Nature Communications.

The researchers experimented in creating scaffolds made of different types of polymer fibers, and of varying thickness and density. They ultimately created a web of relatively thick fibers using a polymer that stem cells successfully adhered to. The stem cells used were human induced pluripotent stem cells (iPSCs), which can be readily generated from adult cell types such as skin cells. The iPSCs were induced to differentiate into neural cells by introducing the protein NeuroD1 into the cells.

The space between the polymer fibers turned out to be critical. “If the scaffolds were too dense, the stem cell-derived neurons were unable to integrate into the scaffold, whereas if they are too sparse then the network organization tends to be poor,” explained Prabhas Moghe, Ph.D., distinguished professor of biomedical engineering & chemical engineering at Rutgers University and co-senior author of the paper. “The optimal pore size was one that was large enough for the cells to populate the scaffold but small enough that the differentiating neurons sensed the presence of their neighbors and produced outgrowths resulting in cell-to-cell contact. This contact enhances cell survival and development into functional neurons able to transmit an electrical signal across the developing neural network.”

To test the viability of neuron-seeded scaffolds when transplanted, the researchers created micro-scaffolds that were small enough for injection into mouse brain tissue using a standard hypodermic needle. They injected scaffolds carrying the human neurons into brain slices from mice and compared them to human neurons injected as individual, dissociated cells.

The neurons on the scaffolds had dramatically increased cell-survival compared with the individual cell suspensions. The scaffolds also promoted improved neuronal outgrowth and electrical activity. Neurons injected individually in suspension resulted in very few cells surviving the transplant procedure.

Human neurons on scaffolds compared to neurons in solution were then tested when injected into the brains of live mice. Similar to the results in the brain slices, the survival rate of neurons on the scaffold network was increased nearly 40-fold compared to injected isolated cells. A critical finding was that the neurons on the micro-scaffolds expressed proteins that are involved in the growth and maturation of neural synapses–a good indication that the transplanted neurons were capable of functionally integrating into the host brain tissue.

The success of the study gives this interdisciplinary group reason to believe that their combined areas of expertise have resulted in a system with much promise for eventual treatment of human neurodegenerative disorders. In fact, they are now refining their system for specific use as an eventual transplant therapy for Parkinson’s disease. The plan is to develop methods to differentiate the stem cells into neurons that produce dopamine, the specific neuron type that degenerates in individuals with Parkinson’s disease. The work also will include fine-tuning the scaffold materials, mechanics and dimensions to optimize the survival and function of dopamine-producing neurons, and finding the best mouse models of the disease to test this Parkinson’s-specific therapy.

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

Generation and transplantation of reprogrammed human neurons in the brain using 3D microtopographic scaffolds by Aaron L. Carlson, Neal K. Bennett, Nicola L. Francis, Apoorva Halikere, Stephen Clarke, Jennifer C. Moore, Ronald P. Hart, Kenneth Paradiso, Marius Wernig, Joachim Kohn, Zhiping P. Pang, & Prabhas V. Moghe. Nature Communications 7, Article number: 10862  doi:10.1038/ncomms10862 Published 17 March 2016

This paper is open access.

Graphene and neurons in a UK-Italy-Spain collaboration

There’s been a lot of talk about using graphene-based implants in the brain due to the material’s flexibility along with its other properties. A step forward has been taking according to a Jan. 29, 2016 news item on phys.org,

Researchers have successfully demonstrated how it is possible to interface graphene – a two-dimensional form of carbon – with neurons, or nerve cells, while maintaining the integrity of these vital cells. The work may be used to build graphene-based electrodes that can safely be implanted in the brain, offering promise for the restoration of sensory functions for amputee or paralysed patients, or for individuals with motor disorders such as epilepsy or Parkinson’s disease.

A Jan. 29, 2016 Cambridge University press release (also on EurekAlert), which originated the news item, provides more detail,

Previously, other groups had shown that it is possible to use treated graphene to interact with neurons. However the signal to noise ratio from this interface was very low. By developing methods of working with untreated graphene, the researchers retained the material’s electrical conductivity, making it a significantly better electrode.

“For the first time we interfaced graphene to neurons directly,” said Professor Laura Ballerini of the University of Trieste in Italy. “We then tested the ability of neurons to generate electrical signals known to represent brain activities, and found that the neurons retained their neuronal signalling properties unaltered. This is the first functional study of neuronal synaptic activity using uncoated graphene based materials.”

Our understanding of the brain has increased to such a degree that by interfacing directly between the brain and the outside world we can now harness and control some of its functions. For instance, by measuring the brain’s electrical impulses, sensory functions can be recovered. This can be used to control robotic arms for amputee patients or any number of basic processes for paralysed patients – from speech to movement of objects in the world around them. Alternatively, by interfering with these electrical impulses, motor disorders (such as epilepsy or Parkinson’s) can start to be controlled.

Scientists have made this possible by developing electrodes that can be placed deep within the brain. These electrodes connect directly to neurons and transmit their electrical signals away from the body, allowing their meaning to be decoded.

However, the interface between neurons and electrodes has often been problematic: not only do the electrodes need to be highly sensitive to electrical impulses, but they need to be stable in the body without altering the tissue they measure.

Too often the modern electrodes used for this interface (based on tungsten or silicon) suffer from partial or complete loss of signal over time. This is often caused by the formation of scar tissue from the electrode insertion, which prevents the electrode from moving with the natural movements of the brain due to its rigid nature.

Graphene has been shown to be a promising material to solve these problems, because of its excellent conductivity, flexibility, biocompatibility and stability within the body.

Based on experiments conducted in rat brain cell cultures, the researchers found that untreated graphene electrodes interfaced well with neurons. By studying the neurons with electron microscopy and immunofluorescence the researchers found that they remained healthy, transmitting normal electric impulses and, importantly, none of the adverse reactions which lead to the damaging scar tissue were seen.

According to the researchers, this is the first step towards using pristine graphene-based materials as an electrode for a neuro-interface. In future, the researchers will investigate how different forms of graphene, from multiple layers to monolayers, are able to affect neurons, and whether tuning the material properties of graphene might alter the synapses and neuronal excitability in new and unique ways. “Hopefully this will pave the way for better deep brain implants to both harness and control the brain, with higher sensitivity and fewer unwanted side effects,” said Ballerini.

“We are currently involved in frontline research in graphene technology towards biomedical applications,” said Professor Maurizio Prato from the University of Trieste. “In this scenario, the development and translation in neurology of graphene-based high-performance biodevices requires the exploration of the interactions between graphene nano- and micro-sheets with the sophisticated signalling machinery of nerve cells. Our work is only a first step in that direction.”

“These initial results show how we are just at the tip of the iceberg when it comes to the potential of graphene and related materials in bio-applications and medicine,” said Professor Andrea Ferrari, Director of the Cambridge Graphene Centre. “The expertise developed at the Cambridge Graphene Centre allows us to produce large quantities of pristine material in solution, and this study proves the compatibility of our process with neuro-interfaces.”

The research was funded by the Graphene Flagship [emphasis mine],  a European initiative which promotes a collaborative approach to research with an aim of helping to translate graphene out of the academic laboratory, through local industry and into society.

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

Graphene-Based Interfaces Do Not Alter Target Nerve Cells by Alessandra Fabbro, Denis Scaini, Verónica León, Ester Vázquez, Giada Cellot, Giulia Privitera, Lucia Lombardi, Felice Torrisi, Flavia Tomarchio, Francesco Bonaccorso, Susanna Bosi, Andrea C. Ferrari, Laura Ballerini, and Maurizio Prato. ACS Nano, 2016, 10 (1), pp 615–623 DOI: 10.1021/acsnano.5b05647 Publication Date (Web): December 23, 2015

Copyright © 2015 American Chemical Society

This paper is behind a paywall.

There are a couple things I found a bit odd about this project. First, all of the funding is from the Graphene Flagship initiative. I was expecting to see at least some funding from the European Union’s other mega-sized science initiative, the Human Brain Project. Second, there was no mention of Spain nor were there any quotes from the Spanish researchers. For the record, the Spanish institutions represented were: University of Castilla-La Mancha, Carbon Nanobiotechnology Laboratory, and the Basque Foundation for Science.

Plastic memristors for neural networks

There is a very nice explanation of memristors and computing systems from the Moscow Institute of Physics and Technology (MIPT). First their announcement, from a Jan. 27, 2016 news item on ScienceDaily,

A group of scientists has created a neural network based on polymeric memristors — devices that can potentially be used to build fundamentally new computers. These developments will primarily help in creating technologies for machine vision, hearing, and other machine sensory systems, and also for intelligent control systems in various fields of applications, including autonomous robots.

The authors of the new study focused on a promising area in the field of memristive neural networks – polymer-based memristors – and discovered that creating even the simplest perceptron is not that easy. In fact, it is so difficult that up until the publication of their paper in the journal Organic Electronics, there were no reports of any successful experiments (using organic materials). The experiments conducted at the Nano-, Bio-, Information and Cognitive Sciences and Technologies (NBIC) centre at the Kurchatov Institute by a joint team of Russian and Italian scientists demonstrated that it is possible to create very simple polyaniline-based neural networks. Furthermore, these networks are able to learn and perform specified logical operations.

A Jan. 27, 2016 MIPT press release on EurekAlert, which originated the news item, offers an explanation of memristors and a description of the research,

A memristor is an electric element similar to a conventional resistor. The difference between a memristor and a traditional element is that the electric resistance in a memristor is dependent on the charge passing through it, therefore it constantly changes its properties under the influence of an external signal: a memristor has a memory and at the same time is also able to change data encoded by its resistance state! In this sense, a memristor is similar to a synapse – a connection between two neurons in the brain that is able, with a high level of plasticity, to modify the efficiency of signal transmission between neurons under the influence of the transmission itself. A memristor enables scientists to build a “true” neural network, and the physical properties of memristors mean that at the very minimum they can be made as small as conventional chips.

Some estimates indicate that the size of a memristor can be reduced up to ten nanometers, and the technologies used in the manufacture of the experimental prototypes could, in theory, be scaled up to the level of mass production. However, as this is “in theory”, it does not mean that chips of a fundamentally new structure with neural networks will be available on the market any time soon, even in the next five years.

The plastic polyaniline was not chosen by chance. Previous studies demonstrated that it can be used to create individual memristors, so the scientists did not have to go through many different materials. Using a polyaniline solution, a glass substrate, and chromium electrodes, they created a prototype with dimensions that, at present, are much larger than those typically used in conventional microelectronics: the strip of the structure was approximately one millimeter wide (they decided to avoid miniaturization for the moment). All of the memristors were tested for their electrical characteristics: it was found that the current-voltage characteristic of the devices is in fact non-linear, which is in line with expectations. The memristors were then connected to a single neuromorphic network.

A current-voltage characteristic (or IV curve) is a graph where the horizontal axis represents voltage and the vertical axis the current. In conventional resistance, the IV curve is a straight line; in strict accordance with Ohm’s Law, current is proportional to voltage. For a memristor, however, it is not just the voltage that is important, but the change in voltage: if you begin to gradually increase the voltage supplied to the memristor, it will increase the current passing through it not in a linear fashion, but with a sharp bend in the graph and at a certain point its resistance will fall sharply.

Then if you begin to reduce the voltage, the memristor will remain in its conducting state for some time, after which it will change its properties rather sharply again to decrease its conductivity. Experimental samples with a voltage increase of 0.5V hardly allowed any current to pass through (around a few tenths of a microamp), but when the voltage was reduced by the same amount, the ammeter registered a figure of 5 microamps. Microamps are of course very small units, but in this case it is the contrast that is most significant: 0.1 μA to 5 μA is a difference of fifty times! This is more than enough to make a clear distinction between the two signals.

After checking the basic properties of individual memristors, the physicists conducted experiments to train the neural network. The training (it is a generally accepted term and is therefore written without inverted commas) involves applying electric pulses at random to the inputs of a perceptron. If a certain combination of electric pulses is applied to the inputs of a perceptron (e.g. a logic one and a logic zero at two inputs) and the perceptron gives the wrong answer, a special correcting pulse is applied to it, and after a certain number of repetitions all the internal parameters of the device (namely memristive resistance) reconfigure themselves, i.e. they are “trained” to give the correct answer.

The scientists demonstrated that after about a dozen attempts their new memristive network is capable of performing NAND logical operations, and then it is also able to learn to perform NOR operations. Since it is an operator or a conventional computer that is used to check for the correct answer, this method is called the supervised learning method.

Needless to say, an elementary perceptron of macroscopic dimensions with a characteristic reaction time of tenths or hundredths of a second is not an element that is ready for commercial production. However, as the researchers themselves note, their creation was made using inexpensive materials, and the reaction time will decrease as the size decreases: the first prototype was intentionally enlarged to make the work easier; it is physically possible to manufacture more compact chips. In addition, polyaniline can be used in attempts to make a three-dimensional structure by placing the memristors on top of one another in a multi-tiered structure (e.g. in the form of random intersections of thin polymer fibers), whereas modern silicon microelectronic systems, due to a number of technological limitations, are two-dimensional. The transition to the third dimension would potentially offer many new opportunities.

The press release goes to explain what the researchers mean when they mention a fundamentally different computer,

The common classification of computers is based either on their casing (desktop/laptop/tablet), or on the type of operating system used (Windows/MacOS/Linux). However, this is only a very simple classification from a user perspective, whereas specialists normally use an entirely different approach – an approach that is based on the principle of organizing computer operations. The computers that we are used to, whether they be tablets, desktop computers, or even on-board computers on spacecraft, are all devices with von Neumann architecture; without going into too much detail, they are devices based on independent processors, random access memory (RAM), and read only memory (ROM).

The memory stores the code of a program that is to be executed. A program is a set of instructions that command certain operations to be performed with data. Data are also stored in the memory* and are retrieved from it (and also written to it) in accordance with the program; the program’s instructions are performed by the processor. There may be several processors, they can work in parallel, data can be stored in a variety of ways – but there is always a fundamental division between the processor and the memory. Even if the computer is integrated into one single chip, it will still have separate elements for processing information and separate units for storing data. At present, all modern microelectronic systems are based on this particular principle and this is partly the reason why most people are not even aware that there may be other types of computer systems – without processors and memory.

*) if physically different elements are used to store data and store a program, the computer is said to be built using Harvard architecture. This method is used in certain microcontrollers, and in small specialized computing devices. The chip that controls the function of a refrigerator, lift, or car engine (in all these cases a “conventional” computer would be redundant) is a microcontroller. However, neither Harvard, nor von Neumann architectures allow the processing and storage of information to be combined into a single element of a computer system.

However, such systems do exist. Furthermore, if you look at the brain itself as a computer system (this is purely hypothetical at the moment: it is not yet known whether the function of the brain is reducible to computations), then you will see that it is not at all built like a computer with von Neumann architecture. Neural networks do not have a specialized computer or separate memory cells. Information is stored and processed in each and every neuron, one element of the computer system, and the human brain has approximately 100 billion of these elements. In addition, almost all of them are able to work in parallel (simultaneously), which is why the brain is able to process information with great efficiency and at such high speed. Artificial neural networks that are currently implemented on von Neumann computers only emulate these processes: emulation, i.e. step by step imitation of functions inevitably leads to a decrease in speed and an increase in energy consumption. In many cases this is not so critical, but in certain cases it can be.

Devices that do not simply imitate the function of neural networks, but are fundamentally the same could be used for a variety of tasks. Most importantly, neural networks are capable of pattern recognition; they are used as a basis for recognising handwritten text for example, or signature verification. When a certain pattern needs to be recognised and classified, such as a sound, an image, or characteristic changes on a graph, neural networks are actively used and it is in these fields where gaining an advantage in terms of speed and energy consumption is critical. In a control system for an autonomous flying robot every milliwatt-hour and every millisecond counts, just in the same way that a real-time system to process data from a collider detector cannot take too long to “think” about highlighting particle tracks that may be of interest to scientists from among a large number of other recorded events.

Bravo to the writer!

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

Hardware elementary perceptron based on polyaniline memristive devices by V.A. Demin. V. V. Erokhin, A.V. Emelyanov, S. Battistoni, G. Baldi, S. Iannotta, P.K. Kashkarov, M.V. Kovalchuk. Organic Electronics Volume 25, October 2015, Pages 16–20 doi:10.1016/j.orgel.2015.06.015

This paper is behind a paywall.

Titanium dioxide nanoparticles and the brain

This research into titanium dioxide nanoparticles and possible effects on your brain should they pass the blood-brain barrier comes from the University of Nebraska-Lincoln (US) according to a Dec. 15, 2015 news item on Nanowerk (Note: A link has been removed),

Even moderate concentrations of a nanoparticle used to whiten certain foods, milk and toothpaste could potentially compromise the brain’s most numerous cells, according to a new study from the University of Nebraska-Lincoln (Nanoscale, “Mitochondrial dysfunction and loss of glutamate uptake in primary astrocytes exposed to titanium dioxide nanoparticles”).

A Dec. 14, 2015 University of Nebraska-Lincoln news release, which originated the news item, provides more detail (Note: Links have been removed),

The researchers examined how three types of titanium dioxide nanoparticles [rutile, anatase, and commercially available P25 TiO2 nanoparticles], the world’s second-most abundant nanomaterial, affected the functioning of astrocyte cells. Astrocytes help regulate the exchange of signal-carrying neurotransmitters in the brain while also supplying energy to the neurons that process those signals, among many other functions.

The team exposed rat-derived astrocyte cells to nanoparticle concentrations well below the extreme levels that have been shown to kill brain cells but are rarely encountered by humans. At the study’s highest concentration of 100 parts per million, or PPM, two of the nanoparticle types still killed nearly two-thirds of the astrocytes within a day. That mortality rate fell to between half and one-third of cells at 50 PPM, settling to about one-quarter at 25 PPM.

Yet the researchers found evidence that even surviving cells are severely impaired by exposure to titanium dioxide nanoparticles. Astrocytes normally take in and process a neurotransmitter called glutamate that plays wide-ranging roles in cognition, memory and learning, along with the formation, migration and maintenance of other cells.

When allowed to accumulate outside cells, however, glutamate becomes a potent toxin that kills neurons and may increase the risk of neurodegenerative diseases such as Alzheimer’s and Parkinson’s. The study reported that one of the nanoparticle types reduced the astrocytes’ uptake of glutamate by 31 percent at concentrations of just 25 PPM. Another type decreased that uptake by 45 percent at 50 PPM.

The team further discovered that the nanoparticles upset the intricate balance of protein dynamics occurring within astrocytes’ mitochondria, the cellular organelles that help regulate energy production and contribute to signaling among cells. Titanium dioxide exposure also led to other signs of mitochondrial distress, breaking apart a significant proportion of the mitochondrial network at 100 PPM.

“These events are oftentimes predecessors of cell death,” said Oleh Khalimonchuk, a UNL assistant professor of biochemistry who co-authored the study. “Usually, people are looking at those ultimate consequences, but what happens before matters just as much. Those little damages add up over time. Ultimately, they’re going to cause a major problem.”

Khalimonchuk and fellow author Srivatsan Kidambi, assistant professor of chemical and biomolecular engineering, cautioned that more research is needed to determine whether titanium dioxide nanoparticles can avoid digestion and cross the blood-brain barrier that blocks the passage of many substances. [emphasis mine]

However, the researchers cited previous studies that have discovered these nanoparticles in the brain tissue of animals with similar blood-brain barriers. [emphasis mine] The concentrations of nanoparticles found in those specimens served as a reference point for the levels examined in the new study.

“There’s evidence building up now that some of these particles can actually cross the (blood-brain) barrier,” Khalimonchuk said. “Few molecules seem to be able to do so, but it turns out that there are certain sites in the brain where you can get this exposure.”

Kidambi said the team hopes the study will help facilitate further research on the presence of nanoparticles in consumer and industrial products.

“We’re hoping that this study will get some discussion going, because these nanoparticles have not been regulated,” said Kidambi, who also holds a courtesy appointment with the University of Nebraska Medical Center. “If you think about anything white – milk, chewing gum, toothpaste, powdered sugar – all these have nanoparticles in them.

“We’ve found that some nanoparticles are safe and some are not, so we are not saying that all of them are bad. Our reasoning is that … we need to have a classification of ‘safe’ versus ‘not safe,’ along with concentration thresholds (for each type). It’s about figuring out how the different forms affect the biology of cells.

I notice the researchers are being careful about alarming anyone unduly while emphasizing the importance of this research. For anyone curious enough to read the paper, here’s a link to and a citation for it,

Mitochondrial dysfunction and loss of glutamate uptake in primary astrocytes exposed to titanium dioxide nanoparticles by Christina L. Wilson, Vaishaali Natarajan, Stephen L. Hayward, Oleh Khalimonchuk and   Srivatsan Kidambi. Nanoscale, 2015,7, 18477-18488 DOI: 10.1039/C5NR03646A First published online 31 Jul 2015

This is paper is open access although you may need to register on the site.

Final comment, I note this was published online way back in July 2015. Either the paper version of the journal was just published and that’s what’s being promoted or the media people thought they’d try to get some attention for this work by reissuing the publicity. Good on them! It’s hard work getting people to notice things when there is so much information floating around.

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).