Effective disposable chips made of organic materials

This article is part of a ditych about so-called neuromorphic computers, which are similar in design to our own brains. These types of computers are theoretically powerful, energy efficient, and can even communicate directly with our bodies. This article looks at soft, organic materials you can use to build a brain-on-a-chip. NEMO Kennislink editor Roel van der Heijden delved into the world of ‘intelligent matter’ and describes how chips based on classic computer materials such as silicon and gold can also function very differently and what new possibilities this provides.

To calculate and process information in the way our brain does – it would mean a huge revolution for everything we now use classical computers for. This so-called neuromorphic computing is much more efficient and therefore much more energy efficient. And it makes completely new uses possible, as can be read in part one of this ditych.

One with your body

But there are many more possibilities if you try to get closer to the biological example. Because if you can make ‘computers’ that really work like our brains, you can also communicate much more directly with biological materials, with living cells and even with our nervous system. This would enable prostheses that not only understand signals from our nerves, but also provide feedback the other way around. This means that our brain can train and control the prosthesis better, so that the attachment becomes much more one with the body.

You can also think of combinations of sensors that you place in or on the body, and which continuously measure things such as pulse, blood sugar level or oxygen level, and can immediately deduce from this whether, for example, a medicine needs to be dispensed. . And because such a ‘smart patch’ does all the calculations itself, there is no need for an exchange with a central computer elsewhere. It works completely independently. It makes a huge difference: no wires, no batteries and no need to be connected to a network.

But if you’re serious about this kind of brain imitation, you have to go “biological”. As does Yoeri van de Burgt, assistant professor at Eindhoven University of Technology. He also works with neuromorphic chips, but from a very different starting point to the chips of Wilbert van der Wiel and colleagues, which are very innovative, but also very conventional in the materials they use. Van de Burgt chooses soft, organic materials and hopes in the long term to make cheap, easy-to-train and easily-printable chips – almost in the direction of disposable chips, and then you don’t have to work with silicon and gold.

Test setup of a neuromorphic chip in Yoeri van de Burgt’s laboratory. PhD candidate Eveline van Doremaele measures how incoming signals are converted into outgoing signals.

Esther Thole for NEMO Knowledgelink

Carbon as a base

The living world does not run on gold or silicon, but on organic materials consisting of carbon, hydrogen, oxygen and nitrogen. That’s why Van de Burgt uses these building blocks to build his brain-inspired chips. These are so-called conducting organic polymers; they are long molecules that can pass an electric current and adjust their resistance as soon as negatively (electrons) or positively (ions) charged particles enter or leave the material. “These polymers are neutral. If a positively charged ion enters the material, it has to be compensated for. In response to this, the polymer pushes out a negative charge, an electron, which creates a matched resistance,” explains Van de Burgt. “You can see the adjusted resistance as a ‘memory’, the material remembers that change.”

Much communication in our body and brain takes place via ions. Materials like gold and silicon can do nothing with such a signal, but organic polymers can. This makes them very suitable for not only mimicking the function of brain cells, but also for communicating directly with living cells. Van de Burgt and his group demonstrated this by connecting a tiny electrical circuit, with a layer of a conductive polymer on top, to living brain cells. These cells secrete the chemical dopamine, which is an important signal in our brain. “The released dopamine reacts with oxygen, and an electron from the polymer layer is also used in that reaction. The polymer then adjusts its resistance to remain neutral, resulting in a change in conductivity. You can measure it as a changing current flowing through the circuit.” For example, the team showed that you can control an artificial electronic system with a natural signal from living cells.

Stronger synapse

Not only are the materials and signals used different from the ‘hard’ neuromorphic chips, the processing of the signals into information is also fundamentally different. The chips that Van de Burgt makes do not require an external processor for processing: everything happens immediately in the hardware or organic material. The material is actually already ‘programmed’ to respond to signals in a certain way. “What we do, you want in-memory computing can name. We create something similar to a neural network: a chain of interconnected nodes, where each connection is assigned a certain ‘weight’ that indicates how important that connection is to the end result. It is very similar to how things work in the brain. Neurons are connected via a so-called synapse (a connection), and the more often two neurons communicate with each other, the stronger that synapse becomes. This way you can also see the connections in our network: the more important a connection is to the end result, the more weight it gets.”

Detailed image of PhD student Eveline van Doremaele’s measurement setup. The needles pierce a layer of the organic, conductive polymers. An incoming electrical signal passes through the layer to another part of the chip, where the outgoing signal is measured via another needle.

Roel van der Heijden for NEMO Kennislink

The big advantage, so far in theory, is that such an in-memory system can be trained much more efficiently. “It is a network of resistors that you adjust at the same time. It is very effective.” Thanks to the organic polymers, programming connections in such a network are very predictable, says Van de Burgt. “An ion in it is an electron out. It doesn’t change.” A disadvantage of the organic polymers is that they not only react to the desired signal, but also react with, for example, oxygen from the air. “Such parasitic reactions are inevitable; organic materials react very easily. As a result, the memory of our network does not last ad infinitum: over time things are “forgotten”.

Nevertheless, Van de Burgt sees important opportunities for these organic chips. “Because of the high predictability, these chips can work very autonomously. You know exactly what they’re doing, and you don’t need an external computer to check if what you wanted to program actually worked. And you can train such network over and over again, which is not possible with a silicon chip.” Other advantages, he says, are that these systems use very little energy, that they are soft materials, and that they are biocompatible: they can directly ‘talk’ to biological materials, that they are easy to make and print, and also very cheap compared to the materials in the classic chips is of course also a plus.

Train again

The big question, of course, is what you can use these organic chips for in the long run. They don’t seem to be a serious competitor to chips from giants like IBM and Intel. Van de Burgt doesn’t have that in mind either. “We see lab-on-a-chip applications (miniature versions of laboratory tests, ed.) as a good niche, for example for diagnostics. Such a chip can classify signals to obtain information about a possible disease – such as recognizing cancer cells in a blood sample. The advantage of our chips would be that you can always process new insights into the chip, because you can train it again on the basis of new data.”

Schematic representation (not to scale) of the communication between a nerve cell that receives biological signals and releases dopamine in response. The dopamine then reacts and is converted into an electrical signal by the neuromorphic material.

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According to Van de Burgt, another important application lies in direct communication with our body. Think of sensors that you stick to the skin and that continuously register and process signals from your body and, on the basis of that, dispense medicine. Or implants, such as pacemakers or electrodes in the brain, which are then no longer made of rigid materials that are foreign to the body, but of materials that fit much better into our body. “Because our chips use so little energy and all information processing takes place directly in the chip, you don’t need a connection to external devices or a power supply.”

Finally, Van de Burgt sees opportunities to allow prostheses to communicate better and more directly with the brain by connecting the nerve endings to an ‘interface’ of conductive polymers. “If you make this intermediate layer adaptive, so that the material adapts under the influence of signals from the brain, the patient can train the prosthesis much more directly. It is much faster than if the prosthesis first has to send the signals to an external computer, which processes everything and returns a corrected signal. I think we can offer many advantages here with our materials.”

Views

The possibilities are there, but the practical applications are not yet in sight, acknowledges Van de Burgt. “The big challenge for us is how we can train such a network properly and efficiently by adjusting the weights of all connections at once. If we understand that principle, we can really take a step.”

Sources:

  • Y. van de Burgt, A. Melianas, ST Keene, G. Malliaras, A. Salleo, Organic electronics for neuromorphic computing, Nature Electronics (2018), doi:10.1038/s41928-018-0103-3
  • S. Keene, Y. van de Burgt, Lowering the threshold for bioelectronics, Nature Materials (2020), doi:10.1038/s41563-020-0623-x
  • ST Keene, C. Lubrano, S. Kazemzadeh, et al., A biohybrid synapse with neurotransmitter-mediated plasticity, Nature Materials (2020), doi:10.1038/s41563-020-0703-y
  • E. van Doremaele, P. Gkoupidenis, Y. van de Burgt, Towards Organic Neuromorphic Devices for Adaptive Sensing and New Computing Paradigms in Bioelectronics, J. Materials Chemistry C (2019), doi:10.1039/C9TC03247A/
  • College of Yoeri van de Burgt for the University of the Netherlands, How to print a brain on a computer chip?

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