World-first artificial neurons create potential
World-first artificial neurons create potential for bionic medicine
Artificial neurons on silicon chips that behave just like the real thing have been invented by a team of international scientists, including University of Auckland’s Professor Julian Paton.
In a major breakthrough, the researchers have successfully reproduced the electrical properties of biological neurons onto semiconductor chips.
The first-of-its-kind achievement gives enormous scope for medical devices to alleviate medical conditions such a neuronal degeneration, spinal cord injury and paralysis, and heart failure.
Critically, the artificial neurons not only behave just like biological neurons but only need one billionth the power of a microprocessor, making them ideally suited for use in medical implants and other bio-electronic devices.
The research team, led by the University of Bath and including researchers from the Universities of Auckland, Bristol and Zurich, describe the artificial neurons in a study published in Nature Communications.
Professor Paton, in the Department of Physiology in the University of Auckland’s Faculty of Medical and Health Sciences, said: “Replicating the response of neurons in bioelectronics that can be miniaturised and implanted is very exciting and opens up enormous opportunities for smarter medical devices that drive towards personalised medicine approaches to a range of diseases and disabilities; we are truly approaching a bionic era in medicine.”
Designing artificial neurons that respond to electrical signals from the nervous system like real neurons has been a major goal in medicine for decades, as it opens up the possibility of curing conditions where neurons are not working properly, or have had their processes severed as in spinal cord injury, or have died. Artificial neurons could repair diseased bio-circuits by replicating their healthy function and responding adequately to biological feedback to restore bodily function.
Professor Alain Nogaret, a physicist from the University of Bath, led the project. He said: “Until now neurons have been like black boxes, but we have managed to open the black box and peer inside. Our work is paradigm changing because it provides a robust method to reproduce the electrical properties of real neurons in minute detail.
“But it’s wider than that, because our neurons only need 140 nanoWatts of power. That’s a billionth the power requirement of a microprocessor, which other attempts to make synthetic neurons have used. This makes the neurons well suited for bio-electronic implants to treat chronic diseases.”
The researchers successfully modelled and derived equations to explain how neurons respond to electrical stimuli from other nerves. This is complicated by the fact that neurones are inherently ‘non-linear’ – in other words if a signal becomes twice as strong it shouldn’t necessarily elicit twice as big a reaction – it might be thrice bigger or only half the size.
They then designed silicon chips that accurately modelled biological ion channels, before proving that their silicon neurons precisely mimicked real, living neurons responding to a range of stimulations.
The researchers accurately replicated the complete dynamics of hippocampal neurons, which are crucial for learning and memory, and respiratory neurons that are essential for breathing, under a wide range of stimuli.
Co-author Professor Paton explains: “Our approach combines several breakthroughs. We can very accurately estimate the precise parameters that control any neurons behaviour with high certainty. We have created physical models of the hardware and demonstrated its ability to successfully mimic the behaviour of real living neurons. Effectively, we can mimic different types and functions of a wide range of complex mammalian neurons.”
Professor Giacomo Indiveri, another co-author on the study, from the University of Zurich added: “This work opens new horizons for neuromorphic chip design thanks to its unique approach to identifying crucial analogue circuit parameters.”
The study was funded by a European Union Horizon 2020 Future Emerging Technologies Programme grant and a doctoral studentship funded by the Engineering and Physical Sciences Research Council (ESPRC).