In a first-of-its-kind achievement, researchers have developed artificial neurons on silicon chips that behave like biological neurons. Not only do the artificial neurons behave similarly, but they also only need one billionth of the power of a microprocessor, which makes them ideal candidates for use in medical implants.
Developing these artificial neurons has been a major goal in medicine, as it opens the possibility of curing conditions where neurons are not working properly. However, their development has been immensely challenging due to the complex biology and hard-to-predict nature of neuronal responses.
Within this study, the researchers were able to successfully model and derive equations to explain how neurons respond to electrical stimuli from other nerves. 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.
According to the team, they were also able to accurately replicate the complete dynamics of hippocampal neurons and respiratory neurons from rats, under a wide range of stimuli.
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“Our work is paradigm changing because it provides a robust method to reproduce the electrical properties of real neurons in minute detail,” commented Alain Nogaret (University of Bath). “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.”
As an example, the team noted that they are developing smart pacemakers that not only stimulate the heart to pump at a steady rate, but also, they are using these artificial neurons to respond in real time to demands placed on the heart. Other potential applications that were mentioned included treatment of conditions such as Alzheimer’s and other neuronal degenerative diseases.
Speaking about their findings, Nogaret mentioned how this study combines several breakthroughs in the field. First, he noted how their approach very accurately estimates the precise parameters that control any neurons behavior with high certainty. Second, the team have created physical models of the hardware and have demonstrate its ability to successfully mimic the behavior of real-living neurons. The last breakthrough relates to the versatility of their model, which Nogaret stated allows for the inclusion of different types and functions of a range of complex mammalian neurons.
Giacomo Indiveri (University of Zurich, Switzerland), co-author of the study, concluded: “This work opens new horizons for neuromorphic chip design thanks to its unique approach to identifying crucial analog circuit parameters.”
Sources: Abu-Hassan K, Taylor JD, Morris PG et al. Optimal solid state neurons. Nat. Comm. 10, 5309 (2019); www.bristol.ac.uk/news/2019/december/artificial-neurons.html