Replicating the complex behaviour of neurons has been a major goal in medicine for decades and a team of researchers has reportedly done just that.
The research team led by the University of Bath and including researchers from the Universities of Bristol, Zurich and Auckland, has designed artificial neurons that respond to electrical signals from the nervous system.
This sounds simple but it isn’t.
Neuron responses can be very hard to predict as these responses are often non-linear. This means that if a signal is twice as strong, the response may not be twice as big.
To address this the researchers modelled and derived equations that could explain how neurons respond to stimuli from the nervous system.
The team then designed silicon chips modelled on biological ion channels which proved the artificial neurons mimicked real neurons in simulations.
To test this the researchers replicated the dynamics of hippocampal and respiratory neurons in rats.
“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, project lead, Professor Alain Nogaret from the University of Bath’s Department of Physics, said in a statement.
These artificial neurons could be used to repair diseased bio-circuits in the body and return functionality to the body. This includes Alzheimer’s and other neuron degenerative diseases.
The silicon chips require just 140 nanoWatts of power meaning they are well-suited for implants to treat the aforementioned diseases.
“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. Our third breakthrough is the versatility of our model which allows for the inclusion of different types and functions of a range of complex mammalian neurons,” adds Nogaret.
The researchers published their findings in Nature Communications under the title “Optimal solid state neurons”. The study was funded by the European Union Horizon 2020 Future Emerging Technologies Programme grant and a doctoral studentship funded by the Engineering and Physical Sciences Research Council.