Artificial neurons on silicon chips that behave just like the real thing have been invented by scientists – a first-of-its-kind achievement with enormous scope for medical devices to cure chronic diseases, such as heart failure, Alzheimer’s, and other diseases of neuronal degeneration.
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 artificial neurons only need 140 nanoWatts of power.
They designed silicon chips that accurately modeled biological ion channels, before proving that their silicon neurons precisely mimicked real, living neurons responding to a range of stimulations.
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, 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.
In heart failure for example, neurons in the base of the brain do not respond properly to nervous system feedback, they in turn do not send the right signals to the heart, which then does not pump as hard as it should.
However developing artificial neurons has been an immense challenge because of the challenges of complex biology and hard-to-predict neuronal responses.
The researchers successfully modelled and derived equations to explain how neurons respond to electrical stimuli from other nerves. This is incredibly complicated as responses are ‘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 something else.
Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback.