Bridging the gap: artificial to biological neuron interfacing

Is non-binary the way forward for BCI (brain computer interfacing)? Chemical computing could be a powerful tool in combating aging.

Dopamine is known for playing a role in how pleasure is felt and how humans think and plan; it is a type of neurotransmitter, made by the body and used as a chemical transmitter by the nervous system to conduct messages between nerve cells.

Longevity.Technology: The brain is a chemical computer as well as an electrical one. Chemical computing is flexible and powerful, and could pave the way for repairing age-related degeneration and improving cognitive function.

Exploiting dopamine’s ability to change how neural circuits behave, a team at Stanford University has been able to induce an artificial neuron to communicate directly with a biological one.

Hybrid circuits of man and machine have had a string of successes of late, from using electrical fields to control genes through to global communication using the internet.

However, electricity generates heat and requires some degree of invasion; moving away from neuromorphic engineering mitigates these risks and this announcement is interesting in that it removes the need for hard-wiring – a big challenge in Neurotech.

The Stanford team started with a dopamine-releasing biological cell and an artificial neuron constructed from biocompatible and electrical-conducting materials. When the biological cell was activated, its dopamine flooded out and reacted chemically with an electrode made from biological polymers on the artificial neuron.

Dr Alberto Salleo, Professor of Materials Science and Engineering at Stanford University, said: “It’s a demonstration that this communication melding chemistry and electricity is possible. You could say it’s a first step toward a brain-machine interface, but it’s a tiny, tiny very first step [1].”

It’s not cyborg time yet, however; there is much work still to be done, but the successful use of chemical signals to activate man-made neurons is a step towards full-capability artificial-biological hybrid circuits.

Working just like dopamine docking onto an actual biological neuron, a current was generated that passed through the conductive solution channel to a second electrode, with the result that the second electrode’s conductance – its ability to pass electrical information – was altered.

This process is similar to what happens in our brains during learning, as docked dopamine ‘shuttles’ alter the likelihood of a biological neuron’s firing in the future. The experiment had successfully altered the artificial neuron’s conductance in a way that mimicked learning.

Study author Scott Keene said: “That’s when we realized the potential this has for emulating the long-term learning process of a synapse [2].”

Dopamine has another trick up its sleeve, however; it only docks with a downstream neuron for a short while, before returning to base at the upstream neuron, where it is either reused or broken down into its component molecules. This means the neural circuit has a period of ‘down time’ where it can readjust its activity. During sleep-periods perhaps?

The Stanford team were able to reproduce this in their hybrid circuit; they created a microfluidic channel that could move both dopamine and its byproducts away from the artificial neurons after the signalling job was complete, meaning they could be recycled. Examination under the microscope showed the hybrid synapse could effectively recycle dopamine on a timescale similar to the brain [3].

Adjusting dopamine levels meant the team could mimic, albeit in a loose way, a learning rule called spike learning, an algorithm of deep learning based on gradient descent for spiking neural networks.

The study concluded: “The neurotransmitter-mediated neuromorphic device presented in this work constitutes a fundamental building block for artificial neural networks that can be directly modulated based on biological feedback from live neurons … [it] is a crucial first step in realizing next-generation adaptive biohybrid interfaces [4].”

[4] Ibid