Researchers from the Vienna University of Technology (VUT) have put a brain on a circuit board — specifically, the brain of the nematode C. elegans. They are now training it to perform tasks without a single line of human-written code.

C. elegans worms.
Image credits PROZEISS Microscopy / Flickr.

C. elegans isn’t much to look at. Growing to just under one millimeter in length, it’s not just tiny, it’s also a very, very simple organism. But in one respect, this little nematode is unique and uniquely valuable for science — it’s the only living being whose neural system has been fully analyzed and mapped. In other words, its brain can be recreated as a circuit — either onto a circuit board or one simulated with software — without losing any of its function.

This has allowed researchers at the VUT to ‘copy-paste’ its brain into a computer, creating a virtual copy of the organism that reacts to stimuli the same way as the real thing. The researchers are now hard at work training this digi-worm to perform simple tasks, and it has already mastered the standard computer science trial of balancing a pole.

Worm in the software

So are your brains at risk of spontaneous copyfication? No. Researchers have been able to map C. elegans‘ neural systems precisely because it’s quite dumb — it can only draw on 300 neurons worth of processing power. However, that’s enough gray matter to allow the worm to navigate its environment, catch bacteria for dinner, and react to certain external stimuli — such as a touch on its body, which triggers a reflexive squirming-away.

This behavior is encoded in the worm’s nerve cells, and governed by the strength of the connections between these neurons. When recreated on a computer, this simple reflex pathway works the same way as its biological counterpart — not because it’s been programmed to do so, but because this behavior arises from the structure itself.

“This reflexive response of such a neural circuit, is very similar to the reaction of a control agent balancing a pole,” says co-author Ramin Hasani.

Pole balancing is actually a typical control trial in computer science. It involves a pole, fixed on its lower end on a moving object, which the device has to keep in a vertical position. It does this by moving the object slightly whenever the pole starts tilting, in a bid to keep it from tipping over.

The worm’s natural behavior is very similar to that required in this test.
Image credits TU Wien.

Standard controllers don’t have much trouble passing this test. The trial is functionally similar to the processes the nematode’s neural system has to handle in the wild — move when a stimulus is registered. So, the team wanted to see if it could solve the problem without adding any extra code or neurons, just by tuning the strength of connections between cells. They chose this final parameter based on the fact that shifting synaptic strength is the characteristic feature of any natural learning process.

After some tweaking, the network managed to easily pass the pole trial.

“With the help of reinforcement learning, a method also known as ‘learning based on experiment and reward’, the artificial reflex network was trained and optimized on the computer,” explains first author Mathias Lechner.

“The result is a controller, which can solve a standard technology problem — stabilizing a pole, balanced on its tip. But no human being has written even one line of code for this controller, it just emerged by training a biological nerve system,” says co-author Radu Grosu.

After establishing that the method works, the team plans to probe the capabilities of similar circuits further. Still, the research does raise some very impactful questions — are machine learning and our brain processes fundamentally the same? If so, is silicon intelligence any less valuable or ‘alive’ than biological intelligences?

For now, however, we simply don’t know — C. elegans doesn’t know or care whether it lives as a worm in the ground or as a virtual collection of 1’s and 0’s on a computer in Vienna.

The paper “Worm-level Control through Search-based Reinforcement” has been published in the preprint server arXiv.