Tens of thousands of living brain cells have been used to build a simple computer that can recognise patterns of light and electricity. It could eventually be used in robotics.
A computer built using tens of thousands of living brain cells can recognise simple patterns of light and electricity. It could eventually be incorporated into a robot that also uses living muscle tissues.
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| The neuron-based computer Andrew Dou, University of Illinois Urbana-Champaign |
Artificially intelligent algorithms inspired by the brain called neural networks have been used for everything from chatbots to searching for new laws of physics. Normally, these algorithms run on conventional computers, but Andrew Dou at the University of Illinois Urbana-Champaign and his colleagues wondered whether they could instead use actual living brain cells – neurons – as part of the set-up.
The team began by growing around 80,000 neurons derived from reprogrammed mouse stem cells in a dish. The process was similar to that used for creating brain organoids, also known as mini-brains, which are clumps of neurons that have been used as simple information processors, as well as for studying intelligence itself. The main difference is that the neurons in the new device were arranged in a flat, two-dimensional layer.
To complete the computer, the researchers placed the neurons below an optical fibre and onto a grid of electrodes so that the neurons could be stimulated with both electricity and light. The electrodes could also detect when the neurons produced their own electrical signals in response. All of this was housed in a palm-sized box, which, in turn, was placed in an incubator to keep the cells alive.
Conventional neural networks can easily learn how to distinguish different patterns of signals, so the researchers attempted to train the living computer to do the same.
They first created 10 distinct patterns of electrical impulses and light flashes. To train the computer to recognise them, they played the 10 different patterns repeatedly over the course of an hour and used a regular computer chip to record and process the electrical signals that the neurons produced in response.
The neurons produced the same signals each time the same pattern was presented. The chip, which was running an artificial neural network, just had to learn to distinguish those signals.
Often artificial neural networks can take a long time and many iterations to train, but the division of labour between the neurons and the chip, a method called reservoir computing, allowed the researchers to minimise this. Overall, the whole procedure took less time and energy this way, says Dou.
After the hour of training was up, the researchers let the neurons rest for 30 minutes, then exposed them to each of the 10 sequences of light and electricity again.
To evaluate how well the device did, they calculated a performance score called F1 that is commonly used for neural networks, where 0 is the worst possible score and 1 indicates perfect pattern recognition. The device’s best score was 0.98.
Dou says that in early experiments it could not score above 0.6 because the neurons would sometimes produce electricity unexpectedly, due to naturally occurring random processes. To improve it, he and the team worked out a combination of chemicals and additional electric impulses to suppress the randomness.
The new device is an early step in the researchers’ long-term goal to develop living computers and robots. In the past, they have made robots that use muscle tissues to move, and other researchers have used living brain cells to make robots process information about and navigate through their environment.
These experiments used a computer to relay signasl from a robot to the brain cells, which were not in direct contact. However, incorporating neurons into a robot would mean that the neurons could sense their environment, then process those inputs at once, with less mediation, says Nicolas Rouleau at Wilfrid Laurier University in Canada.
“Brain cells sort of micromanage themselves. They connect to each other on their own, and then you can give them information in many ways,” he says. Neurons respond to pressure, chemicals and magnetic fields in addition to light and electricity, so they could take in lots of information about a robot’s environment all in one go, he says.
Using living cells for computing, and reservoir computing in particular, makes for energy-efficient devices that can also keep working even if some of their smaller parts get damaged or experience failure, says Ilya Shmulevich at the Institute for Systems Biology in Seattle. Consequently, a robot that mixes living neurons and reservoir computing could have advantages over more conventional, purely mechanical machines, he says.
At the moment, the device cannot compete with conventional neural networks, but Dou says that the team is going to make it larger and more complex in the hope that some unexpected behaviour, or behaviour that they didn’t input into or train the network for, will emerge as more and more cells interact with each other.
Dou presented the work at the American Physical Society’s March Meeting in Las Vegas, Nevada.