World’s first ‘thinking’ robot with neurons grown in a lab from living brain cells is taught to steer its way around walls and obstacles
- Researchers at University of Tokyo grow artificial neurons from living brain cells
- These were connected to a compact robot on wheels, about the size of a hand
- Artificial neurons were electrically stimulated so the bot could navigate a maze
Japanese researchers have built a robot with brain-like neurons that were grown in the lab, in order to teach it to ‘think like us’.
In experiments at the University of Tokyo, the compact robotic vehicle on wheels, small enough to fit in a person’s palm, was placed in a simple maze.
The robot was connected to a culture of brain neurons, also known as nerve cells, that were grown from living cells.
When these artificial neurons were electrically stimulated, the machine successfully reached its goal – a black circular box.
NEURONS: SPECIAL CELLS THAT TRANSMIT NERVE
A neuron, also known as nerve cell, is an electrically excitable cell that takes up, processes and transmits information through electrical and chemical signals.
About a tenth the diameter of a human hair, it is one of the basic elements of the nervous system. They transport stimuli so a human can react to his or her environment.
The stimulation, for example the burning of the finger at a candle flame, is transported by the ascending neurons to the central nervous system and in return, the descending neurons stimulate the arm in order to remove the finger from the candle.
And whenever the robot veered in the wrong direction or faced the wrong way, the neurons in the cell culture were disturbed by an electric impulse to put it back on track.
The experiments, detailed in a new paper published in Applied Physics Letters, mark a big step forward in the bid to teach intelligence to robots, according to researchers.
It marks the first time intelligence has been ‘taught’ to a robot using lab-grown neurons grown from living cells.
‘We developed a closed-loop system to generate a coherent signal from a spontaneously active living neuronal culture and embodied the culture with a mobile vehicle robot,’ the authors say in their paper.
‘When the robot collided with obstacles or when its goal was not within 90 degrees in front of it, an electrical stimulation from an electrode was applied to the culture.
‘The robot could successfully reach its goal in four different fields.’
The artificial neurons grown from living cells acted as ‘the physical reservoir’ for the robot to make decisions.
During the trial, the robot was fed homeostatic signals to effectively tell it that everything was going to plan and it was making progress towards the goal.
A robot was placed on a flat surface with obstacles and was directed toward the goal (bottom right). It steered its way around walls and obstacles – using neurons grown in the lab. If the vehicle veered in the wrong direction or faced the wrong way, neurons in the cell culture were disturbed by an electric impulse
LAB-GROWN ‘MINI BRAINS’ COULD HELP TREAT MND
University of Cambridge researchers have developed ‘mini brains’ that allow them to study a fatal and untreatable neurological disorder causing paralysis and dementia.
Amyotrophic lateral sclerosis (ALS), a common form of motor neurone disease (MND), can affect younger people occurring mostly after the age of 40-45.
The mini brains help scientists see how the condition develops long before any symptoms begin to emerge, and in turn screen for potential drugs.
Read more: Lab-grown ‘mini brains’ developed from MND patients
However, if the bot encountered an obstacle, this homeostasis was disrupted with disturbance signals, making the robot awkwardly judder and recalibrate.
Throughout trials, the robot was continually fed the homeostatic signals interrupted by the disturbance signals until it had successfully solved the maze task.
The robot could not see the environment or obtain other sensory information, so it was entirely dependent on the electrical trial-and-error impulses.
The researchers have showed intelligent task-solving abilities can be produced by ‘physical reservoir computers’ – a physical body (not necessarily a human) that performs computations based on brain signals
‘I was inspired by our experiments to hypothesise that intelligence in a living system emerges from a mechanism extracting a coherent output from a disorganised state, or a chaotic state,’ said study author Hirokazu Takahashi, an associate professor of mechano-informatics at the University of Tokyo.
Advances in physical reservoir computing could contribute to creating artificial intelligence machines that think like us.
‘A brain of [an] elementary school kid is unable to solve mathematical problems in a college admission exam, possibly because the dynamics of the brain or their “physical reservoir computer” is not rich enough,’ said Professor Takahashi.
A neuron, also known as nerve cell, is an electrically excitable cell that takes up, processes and transmits information through electrical and chemical signals (pictured, an artist’s rendering of neurons in the head)
‘Task-solving ability is determined by how rich a repertoire of spatiotemporal patterns the network can generate.’
The team believes using physical reservoir computing in this context will contribute to a better understanding of the brain’s mechanisms and may lead to the development of a neuromorphic computer.
A neuromorphic computer can mimic the neuro-biological architectures present in the nervous system of a human.
THE POTENTIAL OF NEUROMORPHIC COMPUTING
Neuromorphic computing draws inspiration from our current understanding of the brain’s architecture and how it makes decisions.
The brain’s neural networks relay information with pulses or spikes, strengthening frequent connections and storing the changes locally at synapse interconnections.
Intelligent behaviours emerge from cooperation and competition between multiple regions within the brain’s neural networks and its environment.
This means brains cells don’t function in isolation, as opposed to the individual logic gates of processors which can make millions of computations but all in isolation.
Developments in machine learning over recent years have largely been thanks to improvements in raw power to process binary ones and zeroes.
A Neuromorphic computing design from US technology firm Intel, called Loihi, turns to an older and as yet unproven technology, which functions more like the synapses of the brain in weighting information based on its value.
The firm believes this ‘neuromorphic’ approach could allow computer systems of the future to autonomously solve problems using learned experiences.