Chinese scientists find neurons may act as minicomputers for flexible decisions
Mouse study finds dendrites combine learned rules with sensory input, suggesting individual neurons may support flexible decisions like minicomputers.
A study of mice has produced what researchers describe as the first experimental evidence that dendrites can integrate learned rules with incoming sensory inputs to drive flexible choices.
The finding sheds light on how neural activity may help the brain adapt its responses to different sensory experiences.
Scientists do not yet have a complete understanding of how the brain adapts to changing contexts and responds appropriately to sensory information.
Against that background, the researchers examined the role of dendrites, which are branchlike structures attached to neurons.
Their findings suggest that these structures can bring together information already learned and new sensory inputs.
That integration can guide flexible choices, according to the experimental evidence obtained from the mice.
The results also support the possibility that individual neurons may act as powerful minicomputers.
Such activity could enable flexible behaviour, including making decisions, the researchers found.
The work focuses on how neural activity guides the brain’s adjustment to different sensory experiences.
The researchers said the new insights into the cognitive process could also contribute to designing more adaptable and efficient artificial intelligence systems.
Those systems could be modelled on the human brain, they said.
Source: South China Morning Post
Frequently asked questions
What did Chinese scientists find about neurons?
They found experimental evidence that dendrites can integrate learned rules with incoming sensory inputs to support flexible choices.
What are dendrites?
Dendrites are branchlike structures attached to neurons.
How might individual neurons act as minicomputers?
By bringing together learned information and new sensory inputs, neural activity may enable flexible behaviour and decision-making.
How could the findings influence artificial intelligence?
The insights could contribute to designing more adaptable and efficient AI systems modelled on the human brain.
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