The feel of a cat's fur can reveal some information, but seeing the feline provides critical details: is it a housecat or a lion? While the sound of fire crackling may be ambiguous, its scent confirms the burning wood. Our senses synergize to give a comprehensive understanding, particularly when individual signals are subtle. The collective sum of biological inputs can be greater than their individual contributions. Robots tend to follow more straightforward addition, but researchers have now harnessed the biological concept for application in artificial intelligence (AI) to develop the first artificial, multisensory integrated neuron.
The feel of a cat's fur can reveal some information, but seeing the feline provides critical details: is it a housecat or a lion? While the sound of fire crackling may be ambiguous, its scent confirms the burning wood. Our senses synergize to give a comprehensive understanding, particularly when individual signals are subtle. The collective sum of biological inputs can be greater than their individual contributions.
"Biology enables small organisms to thrive in environments with limited resources, minimizing energy consumption in the process," said Das, who is also affiliated with the Materials Research Institute."The requirements for different sensors are based on the context -- in a dark forest, you'd rely more on listening than seeing, but we don't make decisions based on just one sense.
The researchers fabricated the multisensory neuron by connecting a tactile sensor to a phototransistor based on a monolayer of molybdenum disulfide, a compound that exhibits unique electrical and optical characteristics useful for detecting light and supporting transistors. The sensor generates electrical spikes in a manner reminiscent of neurons processing information, allowing it to integrate both visual and tactile cues.
"Interestingly, this effect resonates remarkably well with its biological counterpart -- a visual memory naturally enhances the sensitivity to tactile stimulus," said co-first author Najam U Sakib, a third-year doctoral student in engineering science and mechanics."When cues are weak, you need to combine them to better understand the information, and that's what we saw in the results.
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