The human brain presents a powerful, low-energy computing model, made up billions of networked neurons connected by synapses which provide the memory elements. Synaptic connections can become stronger or weaker, via potentiation and depression, through a process called synaptic plasticity as the brain encodes learning and memory. It is this synaptic plasticity that lays the foundation for neuromorphic computing, where natural neural architectures are biomimicked by artificial intelligence. One desirable application is soft-body neurobots, engineered to operate in the body autonomously, even integrating with neural systems. However, neurologic functions have never been achieved in these robotic systems. Now, a study from researchers at the University of Houston develops a robotic device containing a stretchable synaptic device enabling its neurological function to sense and interact with its environment. The team states this is a significant step toward the development of prosthetics that could directly connect with the peripheral nerves in biological tissues, offering neurological function to artificial limbs, as well as autonomous soft neurobots. The opensource study is published in the journal of Science Advances.
Previous studies show advances in materials and electronic technologies have led to the recent development of artificial synaptic devices, mostly in rigid or flexible formats, to biomimic low-energy, high speed biological neural systems to innovate parallel processing, low-power computing, and neuroprosthetics. Artificial synapses, which are very stretchable are key to enabling neurological functions in soft autonomous robotics for use in the body, however, only a handful of studies have reported stretchable synaptic devices. The current study engineers a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics.
The current study engineers a stretchable synaptic transistor and its neurologically integrated devices from rubbery materials. Results show the transistor exhibits functions similar to those of biological synapses, including excitatory postsynaptic potential, current, facilitation, and short-term memory and long-term memory. Data findings show the soft neurobots’ neurologically integrated tactile sensory skin allows it to sense and react to its external environment.
The group states their soft adaptive neurorobot is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. They go on to add their autonomous neurobot has implications for neuroprosthetics, as well as neuromorphic computing, an emerging technology with the potential to allow high volume information processing using small amounts of energy through devices biomimicking the electric behavior of neural networks.
The team surmises they have developed a soft neurobot with a neurologically integrated tactile sensory skin, allowing it to sense and interact with its environment. For the future, the researchers state their rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications.
Source: University of Houston
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Michelle is a health industry veteran who taught and worked in the field before training as a science journalist.
Featured by numerous prestigious brands and publishers, she specializes in clinical trial innovation--expertise she gained while working in multiple positions within the private sector, the NHS, and Oxford University.