Scientists have recently designed an artificial flexible sensory nerve capable of encoding neurons, tactile sensing, and performing synaptic processing functions. Interestingly, this device does not depend on algorithms or computer resources. The study is available in Advanced Science.
Study: A flexible artificial sensory nerve activated by nanoparticle-mounted synaptic devices for neuromorphic tactile recognition. Image credit: Christoph Burgstedt / Shutterstock.com
Perception and tactile recognition in humans
In humans, tactile recognition and perception have been associated with the determination of the strength and dynamics of sensory stimuli, which are subjected to the skin by touch (active or passive). External stimuli or touch are perceived by sensory receptors, which are present in the skin, and are encoded as neural tips.
These peaks are managed by neurons and synapses, by various processes, such as amplification, adaptation, and memory, and are subsequently transmitted to the cerebral cortex. The cerebral cortex is involved with high-level functions such as identification, classification, and perceptual learning.
Due to tactile perception, humans can make delicate prey, distinguish between varied textures, and identify different objects based on their tactile characteristics and patterns.
Neuromorphic Electronics
Scientists believe that the incorporation of tactile functions into prostheses, robotics, and other interactive systems will substantially improve an individual’s cognitive abilities when encountering unstructured environments or maneuvering unidentified objects. Advances in neuromorphic electronics have greatly helped to design artificial tactile sensory systems.
The scientists claimed that an artificial afferent nerve, which includes organic synaptic transistors and pressure sensor arrays, is capable of distinguishing Braille characters. It is based on optoelectronic afferent nerve output (sensor signals) as optical tips that are processed by a software algorithm and detect touch and recognize handwriting.
Some techniques used to process tactile data obtained from the synaptic device are machine learning and deep learning algorithms. Therefore, this synaptic electronics requires additional computer processors and tactile reconnaissance is not performed in real time.
Nano-based neuromorphic tactile recognition device: a new study
In a new study, scientists developed a flexible artificial sensory nerve that can perform real-time neuromorphic tactile recognition. This touch sensor showed great sensitivity and a linear response for touch detection performance. A flexible synaptic transistor was associated with sensory processing based on neuronal sensitivity and sensory memory.
The scientists designed the synaptic device using a simple technique associated with nanoparticle self-assembly (NP) that can produce continuous, uniform NP films on arbitrary substrates. It is important to note that the tactile data was processed directly by the device without the need for external algorithms or computer resources. In addition, the synaptic device also did not require erasure of memory or state reset operation.
In this study, the synaptic device is an ion gel transistor composed of interdigitated electrodes, a chitosan-based electrolyte placed on a flexible polyimide substrate, and a self-assembled NP channel. The researchers stated that NP’s interfacial self-assembly technique is an efficient, low-cost manufacturing process. In this study, researchers used NP of colloidal amphiphilic zinc oxide.
The touch sensor was designed from the lamination of the pressure-sensitive layer, which consisted of carbon nanotubes (CNT) and vulcanized latex at room temperature (RTV) on a polyimide film coated with electrodes of or. The interdigitated electrodes were connected by a conductive detection layer.
The authors stated that the flexible artificial sensory nerve processed stimuli similar to signal transmission and had processing characteristics associated with the tactile biological sensory system. The authors of this study noted that its simplified design of the artificial sensory system has substantially elevated the neuromorphic sensing capabilities of the device. Device output, i.e. synaptic weight and number of tip pulses, are classified for real-time intelligent touch recognition during robotic manipulation and tactile interaction.
Hardness is one of the most important characteristics of an object that humans learn through touch. In this study, the tactile recognition capabilities of the new device were assessed for its ability to identify the hardness of a material during robotic slow capture and release it quickly.
The contact force between the object and the finger during a touch was initially assessed by the touch sensor followed by the synaptic device that was connected to the robotic fingers. The touch pattern was studied to assess the tactile interaction capability of the device.
The authors revealed that during the taking of porous material with low hardness, the postsynaptic current (PSC) presented inconsistencies. This may be due to the release of compressive pressure on the material.
Conclusion
This study provides a guideline for developing a neuromorphic sensory system that possesses human-like cognitive functions. Because the new synaptic device and touch sensor are flexible, they can be incorporated into a wide range of portable electronics and robotics.
One of the main advantages of this device is that the neuromorphic tactile recognition is obtained directly at the device level without depending on an algorithm or computer resource. In the future, the authors will focus on the miniaturization and optimization of the S coding circuit to develop flexible electronics with adjustable synaptic plasticity and long-term memory in the synaptic device.
Reference
Jiang, C. et al. (2022) A flexible artificial sensory nerve activated by nanoparticle-mounted synaptic devices for neuromorphic tactile recognition. Advanced science. https://doi.org/10.1002/advs.202106124
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