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      Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems

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          Abstract

          Fully rubbery synaptic transistors and neurologically integrated soft engineering systems are developed.

          Abstract

          Artificial synaptic devices that can be stretched similar to those appearing in soft-bodied animals, such as earthworms, could be seamlessly integrated onto soft machines toward enabled neurological functions. Here, we report a stretchable synaptic transistor fully based on elastomeric electronic materials, which exhibits a full set of synaptic characteristics. These characteristics retained even the rubbery synapse that is stretched by 50%. By implementing stretchable synaptic transistor with mechanoreceptor in an array format, we developed a deformable sensory skin, where the mechanoreceptors interface the external stimulations and generate presynaptic pulses and then the synaptic transistors render postsynaptic potentials. Furthermore, we demonstrated a soft adaptive neurorobot that is able to perform adaptive locomotion based on robotic memory in a programmable manner upon physically tapping the skin. Our rubbery synaptic transistor and neurologically integrated devices pave the way toward enabled neurological functions in soft machines and other applications.

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          Most cited references44

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          Design, fabrication and control of soft robots.

          Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.
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            From sensation to cognition.

            M. Mesulam (1998)
            Sensory information undergoes extensive associative elaboration and attentional modulation as it becomes incorporated into the texture of cognition. This process occurs along a core synaptic hierarchy which includes the primary sensory, upstream unimodal, downstream unimodal, heteromodal, paralimbic and limbic zones of the cerebral cortex. Connections from one zone to another are reciprocal and allow higher synaptic levels to exert a feedback (top-down) influence upon earlier levels of processing. Each cortical area provides a nexus for the convergence of afferents and divergence of efferents. The resultant synaptic organization supports parallel as well as serial processing, and allows each sensory event to initiate multiple cognitive and behavioural outcomes. Upstream sectors of unimodal association areas encode basic features of sensation such as colour, motion, form and pitch. More complex contents of sensory experience such as objects, faces, word-forms, spatial locations and sound sequences become encoded within downstream sectors of unimodal areas by groups of coarsely tuned neurons. The highest synaptic levels of sensory-fugal processing are occupied by heteromodal, paralimbic and limbic cortices, collectively known as transmodal areas. The unique role of these areas is to bind multiple unimodal and other transmodal areas into distributed but integrated multimodal representations. Transmodal areas in the midtemporal cortex, Wernicke's area, the hippocampal-entorhinal complex and the posterior parietal cortex provide critical gateways for transforming perception into recognition, word-forms into meaning, scenes and events into experiences, and spatial locations into targets for exploration. All cognitive processes arise from analogous associative transformations of similar sets of sensory inputs. The differences in the resultant cognitive operation are determined by the anatomical and physiological properties of the transmodal node that acts as the critical gateway for the dominant transformation. Interconnected sets of transmodal nodes provide anatomical and computational epicentres for large-scale neurocognitive networks. In keeping with the principles of selectively distributed processing, each epicentre of a large-scale network displays a relative specialization for a specific behavioural component of its principal neurospychological domain. The destruction of transmodal epicentres causes global impairments such as multimodal anomia, neglect and amnesia, whereas their selective disconnection from relevant unimodal areas elicits modality-specific impairments such as prosopagnosia, pure word blindness and category-specific anomias. The human brain contains at least five anatomically distinct networks. The network for spatial awareness is based on transmodal epicentres in the posterior parietal cortex and the frontal eye fields; the language network on epicentres in Wernicke's and Broca's areas; the explicit memory/emotion network on epicentres in the hippocampal-entorhinal complex and the amygdala; the face-object recognition network on epicentres in the midtemporal and temporopolar cortices; and the working memory-executive function network on epicentres in the lateral prefrontal cortex and perhaps the posterior parietal cortex. Individual sensory modalities give rise to streams of processing directed to transmodal nodes belonging to each of these networks. The fidelity of sensory channels is actively protected through approximately four synaptic levels of sensory-fugal processing. The modality-specific cortices at these four synaptic levels encode the most veridical representations of experience. Attentional, motivational and emotional modulations, including those related to working memory, novelty-seeking and mental imagery, become increasingly more pronounced within downstream components of unimodal areas, where they help to create a highly edited subjective version of the world. (ABSTRACT TRUNCATED)
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              Short-term plasticity and long-term potentiation mimicked in single inorganic synapses.

              Memory is believed to occur in the human brain as a result of two types of synaptic plasticity: short-term plasticity (STP) and long-term potentiation (LTP; refs 1-4). In neuromorphic engineering, emulation of known neural behaviour has proven to be difficult to implement in software because of the highly complex interconnected nature of thought processes. Here we report the discovery of a Ag(2)S inorganic synapse, which emulates the synaptic functions of both STP and LTP characteristics through the use of input pulse repetition time. The structure known as an atomic switch, operating at critical voltages, stores information as STP with a spontaneous decay of conductance level in response to intermittent input stimuli, whereas frequent stimulation results in a transition to LTP. The Ag(2)S inorganic synapse has interesting characteristics with analogies to an individual biological synapse, and achieves dynamic memorization in a single device without the need of external preprogramming. A psychological model related to the process of memorizing and forgetting is also demonstrated using the inorganic synapses. Our Ag(2)S element indicates a breakthrough in mimicking synaptic behaviour essential for the further creation of artificial neural systems that emulate characteristics of human memory.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                October 2019
                11 October 2019
                : 5
                : 10
                : eaax4961
                Affiliations
                [1 ]Materials Science and Engineering Program, University of Houston, Houston, TX 77204, USA.
                [2 ]Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
                [3 ]Department of Mechanical Engineering, University of Houston, Houston, TX 77204, USA.
                [4 ]School of Mechanical and Aerospace Engineering, Gyeongsang National University, 501, Jinju-daero, Jinju, Gyeongnam 52828, Korea.
                [5 ]Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
                [6 ]State Key Laboratory of Mechanical System and Vibration, Robotics Institute, and School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
                [7 ]Key Laboratory for Organic Electronics and Information Displays (KLOEID), Institute of Advanced Materials (IAM), and School of Materials Science and Engineering, Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210046, China.
                [8 ]National Laboratory of Solid State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
                [9 ]Department of Electrical and Computer Engineering, Texas Center for Superconductivity, University of Houston, Houston, TX 77204, USA.
                Author notes
                [* ]Corresponding author. Email: cyu15@ 123456uh.edu
                Author information
                http://orcid.org/0000-0003-2225-0978
                http://orcid.org/0000-0001-5284-6831
                http://orcid.org/0000-0002-1036-169X
                http://orcid.org/0000-0002-3443-3184
                http://orcid.org/0000-0003-1394-8581
                http://orcid.org/0000-0002-7778-4523
                http://orcid.org/0000-0001-6362-5487
                http://orcid.org/0000-0002-3975-1667
                http://orcid.org/0000-0002-8943-0861
                http://orcid.org/0000-0002-0155-0368
                Article
                aax4961
                10.1126/sciadv.aax4961
                6788872
                31646177
                1f96bdbc-f393-408f-8899-5f7dfd4b2aa3
                Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 31 March 2019
                : 18 September 2019
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: CMMI-1554499
                Funded by: doi http://dx.doi.org/10.13039/100000006, Office of Naval Research;
                Award ID: N00014-18-1-2338
                Funded by: doi http://dx.doi.org/10.13039/100006770, American Chemical Society Petroleum Research Fund;
                Award ID: 56840-DNI7
                Funded by: Research Grant Council of Hong Kong;
                Award ID: 15205318
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Engineering
                Applied Sciences and Engineering
                Engineering
                Custom metadata
                Fritzie Benzon

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