1
|
Rehman NU, Ullah A, Mahmood MA, Rahman N, Sohail M, Iqbal S, Juraev N, Althubeiti K, Al Otaibi S, Khan R. Cobalt-doped zinc oxide based memristors with nociceptor characteristics for bio-inspired technology. RSC Adv 2024; 14:11797-11810. [PMID: 38617576 PMCID: PMC11009837 DOI: 10.1039/d4ra01250j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024] Open
Abstract
Neuromorphic computing is a new field of information technology, which is inspired by the biomimetic properties of the memristor as an electronic synapse and neuron. If there are electronic receptors that can transmit exterior impulses to the internal nervous system, then the use of memristors can be expanded to artificial nerves. In this study, a layer type memristor is used to build an artificial nociceptor in a very feasible and straightforward manner. An artificial nociceptor is demonstrated here through the fabrication and characterization of a cobalt-doped zinc oxide (CZO)/Au based memristor. In order to increase threshold switching performance, the surface effects of the CZO layer are eliminated by adding cobalt cobalt-doped zinc oxide (CZO) layer between the P++-Si and Au electrodes. Allodynia, hyperalgesia, threshold, and relaxation are the four distinct nociceptive behaviours that the device displays based on the strength, rate of relapse, and duration of the external stimuli. The electrons that are trapped in or released from the CZO layer's traps are responsible for these nociceptive behaviours. A multipurpose nociceptor performance is produced by this type of CZO-based device, which is crucial for artificial intelligence system applications such as neural integrated devices with nanometer-sized characteristics.
Collapse
Affiliation(s)
- Naveed Ur Rehman
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | - Aziz Ullah
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | | | - Nasir Rahman
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | - Mohammad Sohail
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
| | - Shahid Iqbal
- Department of Physics, University of Wisconsin La Crosse WI 54601 USA
| | - Nizomiddin Juraev
- Faculty of Chemical Engineering, New Uzbekistan University Tashkent Uzbekistan
- Scientific and Innovation Department, Tashkent State Pedagogical University Tashkent Uzbekistan
| | - Khaled Althubeiti
- Department of Chemistry, College of Science, Taif University P.O. BOX. 110 21944 Taif Saudi Arabia
| | - Sattam Al Otaibi
- Department of Electrical Engineering, College of Engineering Taif University P.O. Box 11099 Taif 21944 Saudi Arabia
| | - Rajwali Khan
- Department of Physics, University of Lakki Marwat Lakki Marwat 2842 KP Pakistan
- Department of Physics, United Arab Emirates University Al Ain 15551 Abu Dhabi UAE
| |
Collapse
|
2
|
Savarimuthu A, Ponniah RJ. Receive, Retain and Retrieve: Psychological and Neurobiological Perspectives on Memory Retrieval. Integr Psychol Behav Sci 2024; 58:303-318. [PMID: 36738400 DOI: 10.1007/s12124-023-09752-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
Memory and learning are interdependent processes that involve encoding, storage, and retrieval. Especially memory retrieval is a fundamental cognitive ability to recall memory traces and update stored memory with new information. For effective memory retrieval and learning, the memory must be stabilized from short-term memory to long-term memory. Hence, it is necessary to understand the process of memory retention and retrieval that enhances the process of learning. Though previous cognitive neuroscience research has focused on memory acquisition and storage, the neurobiological mechanisms underlying memory retrieval and its role in learning are less understood. Therefore, this article offers the viewpoint that memory retrieval is essential for selecting, reactivating, stabilizing, and storing information in long-term memory. In arguing how memories are retrieved, consolidated, transmitted, and strengthened for the long term, the article will examine the psychological and neurobiological aspects of memory and learning with synaptic plasticity, long-term potentiation, genetic transcription, and theta oscillation in the brain.
Collapse
Affiliation(s)
- Anisha Savarimuthu
- Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, India
| | - R Joseph Ponniah
- Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, India.
| |
Collapse
|
4
|
Gao W, Cui Z, Yu Y, Mao J, Xu J, Ji L, Kan X, Shen X, Li X, Zhu S, Hong Y. Application of a Brain-Computer Interface System with Visual and Motor Feedback in Limb and Brain Functional Rehabilitation after Stroke: Case Report. Brain Sci 2022; 12:1083. [PMID: 36009146 PMCID: PMC9405856 DOI: 10.3390/brainsci12081083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 11/25/2022] Open
Abstract
(1) Objective: To investigate the feasibility, safety, and effectiveness of a brain-computer interface (BCI) system with visual and motor feedback in limb and brain function rehabilitation after stroke. (2) Methods: First, we recruited three hemiplegic stroke patients to perform rehabilitation training using a BCI system with visual and motor feedback for two consecutive days (four sessions) to verify the feasibility and safety of the system. Then, we recruited five other hemiplegic stroke patients for rehabilitation training (6 days a week, lasting for 12-14 days) using the same BCI system to verify the effectiveness. The mean and Cohen's w were used to compare the changes in limb motor and brain functions before and after training. (3) Results: In the feasibility verification, the continuous motor state switching time (CMSST) of the three patients was 17.8 ± 21.0s, and the motor state percentages (MSPs) in the upper and lower limb training were 52.6 ± 25.7% and 72.4 ± 24.0%, respectively. The effective training revolutions (ETRs) per minute were 25.8 ± 13.0 for upper limb and 24.8 ± 6.4 for lower limb. There were no adverse events during the training process. Compared with the baseline, the motor function indices of the five patients were improved, including sitting balance ability, upper limb Fugel-Meyer assessment (FMA), lower limb FMA, 6 min walking distance, modified Barthel index, and root mean square (RMS) value of triceps surae, which increased by 0.4, 8.0, 5.4, 11.4, 7.0, and 0.9, respectively, and all had large effect sizes (Cohen's w ≥ 0.5). The brain function indices of the five patients, including the amplitudes of the motor evoked potentials (MEP) on the non-lesion side and lesion side, increased by 3.6 and 3.7, respectively; the latency of MEP on the non-lesion side was shortened by 2.6 ms, and all had large effect sizes (Cohen's w ≥ 0.5). (4) Conclusions: The BCI system with visual and motor feedback is applicable in active rehabilitation training of stroke patients with hemiplegia, and the pilot results show potential multidimensional benefits after a short course of treatment.
Collapse
Affiliation(s)
- Wen Gao
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Zhengzhe Cui
- Zhejiang Laboratory, Department of Intelligent Robot, Keji Avenue, Yuhang Zone, Hangzhou 311100, China
| | - Yang Yu
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Jing Mao
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Jun Xu
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Leilei Ji
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xiuli Kan
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xianshan Shen
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Xueming Li
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| | - Shiqiang Zhu
- Zhejiang Laboratory, Department of Intelligent Robot, Keji Avenue, Yuhang Zone, Hangzhou 311100, China
- Ocean College, Zhejiang University, No. 866 Yuhangtang Road, Xihu Zone, Hangzhou 310030, China
| | - Yongfeng Hong
- Department of Rehabilitation Medicine, The Second Hospital of Anhui Medical University, No. 678 Furong Road, Economic and Technological Development Zone, Hefei 230601, China
| |
Collapse
|
5
|
Wang X, Zhong M, Cheng H, Xie J, Zhou Y, Ren J, Liu M. SpikeGoogle: Spiking Neural Networks with GoogLeNet‐like inception module. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Xuan Wang
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China
| | - Minghong Zhong
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China
| | - Hoiyuen Cheng
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China
| | - Junjie Xie
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China
| | - Yingchu Zhou
- Shenzhen Academy of Metrology and Quality Inspection Shenzhen China
| | - Jun Ren
- Infocare Systems Limited New Zealand
| | - Mengyuan Liu
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China
| |
Collapse
|