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Lee J, Lee AH, Leung V, Laiwalla F, Lopez-Gordo MA, Larson L, Nurmikko A. An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors. NATURE ELECTRONICS 2024; 7:313-324. [PMID: 38737565 PMCID: PMC11078753 DOI: 10.1038/s41928-024-01134-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/15/2024] [Indexed: 05/14/2024]
Abstract
Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.
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Affiliation(s)
- Jihun Lee
- School of Engineering, Brown University, Providence, RI USA
| | - Ah-Hyoung Lee
- School of Engineering, Brown University, Providence, RI USA
| | - Vincent Leung
- Electrical and Computer Engineering, Baylor University, Waco, TX USA
| | - Farah Laiwalla
- School of Engineering, Brown University, Providence, RI USA
| | - Miguel Angel Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain
| | | | - Arto Nurmikko
- School of Engineering, Brown University, Providence, RI USA
- Carney Institute for Brain Science, Brown University, Providence, RI USA
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2
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Du Z, Gupta M, Xu F, Zhang K, Zhang J, Zhou Y, Liu Y, Wang Z, Wrachtrup J, Wong N, Li C, Chu Z. Widefield Diamond Quantum Sensing with Neuromorphic Vision Sensors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304355. [PMID: 37939304 PMCID: PMC10787069 DOI: 10.1002/advs.202304355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/04/2023] [Indexed: 11/10/2023]
Abstract
Despite increasing interest in developing ultrasensitive widefield diamond magnetometry for various applications, achieving high temporal resolution and sensitivity simultaneously remains a key challenge. This is largely due to the transfer and processing of massive amounts of data from the frame-based sensor to capture the widefield fluorescence intensity of spin defects in diamonds. In this study, a neuromorphic vision sensor to encode the changes of fluorescence intensity into spikes in the optically detected magnetic resonance (ODMR) measurements is adopted, closely resembling the operation of the human vision system, which leads to highly compressed data volume and reduced latency. It also results in a vast dynamic range, high temporal resolution, and exceptional signal-to-background ratio. After a thorough theoretical evaluation, the experiment with an off-the-shelf event camera demonstrated a 13× improvement in temporal resolution with comparable precision of detecting ODMR resonance frequencies compared with the state-of-the-art highly specialized frame-based approach. It is successfully deploy this technology in monitoring dynamically modulated laser heating of gold nanoparticles coated on a diamond surface, a recognizably difficult task using existing approaches. The current development provides new insights for high-precision and low-latency widefield quantum sensing, with possibilities for integration with emerging memory devices to realize more intelligent quantum sensors.
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Affiliation(s)
- Zhiyuan Du
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Madhav Gupta
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Feng Xu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Kai Zhang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518000, China
| | - Jiahua Zhang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Yan Zhou
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518000, China
| | - Yiyao Liu
- Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, 510006, China
| | - Zhenyu Wang
- Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, 510006, China
- Frontier Research Institute for Physics, South China Normal University, Guangzhou, 510006, China
| | - Jörg Wrachtrup
- 3rd Institute of Physics, Research Center SCoPE and IQST, University of Stuttgart, 70569, Stuttgart, Germany
- Max Planck Institute for Solid State Research, 70569, Stuttgart, Germany
| | - Ngai Wong
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Can Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
| | - Zhiqin Chu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, P. R. China
- School of Biomedical Sciences, The University of Hong Kong, Hong Kong, 999077, P. R. China
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Hong Kong, 999077, P. R. China
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Siddique MAB, Zhang Y, An H. Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system. Front Comput Neurosci 2023; 17:1274575. [PMID: 38162516 PMCID: PMC10754992 DOI: 10.3389/fncom.2023.1274575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/06/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices. Methods In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms. Results Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage. Discussion This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.
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Affiliation(s)
- Md Abu Bakr Siddique
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
| | - Yan Zhang
- Department of Biological Sciences, Michigan Technological University, Houghton, MI, United States
| | - Hongyu An
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
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Dutta R, Bala A, Sen A, Spinazze MR, Park H, Choi W, Yoon Y, Kim S. Optical Enhancement of Indirect Bandgap 2D Transition Metal Dichalcogenides for Multi-Functional Optoelectronic Sensors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2303272. [PMID: 37453927 DOI: 10.1002/adma.202303272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/21/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023]
Abstract
The unique electrical and optical properties of transition metal dichalcogenides (TMDs) make them attractive nanomaterials for optoelectronic applications, especially optical sensors. However, the optical characteristics of these materials are dependent on the number of layers. Monolayer TMDs have a direct bandgap that provides higher photoresponsivity compared to multilayer TMDs with an indirect bandgap. Nevertheless, multilayer TMDs are more appropriate for various photodetection applications due to their high carrier density, broad spectral response from UV to near-infrared, and ease of large-scale synthesis. Therefore, this review focuses on the modification of the optical properties of devices based on indirect bandgap TMDs and their emerging applications. Several successful developments in optical devices are examined, including band structure engineering, device structure optimization, and heterostructures. Furthermore, it introduces cutting-edge techniques and future directions for optoelectronic devices based on multilayer TMDs.
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Affiliation(s)
- Riya Dutta
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Arindam Bala
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Anamika Sen
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Michael Ross Spinazze
- Waterloo Institute for Nanotechnology and the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
| | - Heekyeong Park
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Woong Choi
- School of Materials Science & Engineering, Kookmin University, Seoul, 02707, Republic of Korea
| | - Youngki Yoon
- Waterloo Institute for Nanotechnology and the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
| | - Sunkook Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
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Zhang H, Qiu P, Lu Y, Ju X, Chi D, Yew KS, Zhu M, Wang S, Wei R, Hu W. In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x-Based Neuristor Array. ACS Sens 2023; 8:3873-3881. [PMID: 37707324 DOI: 10.1021/acssensors.3c01418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
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Affiliation(s)
- Haizhong Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Peng Qiu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Yaoping Lu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Xin Ju
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Kwang Sing Yew
- Global Foundries, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Minmin Zhu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Shaohao Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Rongshan Wei
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Wei Hu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
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Schmid D, Jarvers C, Neumann H. Canonical circuit computations for computer vision. BIOLOGICAL CYBERNETICS 2023; 117:299-329. [PMID: 37306782 PMCID: PMC10600314 DOI: 10.1007/s00422-023-00966-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 05/18/2023] [Indexed: 06/13/2023]
Abstract
Advanced computer vision mechanisms have been inspired by neuroscientific findings. However, with the focus on improving benchmark achievements, technical solutions have been shaped by application and engineering constraints. This includes the training of neural networks which led to the development of feature detectors optimally suited to the application domain. However, the limitations of such approaches motivate the need to identify computational principles, or motifs, in biological vision that can enable further foundational advances in machine vision. We propose to utilize structural and functional principles of neural systems that have been largely overlooked. They potentially provide new inspirations for computer vision mechanisms and models. Recurrent feedforward, lateral, and feedback interactions characterize general principles underlying processing in mammals. We derive a formal specification of core computational motifs that utilize these principles. These are combined to define model mechanisms for visual shape and motion processing. We demonstrate how such a framework can be adopted to run on neuromorphic brain-inspired hardware platforms and can be extended to automatically adapt to environment statistics. We argue that the identified principles and their formalization inspires sophisticated computational mechanisms with improved explanatory scope. These and other elaborated, biologically inspired models can be employed to design computer vision solutions for different tasks and they can be used to advance neural network architectures of learning.
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Affiliation(s)
- Daniel Schmid
- Institute for Neural Information Processing, Ulm University, James-Franck-Ring, Ulm, 89081 Germany
| | - Christian Jarvers
- Institute for Neural Information Processing, Ulm University, James-Franck-Ring, Ulm, 89081 Germany
| | - Heiko Neumann
- Institute for Neural Information Processing, Ulm University, James-Franck-Ring, Ulm, 89081 Germany
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7
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Chun SY, Song YG, Kim JE, Kwon JU, Soh K, Kwon JY, Kang CY, Yoon JH. An Artificial Olfactory System Based on a Chemi-Memristive Device. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2302219. [PMID: 37116944 DOI: 10.1002/adma.202302219] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/18/2023] [Indexed: 06/19/2023]
Abstract
Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.
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Affiliation(s)
- Suk Yeop Chun
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Young Geun Song
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
| | - Ji Eun Kim
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Jae Uk Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Keunho Soh
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Ju Young Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
| | - Chong-Yun Kang
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
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8
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Yin Z, Kaiser MAA, Camara LO, Camarena M, Parsa M, Jacob A, Schwartz G, Jaiswal A. IRIS: Integrated Retinal Functionality in Image Sensors. Front Neurosci 2023; 17:1241691. [PMID: 37719155 PMCID: PMC10502419 DOI: 10.3389/fnins.2023.1241691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware. Gain control in phototransduction and temporal differentiation at the first retinal synapse inspired the first generation of neuromorphic sensors, but processing in downstream retinal circuits, much of which has been discovered in the past decade, has not been implemented in image sensor technology. We present a technology-circuit co-design solution that implements two motion computations-object motion sensitivity and looming detection-at the retina's output that could have wide applications for vision-based decision-making in dynamic environments. Our simulations on Globalfoundries 22 nm technology node show that the proposed retina-inspired circuits can be fabricated on image sensing platforms in existing semiconductor foundries by taking advantage of the recent advances in semiconductor chip stacking technology. Integrated Retinal Functionality in Image Sensors (IRIS) technology could drive advances in machine vision applications that demand energy-efficient and low-bandwidth real-time decision-making.
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Affiliation(s)
- Zihan Yin
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Md Abdullah-Al Kaiser
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | | | - Mark Camarena
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Maryam Parsa
- Electrical and Computer Engineering, George Mason University, Fairfax, VA, United States
| | - Ajey Jacob
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
| | - Gregory Schwartz
- Department of Ophthalmology, Northwestern University, Evanston, IL, United States
| | - Akhilesh Jaiswal
- Information Sciences Institute, University of Southern California, Los Angeles, CA, United States
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9
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Aboumerhi K, Güemes A, Liu H, Tenore F, Etienne-Cummings R. Neuromorphic applications in medicine. J Neural Eng 2023; 20:041004. [PMID: 37531951 DOI: 10.1088/1741-2552/aceca3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023]
Abstract
In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.
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Affiliation(s)
- Khaled Aboumerhi
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Amparo Güemes
- Electrical Engineering Division, Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge CB3 0FA, United Kingdom
| | - Hongtao Liu
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Francesco Tenore
- Research and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
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10
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Zeng T, Wang Z, Lin Y, Cheng Y, Shan X, Tao Y, Zhao X, Xu H, Liu Y. Doppler Frequency-Shift Information Processing in WO x -Based Memristive Synapse for Auditory Motion Perception. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300030. [PMID: 36862024 PMCID: PMC10161103 DOI: 10.1002/advs.202300030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/10/2023] [Indexed: 05/06/2023]
Abstract
Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency-shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx -based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi-nonvolatile mode (M2), which are capable of implementing the high-pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency-shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike-timing-dependent-plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing.
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Affiliation(s)
- Tao Zeng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - YanKun Cheng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xuanyu Shan
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Ye Tao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
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11
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Mangalwedhekar R, Singh N, Thakur CS, Seelamantula CS, Jose M, Nair D. Achieving nanoscale precision using neuromorphic localization microscopy. NATURE NANOTECHNOLOGY 2023; 18:380-389. [PMID: 36690737 DOI: 10.1038/s41565-022-01291-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 11/09/2022] [Indexed: 06/17/2023]
Abstract
Neuromorphic cameras are a new class of dynamic-vision-inspired sensors that encode the rate of change of intensity as events. They can asynchronously record intensity changes as spikes, independent of the other pixels in the receptive field, resulting in sparse measurements. This recording of such sparse events makes them ideal for imaging dynamic processes, such as the stochastic emission of isolated single molecules. Here we show the application of neuromorphic detection to localize nanoscale fluorescent objects below the diffraction limit, with a precision below 20 nm. We demonstrate a combination of neuromorphic detection with segmentation and deep learning approaches to localize and track fluorescent particles below 50 nm with millisecond temporal resolution. Furthermore, we show that combining information from events resulting from the rate of change of intensities improves the classical limit of centroid estimation of single fluorescent objects by nearly a factor of two. Additionally, we validate that using post-processed data from the neuromorphic detector at defined windows of temporal integration allows a better evaluation of the fractalized diffusion of single particle trajectories. Our observations and analysis is useful for event sensing by nonlinear neuromorphic devices to ameliorate real-time particle localization approaches at the nanoscale.
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Affiliation(s)
| | - Nivedita Singh
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Chetan Singh Thakur
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
| | | | - Mini Jose
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Deepak Nair
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India.
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12
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Xia Q, Qin Y, Zheng A, Qiu P. A low-power and flexible bioinspired artificial sensory neuron capable of tactile perceptual and associative learning. J Mater Chem B 2023; 11:1469-1477. [PMID: 36655946 DOI: 10.1039/d2tb02408j] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Biomimetic haptic neuron systems have received a lot of attention from the booming artificial intelligence industry for their wide applications in personal health monitoring, electronic skin, and human-machine interfaces. In this work, inspired by the human tactile afferent nerve, we developed a flexible and low energy consumption artificial tactile neuron, which was constructed by combining a dual network (DN) hydrogel-based sensor and a low power memristor. The tactile sensor (ITO/PAM:CS-Fe3+ hydrogel/ITO) serves as E-skin, with mechanical properties including pressure and stretching. The memristor (Ti:ITO/BiFeO3/ITO) serving as an artificial synapse has low power (∼3.96 × 10-7 W), remarkable uniformity, a large memory window of 500 and excellent plasticity. Remarkably, the pattern recognition simulation based on a neuromorphic network is conducted with a high recognition accuracy of ∼89.81%. In the constructed system, the artificial synapse could be activated by the electrical information from the E-skin induced by an external pressure, to generate excitatory postsynaptic currents. The system shows functions of perception and memory functions, and it also enables tactile associative learning. The present work is important for the development of empowering robots and prostheses with the capability of perceptual learning, and it provides a paradigm for next-generation artificial sensory systems with low-power, wearable and low-cost features.
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Affiliation(s)
- Qing Xia
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Yuxiang Qin
- School of Microelectronics, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin University, Tianjin 300072, China. .,Key Laboratory for Advanced Ceramics and Machining Technology, Ministry of Education, School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Anbo Zheng
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Peilun Qiu
- School of Microelectronics, Tianjin University, Tianjin 300072, China
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13
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Khan MS, Olds JL. When neuro-robots go wrong: A review. Front Neurorobot 2023; 17:1112839. [PMID: 36819005 PMCID: PMC9935594 DOI: 10.3389/fnbot.2023.1112839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Neuro-robots are a class of autonomous machines that, in their architecture, mimic aspects of the human brain and cognition. As such, they represent unique artifacts created by humans based on human understanding of healthy human brains. European Union's Convention on Roboethics 2025 states that the design of all robots (including neuro-robots) must include provisions for the complete traceability of the robots' actions, analogous to an aircraft's flight data recorder. At the same time, one can anticipate rising instances of neuro-robotic failure, as they operate on imperfect data in real environments, and the underlying AI behind such neuro-robots has yet to achieve explainability. This paper reviews the trajectory of the technology used in neuro-robots and accompanying failures. The failures demand an explanation. While drawing on existing explainable AI research, we argue explainability in AI limits the same in neuro-robots. In order to make robots more explainable, we suggest potential pathways for future research.
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14
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Sun C, Liu X, Jiang Q, Ye X, Zhu X, Li RW. Emerging electrolyte-gated transistors for neuromorphic perception. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2162325. [PMID: 36684849 PMCID: PMC9848240 DOI: 10.1080/14686996.2022.2162325] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/18/2022] [Accepted: 12/21/2022] [Indexed: 05/31/2023]
Abstract
With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.
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Affiliation(s)
- Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Qian Jiang
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
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15
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Texture recognition based on multi-sensory integration of proprioceptive and tactile signals. Sci Rep 2022; 12:21690. [PMID: 36522364 PMCID: PMC9755227 DOI: 10.1038/s41598-022-24640-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 11/17/2022] [Indexed: 12/23/2022] Open
Abstract
The sense of touch plays a fundamental role in enabling us to interact with our surrounding environment. Indeed, the presence of tactile feedback in prostheses greatly assists amputees in doing daily tasks. In this line, the present study proposes an integration of artificial tactile and proprioception receptors for texture discrimination under varying scanning speeds. Here, we fabricated a soft biomimetic fingertip including an 8 × 8 array tactile sensor and a piezoelectric sensor to mimic Merkel, Meissner, and Pacinian mechanoreceptors in glabrous skin, respectively. A hydro-elastomer sensor was fabricated as an artificial proprioception sensor (muscle spindles) to assess the instantaneous speed of the biomimetic fingertip. In this study, we investigated the concept of the complex receptive field of RA-I and SA-I afferents for naturalistic textures. Next, to evaluate the synergy between the mechanoreceptors and muscle spindle afferents, ten naturalistic textures were manipulated by a soft biomimetic fingertip at six different speeds. The sensors' outputs were converted into neuromorphic spike trains to mimic the firing pattern of biological mechanoreceptors. These spike responses are then analyzed using machine learning classifiers and neural coding paradigms to explore the multi-sensory integration in real experiments. This synergy between muscle spindle and mechanoreceptors in the proposed neuromorphic system represents a generalized texture discrimination scheme and interestingly irrespective of the scanning speed.
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16
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Yu C, Du Y, Chen M, Wang A, Wang G, Li E. MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks. Front Neurosci 2022; 16:945037. [PMID: 36203801 PMCID: PMC9531034 DOI: 10.3389/fnins.2022.945037] [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: 05/16/2022] [Accepted: 08/29/2022] [Indexed: 11/26/2022] Open
Abstract
Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate the fundamental mechanism of the human brain. Backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, those BP-based algorithms partially ignore bio-interpretability. In modeling spike activity for biological plausible BP-based SNNs, we examine three properties: multiplicity, adaptability, and plasticity (MAP). Regarding multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple-spike transmission to improve model robustness in discrete time iterations. To realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to reduce spike activities for enhanced efficiency. For plasticity, we propose a trainable state-free synapse that models spike response current to increase the diversity of spiking neurons for temporal feature extraction. The proposed SNN model achieves competitive performances on the N-MNIST and SHD neuromorphic datasets. In addition, experimental results demonstrate that the proposed three aspects are significant to iterative robustness, spike efficiency, and the capacity to extract spikes' temporal features. In summary, this study presents a realistic approach for bio-inspired spike activity with MAP, presenting a novel neuromorphic perspective for incorporating biological properties into spiking neural networks.
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Affiliation(s)
- Chengting Yu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
- Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
| | - Yangkai Du
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Mufeng Chen
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
| | - Aili Wang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
- Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
- *Correspondence: Aili Wang
| | - Gaoang Wang
- Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
| | - Erping Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
- Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
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17
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Wang Y, Zhang X, Shen Y, Du B, Zhao G, Cui L, Wen H. Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:3436-3449. [PMID: 33502972 DOI: 10.1109/tpami.2021.3054886] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Dynamic vision sensors (event cameras) have recently been introduced to solve a number of different vision tasks such as object recognition, activities recognition, tracking, etc. Compared with the traditional RGB sensors, the event cameras have many unique advantages such as ultra low resources consumption, high temporal resolution and much larger dynamic range. However, these cameras only produce noisy and asynchronous events of intensity changes, i.e., event-streams rather than frames, where conventional computer vision algorithms can't be directly applied. In our opinion the key challenge for improving the performance of event cameras in vision tasks is finding the appropriate representations of the event-streams so that cutting-edge learning approaches can be applied to fully uncover the spatio-temporal information contained in the event-streams. In this paper, we focus on the event-based human gait identification task and investigate the possible representations of the event-streams when deep neural networks are applied as the classifier. We propose new event-based gait recognition approaches basing on two different representations of the event-stream, i.e., graph and image-like representations, and use graph-based convolutional network (GCN) and convolutional neural networks (CNN) respectively to recognize gait from the event-streams. The two approaches are termed as EV-Gait-3DGraph and EV-Gait-IMG. To evaluate the performance of the proposed approaches, we collect two event-based gait datasets, one from real-world experiments and the other by converting the publicly available RGB gait recognition benchmark CASIA-B. Extensive experiments show that EV-Gait-3DGraph achieves significantly higher recognition accuracy than other competing methods when sufficient training samples are available. However, EV-Gait-IMG converges more quickly than graph-based approaches while training and shows good accuracy with only few number of training samples (less than ten). So image-like presentation is preferable when the amount of training data is limited.
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18
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Neuromorphic object localization using resistive memories and ultrasonic transducers. Nat Commun 2022; 13:3506. [PMID: 35717413 PMCID: PMC9206646 DOI: 10.1038/s41467-022-31157-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 06/03/2022] [Indexed: 11/25/2022] Open
Abstract
Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application. Drawing inspiration from the barn owl’s neuroanatomy, we developed a bio-inspired, event-driven object localization system that couples state-of-the-art piezoelectric micromachined ultrasound transducer sensors to a neuromorphic resistive memories-based computational map. We present measurement results from the fabricated system comprising resistive memories-based coincidence detectors, delay line circuits, and a full-custom ultrasound sensor. We use these experimental results to calibrate our system-level simulations. These simulations are then used to estimate the angular resolution and energy efficiency of the object localization model. The results reveal the potential of our approach, evaluated in orders of magnitude greater energy efficiency than a microcontroller performing the same task. The real-world object localization application needs a low-latency and power efficient computing system. Here, Moro et al. demonstrate a neuromorphic in-memory event driven system, inspired by the barn owl’s neuroanatomy, which is orders of magnitude more energy efficient than microcontrollers.
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19
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A Spike Neural Network Model for Lateral Suppression of Spike-Timing-Dependent Plasticity with Adaptive Threshold. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Aiming at the practical constraints of high resource occupancy and complex calculations in the existing Spike Neural Network (SNN) image classification model, in order to seek a more lightweight and efficient machine vision solution, this paper proposes an adaptive threshold Spike Neural Network (SNN) model of lateral inhibition of Spike-Timing-Dependent Plasticity (STDP). The conversion from grayscale image to pulse sequence is completed by convolution normalization and first pulse time coding. The network self-classification is realized by combining the classical Spike-Timing-Dependent Plasticity algorithm (STDP) and lateral suppression algorithm. The occurrence of overfitting is effectively suppressed by introducing an adaptive threshold. The experimental results on the MNIST data set show that compared with the traditional SNN classification model, the complexity of the weight update algorithm is reduced from O(n2) to O(1), and the accuracy rate can still remain stable at about 96%. The provided model is conducive to the migration of software algorithms to the bottom layer of the hardware platform, and can provide a reference for the realization of edge computing solutions for small intelligent hardware terminals with high efficiency and low power consumption.
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20
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Neuromorphic Neural Engineering Framework-Inspired Online Continuous Learning with Analog Circuitry. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094528] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Neuromorphic hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for machine learning. Here, we propose a neuromorphic analog design for continuous real-time learning. Our hardware design realizes the underlying principles of the neural engineering framework (NEF). NEF brings forth a theoretical framework for the representation and transformation of mathematical constructs with spiking neurons, thus providing efficient means for neuromorphic machine learning and the design of intricate dynamical systems. Our analog circuit design implements the neuromorphic prescribed error sensitivity (PES) learning rule with OZ neurons. OZ is an analog implementation of a spiking neuron, which was shown to have complete correspondence with NEF across firing rates, encoding vectors, and intercepts. We demonstrate PES-based neuromorphic representation of mathematical constructs with varying neuron configurations, the transformation of mathematical constructs, and the construction of a dynamical system with the design of an inducible leaky oscillator. We further designed a circuit emulator, allowing the evaluation of our electrical designs on a large scale. We used the circuit emulator in conjunction with a robot simulator to demonstrate adaptive learning-based control of a robotic arm with six degrees of freedom.
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21
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Yu Q, Song S, Ma C, Pan L, Tan KC. Synaptic Learning With Augmented Spikes. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:1134-1146. [PMID: 33471768 DOI: 10.1109/tnnls.2020.3040969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.
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22
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Abstract
This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.
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Abstract
The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies - from perception to motor control - represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.
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Affiliation(s)
- Chiara Bartolozzi
- Event-Driven Perception for Robotics, Istituto Italiano di Tecnologia, via San Quirico 19D, 16163, Genova, Italy.
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland
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24
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Milde MB, Afshar S, Xu Y, Marcireau A, Joubert D, Ramesh B, Bethi Y, Ralph NO, El Arja S, Dennler N, van Schaik A, Cohen G. Neuromorphic Engineering Needs Closed-Loop Benchmarks. Front Neurosci 2022; 16:813555. [PMID: 35237122 PMCID: PMC8884247 DOI: 10.3389/fnins.2022.813555] [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: 11/11/2021] [Accepted: 01/24/2022] [Indexed: 12/02/2022] Open
Abstract
Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future.
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25
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Vanarse A, Osseiran A, Rassau A, van der Made P. Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts. SENSORS (BASEL, SWITZERLAND) 2022; 22:440. [PMID: 35062402 PMCID: PMC8778084 DOI: 10.3390/s22020440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/30/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.
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Affiliation(s)
- Anup Vanarse
- Brainchip Research Institute, Perth 6000, Australia; (A.O.); (P.v.d.M.)
| | - Adam Osseiran
- Brainchip Research Institute, Perth 6000, Australia; (A.O.); (P.v.d.M.)
| | - Alexander Rassau
- School of Engineering, Edith Cowan University, Joondalup 6027, Australia;
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Xia Q, Qin Y, Qiu P, Zheng A, Zhang X. Bio‑inspired Tactile Nociceptor Constructed by Integrating Wearable Sensing Paper and VO2 Threshold Switching Memristor. J Mater Chem B 2022; 10:1991-2000. [DOI: 10.1039/d1tb02578c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The sensations of touch and pain are fundamental components of our daily life, which can transport vital information about the surroundings and provide protection to our bodies. In this study,...
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27
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Gallego G, Delbruck T, Orchard G, Bartolozzi C, Taba B, Censi A, Leutenegger S, Davison AJ, Conradt J, Daniilidis K, Scaramuzza D. Event-Based Vision: A Survey. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:154-180. [PMID: 32750812 DOI: 10.1109/tpami.2020.3008413] [Citation(s) in RCA: 151] [Impact Index Per Article: 75.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of μs), very high dynamic range (140 dB versus 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.
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Hazan A, Ezra Tsur E. Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation. Front Neurosci 2021; 15:627221. [PMID: 33692670 PMCID: PMC7937893 DOI: 10.3389/fnins.2021.627221] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 01/25/2021] [Indexed: 11/13/2022] Open
Abstract
Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence. The Neural Engineering Framework (NEF) brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons to implement functional large-scale neural networks. Here, we present OZ, a programable analog implementation of NEF-inspired spiking neurons. OZ neurons can be dynamically programmed to feature varying high-dimensional response curves with positive and negative encoders for a neuromorphic distributed representation of normalized input data. Our hardware design demonstrates full correspondence with NEF across firing rates, encoding vectors, and intercepts. OZ neurons can be independently configured in real-time to allow efficient spanning of a representation space, thus using fewer neurons and therefore less power for neuromorphic data representation.
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Affiliation(s)
- Avi Hazan
- Neuro-Biomorphic Engineering Lab, Department of Mathematics and Computer Science, The Open University of Israel, Ra'anana, Israel
| | - Elishai Ezra Tsur
- Neuro-Biomorphic Engineering Lab, Department of Mathematics and Computer Science, The Open University of Israel, Ra'anana, Israel
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Parvizi-Fard A, Salimi-Nezhad N, Amiri M, Falotico E, Laschi C. Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors. Sci Rep 2021; 11:2109. [PMID: 33483529 PMCID: PMC7822817 DOI: 10.1038/s41598-021-81199-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 01/04/2021] [Indexed: 01/30/2023] Open
Abstract
Touch and pain sensations are complementary aspects of daily life that convey crucial information about the environment while also providing protection to our body. Technological advancements in prosthesis design and control mechanisms assist amputees to regain lost function but often they have no meaningful tactile feedback or perception. In the present study, we propose a bio-inspired tactile system with a population of 23 digital afferents: 12 RA-I, 6 SA-I, and 5 nociceptors. Indeed, the functional concept of the nociceptor is implemented on the FPGA for the first time. One of the main features of biological tactile afferents is that their distal axon branches in the skin, creating complex receptive fields. Given these physiological observations, the bio-inspired afferents are randomly connected to the several neighboring mechanoreceptors with different weights to form their own receptive field. To test the performance of the proposed neuromorphic chip in sharpness detection, a robotic system with three-degree of freedom equipped with the tactile sensor indents the 3D-printed objects. Spike responses of the biomimetic afferents are then collected for analysis by rate and temporal coding algorithms. In this way, the impact of the innervation mechanism and collaboration of afferents and nociceptors on sharpness recognition are investigated. Our findings suggest that the synergy between sensory afferents and nociceptors conveys more information about tactile stimuli which in turn leads to the robustness of the proposed neuromorphic system against damage to the taxels or afferents. Moreover, it is illustrated that spiking activity of the biomimetic nociceptors is amplified as the sharpness increases which can be considered as a feedback mechanism for prosthesis protection. This neuromorphic approach advances the development of prosthesis to include the sensory feedback and to distinguish innocuous (non-painful) and noxious (painful) stimuli.
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Affiliation(s)
- Adel Parvizi-Fard
- grid.412112.50000 0001 2012 5829Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Nima Salimi-Nezhad
- grid.412112.50000 0001 2012 5829Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahmood Amiri
- grid.412112.50000 0001 2012 5829Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Parastar Ave., Kermanshah, Iran
| | - Egidio Falotico
- grid.263145.70000 0004 1762 600XThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy ,grid.263145.70000 0004 1762 600XDepartment of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Cecilia Laschi
- grid.263145.70000 0004 1762 600XThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy ,grid.263145.70000 0004 1762 600XDepartment of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy ,grid.4280.e0000 0001 2180 6431Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore
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Hulea M, Ghassemlooy Z, Rajbhandari S, Younus OI, Barleanu A. Optical Axons for Electro-Optical Neural Networks. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6119. [PMID: 33121207 PMCID: PMC7663001 DOI: 10.3390/s20216119] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/19/2020] [Accepted: 10/23/2020] [Indexed: 11/30/2022]
Abstract
Recently, neuromorphic sensors, which convert analogue signals to spiking frequencies, have been reported for neurorobotics. In bio-inspired systems these sensors are connected to the main neural unit to perform post-processing of the sensor data. The performance of spiking neural networks has been improved using optical synapses, which offer parallel communications between the distanced neural areas but are sensitive to the intensity variations of the optical signal. For systems with several neuromorphic sensors, which are connected optically to the main unit, the use of optical synapses is not an advantage. To address this, in this paper we propose and experimentally verify optical axons with synapses activated optically using digital signals. The synaptic weights are encoded by the energy of the stimuli, which are then optically transmitted independently. We show that the optical intensity fluctuations and link's misalignment result in delay in activation of the synapses. For the proposed optical axon, we have demonstrated line of sight transmission over a maximum link length of 190 cm with a delay of 8 μs. Furthermore, we show the axon delay as a function of the illuminance using a fitted model for which the root mean square error (RMS) similarity is 0.95.
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Affiliation(s)
- Mircea Hulea
- Faculty of Automatic Control and Computer Engineering at Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania;
| | - Zabih Ghassemlooy
- Optical Communications Research Group, Faculty of Engineering and Environment at Northumbria University, Newcastle upon Tyne NE7 7XA, UK; (Z.G.); (O.I.Y.)
| | | | - Othman Isam Younus
- Optical Communications Research Group, Faculty of Engineering and Environment at Northumbria University, Newcastle upon Tyne NE7 7XA, UK; (Z.G.); (O.I.Y.)
| | - Alexandru Barleanu
- Faculty of Automatic Control and Computer Engineering at Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania;
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George R, Chiappalone M, Giugliano M, Levi T, Vassanelli S, Partzsch J, Mayr C. Plasticity and Adaptation in Neuromorphic Biohybrid Systems. iScience 2020; 23:101589. [PMID: 33083749 PMCID: PMC7554028 DOI: 10.1016/j.isci.2020.101589] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.
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Affiliation(s)
- Richard George
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | | | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies, Trieste, Italy
| | - Timothée Levi
- Laboratoire de l’Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Stefano Vassanelli
- Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | - Christian Mayr
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
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Sankar S, Balamurugan D, Brown A, Ding K, Xu X, Low JH, Yeow CH, Thakor N. Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback. Soft Robot 2020; 8:577-587. [PMID: 32976080 DOI: 10.1089/soro.2020.0016] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over 16 independent parameters when tested on 13 standardized textured surfaces. The 16 parameters were the combination of 4 angles of flexion of the soft finger and 4 speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provide sensory feedback; furthermore, texture feedback has the potential to enhance user experience when interacting with their surroundings.
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Affiliation(s)
- Sriramana Sankar
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Darshini Balamurugan
- Laboratory for Computational Sensing and Robotics, (LCSR) Johns Hopkins University, Baltimore, Maryland, USA
| | - Alisa Brown
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xingyuan Xu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Jin Huat Low
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Chen Hua Yeow
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Nitish Thakor
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biomedical Engineering, National University of Singapore, Singapore.,Singapore Institute for Neurotechnology (SINAPSE) Laboratory, National University of Singapore, Singapore
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Tan C, Ceballos G, Kasabov N, Puthanmadam Subramaniyam N. FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5328. [PMID: 32957655 PMCID: PMC7571195 DOI: 10.3390/s20185328] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/04/2020] [Accepted: 09/11/2020] [Indexed: 01/22/2023]
Abstract
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.
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Affiliation(s)
- Clarence Tan
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Gerardo Ceballos
- School of Electrical Engineering, University of Los Andes, Merida 5101, Venezuela;
| | - Nikola Kasabov
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Narayan Puthanmadam Subramaniyam
- Faculty of Medicine and Health Technology and BioMediTech Institute, Tampere University, 33520 Tampere, Finland;
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
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35
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Salt L, Howard D, Indiveri G, Sandamirskaya Y. Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance Targeting Neuromorphic Processors. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:3305-3318. [PMID: 31613785 DOI: 10.1109/tnnls.2019.2941506] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The Lobula giant movement detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses. Understanding the neural principles and network structure that leads to these fast and robust responses can facilitate the design of efficient obstacle avoidance strategies for robotic applications. Here, we present a neuromorphic spiking neural network model of the LGMD driven by the output of a neuromorphic dynamic vision sensor (DVS), which incorporates spiking frequency adaptation and synaptic plasticity mechanisms, and which can be mapped onto existing neuromorphic processor chips. However, as the model has a wide range of parameters and the mixed-signal analog-digital circuits used to implement the model are affected by variability and noise, it is necessary to optimize the parameters to produce robust and reliable responses. Here, we propose to use differential evolution (DE) and Bayesian optimization (BO) techniques to optimize the parameter space and investigate the use of self-adaptive DE (SADE) to ameliorate the difficulties of finding appropriate input parameters for the DE technique. We quantify the performance of the methods proposed with a comprehensive comparison of different optimizers applied to the model and demonstrate the validity of the approach proposed using recordings made from a DVS sensor mounted on an unmanned aerial vehicle (UAV).
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36
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Vanarse A, Espinosa-Ramos JI, Osseiran A, Rassau A, Kasabov N. Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification. SENSORS 2020; 20:s20102756. [PMID: 32408563 PMCID: PMC7294411 DOI: 10.3390/s20102756] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/03/2020] [Accepted: 05/07/2020] [Indexed: 01/24/2023]
Abstract
Existing methods in neuromorphic olfaction mainly focus on implementing the data transformation based on the neurobiological architecture of the olfactory pathway. While the transformation is pivotal for the sparse spike-based representation of odor data, classification techniques based on the bio-computations of the higher brain areas, which process the spiking data for identification of odor, remain largely unexplored. This paper argues that brain-inspired spiking neural networks constitute a promising approach for the next generation of machine intelligence for odor data processing. Inspired by principles of brain information processing, here we propose the first spiking neural network method and associated deep machine learning system for classification of odor data. The paper demonstrates that the proposed approach has several advantages when compared to the current state-of-the-art methods. Based on results obtained using a benchmark dataset, the model achieved a high classification accuracy for a large number of odors and has the capacity for incremental learning on new data. The paper explores different spike encoding algorithms and finds that the most suitable for the task is the step-wise encoding function. Further directions in the brain-inspired study of odor machine classification include investigation of more biologically plausible algorithms for mapping, learning, and interpretation of odor data along with the realization of these algorithms on some highly parallel and low power consuming neuromorphic hardware devices for real-world applications.
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Affiliation(s)
- Anup Vanarse
- School of Engineering, Edith Cowan University, Perth 6027, Australia; (A.O.); (A.R.)
- Correspondence: (A.V.); (N.K.)
| | | | - Adam Osseiran
- School of Engineering, Edith Cowan University, Perth 6027, Australia; (A.O.); (A.R.)
| | - Alexander Rassau
- School of Engineering, Edith Cowan University, Perth 6027, Australia; (A.O.); (A.R.)
| | - Nikola Kasabov
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand;
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Londonderry BT48 7JL, UK
- Correspondence: (A.V.); (N.K.)
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Kugele A, Pfeil T, Pfeiffer M, Chicca E. Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks. Front Neurosci 2020; 14:439. [PMID: 32431592 PMCID: PMC7214871 DOI: 10.3389/fnins.2020.00439] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/09/2020] [Indexed: 11/15/2022] Open
Abstract
Spiking neural networks (SNNs) are potentially highly efficient models for inference on fully parallel neuromorphic hardware, but existing training methods that convert conventional artificial neural networks (ANNs) into SNNs are unable to exploit these advantages. Although ANN-to-SNN conversion has achieved state-of-the-art accuracy for static image classification tasks, the following subtle but important difference in the way SNNs and ANNs integrate information over time makes the direct application of conversion techniques for sequence processing tasks challenging. Whereas all connections in SNNs have a certain propagation delay larger than zero, ANNs assign different roles to feed-forward connections, which immediately update all neurons within the same time step, and recurrent connections, which have to be rolled out in time and are typically assigned a delay of one time step. Here, we present a novel method to obtain highly accurate SNNs for sequence processing by modifying the ANN training before conversion, such that delays induced by ANN rollouts match the propagation delays in the targeted SNN implementation. Our method builds on the recently introduced framework of streaming rollouts, which aims for fully parallel model execution of ANNs and inherently allows for temporal integration by merging paths of different delays between input and output of the network. The resulting networks achieve state-of-the-art accuracy for multiple event-based benchmark datasets, including N-MNIST, CIFAR10-DVS, N-CARS, and DvsGesture, and through the use of spatio-temporal shortcut connections yield low-latency approximate network responses that improve over time as more of the input sequence is processed. In addition, our converted SNNs are consistently more energy-efficient than their corresponding ANNs.
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Affiliation(s)
- Alexander Kugele
- Faculty of Technology and Center of Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
- Bosch Center for Artificial Intelligence, Renningen, Germany
| | - Thomas Pfeil
- Bosch Center for Artificial Intelligence, Renningen, Germany
| | | | - Elisabetta Chicca
- Faculty of Technology and Center of Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
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Bergner F, Dean-Leon E, Cheng G. Design and Realization of an Efficient Large-Area Event-Driven E-Skin. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1965. [PMID: 32244511 PMCID: PMC7180917 DOI: 10.3390/s20071965] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/26/2020] [Accepted: 03/27/2020] [Indexed: 12/20/2022]
Abstract
The sense of touch enables us to safely interact and control our contacts with our surroundings. Many technical systems and applications could profit from a similar type of sense. Yet, despite the emergence of e-skin systems covering more extensive areas, large-area realizations of e-skin effectively boosting applications are still rare. Recent advancements have improved the deployability and robustness of e-skin systems laying the basis for their scalability. However, the upscaling of e-skin systems introduces yet another challenge-the challenge of handling a large amount of heterogeneous tactile information with complex spatial relations between sensing points. We targeted this challenge and proposed an event-driven approach for large-area skin systems. While our previous works focused on the implementation and the experimental validation of the approach, this work now provides the consolidated foundations for realizing, designing, and understanding large-area event-driven e-skin systems for effective applications. This work homogenizes the different perspectives on event-driven systems and assesses the applicability of existing event-driven implementations in large-area skin systems. Additionally, we provide novel guidelines for tuning the novelty-threshold of event generators. Overall, this work develops a systematic approach towards realizing a flexible event-driven information handling system on standard computer systems for large-scale e-skin with detailed descriptions on the effective design of event generators and decoders. All designs and guidelines are validated by outlining their impacts on our implementations, and by consolidating various experimental results. The resulting system design for e-skin systems is scalable, efficient, flexible, and capable of handling large amounts of information without customized hardware. The system provides the feasibility of complex large-area tactile applications, for instance in robotics.
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Affiliation(s)
- Florian Bergner
- Institute for Cognitive Systems (ICS), Technische Universität München, Arcisstraße 21, 80333 München, Germany; (E.D.-L.); (G.C.)
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Marcireau A, Ieng SH, Benosman R. Sepia, Tarsier, and Chameleon: A Modular C++ Framework for Event-Based Computer Vision. Front Neurosci 2020; 13:1338. [PMID: 31969799 PMCID: PMC6960268 DOI: 10.3389/fnins.2019.01338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 11/27/2019] [Indexed: 11/13/2022] Open
Abstract
This paper introduces an new open-source, header-only and modular C++ framework to facilitate the implementation of event-driven algorithms. The framework relies on three independent components: sepia (file IO), tarsier (algorithms), and chameleon (display). Our benchmarks show that algorithms implemented with tarsier are faster and have a lower latency than identical implementations in other state-of-the-art frameworks, thanks to static polymorphism (compile-time pipeline assembly). The observer pattern used throughout the framework encourages implementations that better reflect the event-driven nature of the algorithms and the way they process events, easing future translation to neuromorphic hardware. The framework integrates drivers to communicate with the DVS, the DAVIS, the Opal Kelly ATIS, and the CCam ATIS.
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Affiliation(s)
- Alexandre Marcireau
- INSERM UMRI S 968, Sorbonne Universites, UPMC Univ Paris 06, UMR S 968, CNRS, UMR 7210, Institut de la Vision, Paris, France
| | - Sio-Hoi Ieng
- INSERM UMRI S 968, Sorbonne Universites, UPMC Univ Paris 06, UMR S 968, CNRS, UMR 7210, Institut de la Vision, Paris, France
| | - Ryad Benosman
- INSERM UMRI S 968, Sorbonne Universites, UPMC Univ Paris 06, UMR S 968, CNRS, UMR 7210, Institut de la Vision, Paris, France.,University of Pittsburgh Medical Center, Pittsburgh, PA, United States.,Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States
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40
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Pan Z, Chua Y, Wu J, Zhang M, Li H, Ambikairajah E. An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks. Front Neurosci 2020; 13:1420. [PMID: 32038132 PMCID: PMC6987407 DOI: 10.3389/fnins.2019.01420] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 12/16/2019] [Indexed: 12/11/2022] Open
Abstract
The auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audio, into an efficient, effective and reconstructable spike pattern to facilitate the subsequent processing. However, most of the auditory front-ends in current studies have not made use of recent findings in psychoacoustics and physiology concerning human listening. In this paper, we propose a neural encoding and decoding scheme that is optimized for audio processing. The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve. We evaluate the perceptual quality of the BAE scheme using PESQ; the performance of the BAE based on sound classification and speech recognition experiments. Finally, we also built and published two spike-version of speech datasets: the Spike-TIDIGITS and the Spike-TIMIT, for researchers to use and benchmarking of future SNN research.
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Affiliation(s)
- Zihan Pan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Yansong Chua
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
| | - Jibin Wu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Malu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Haizhou Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Eliathamby Ambikairajah
- School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia
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41
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Towards spike-based machine intelligence with neuromorphic computing. Nature 2019; 575:607-617. [PMID: 31776490 DOI: 10.1038/s41586-019-1677-2] [Citation(s) in RCA: 286] [Impact Index Per Article: 57.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 07/09/2019] [Indexed: 11/08/2022]
Abstract
Guided by brain-like 'spiking' computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence-promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm-hardware codesign.
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Yang S, Wang J, Deng B, Liu C, Li H, Fietkiewicz C, Loparo KA. Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:2490-2503. [PMID: 29993922 DOI: 10.1109/tcyb.2018.2823730] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The investigation of the human intelligence, cognitive systems and functional complexity of human brain is significantly facilitated by high-performance computational platforms. In this paper, we present a real-time digital neuromorphic system for the simulation of large-scale conductance-based spiking neural networks (LaCSNN), which has the advantages of both high biological realism and large network scale. Using this system, a detailed large-scale cortico-basal ganglia-thalamocortical loop is simulated using a scalable 3-D network-on-chip (NoC) topology with six Altera Stratix III field-programmable gate arrays simulate 1 million neurons. Novel router architecture is presented to deal with the communication of multiple data flows in the multinuclei neural network, which has not been solved in previous NoC studies. At the single neuron level, cost-efficient conductance-based neuron models are proposed, resulting in the average utilization of 95% less memory resources and 100% less DSP resources for multiplier-less realization, which is the foundation of the large-scale realization. An analysis of the modified models is conducted, including investigation of bifurcation behaviors and ionic dynamics, demonstrating the required range of dynamics with a more reduced resource cost. The proposed LaCSNN system is shown to outperform the alternative state-of-the-art approaches previously used to implement the large-scale spiking neural network, and enables a broad range of potential applications due to its real-time computational power.
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Krichmar JL, Severa W, Khan MS, Olds JL. Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future. Front Neurosci 2019; 13:666. [PMID: 31316340 PMCID: PMC6610536 DOI: 10.3389/fnins.2019.00666] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 06/11/2019] [Indexed: 11/24/2022] Open
Abstract
The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the twenty first century. Its period of phenomenal growth likely lies ahead. AI-operated machines and technologies will extend the reach of Homo sapiens far beyond the biological constraints imposed by evolution: outwards further into deep space, as well as inwards into the nano-world of DNA sequences and relevant medical applications. And yet, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. The AI's design and function may be dependent upon the type of energy source, as well as its availability and accessibility. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. While one of the key advantages of AI use is to reduce the dimensionality of a complex problem, the fact remains that some energy is required for functionality. Hence, the materials and technologies that provide the needed energy represent a critical challenge toward future use scenarios of AI and should be integrated into their design. Here we look to the brain and other aspects of biology as inspiration for Biomimetic Research for Energy-efficient AI Designs (BREAD).
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Affiliation(s)
- Jeffrey L. Krichmar
- Departments of Cognitive Sciences and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - William Severa
- Sandia National Laboratories, Data-Driven and Neural Computing, Albuquerque, NM, United States
| | - Muhammad S. Khan
- Schar School, George Mason University, Arlington, VA, United States
| | - James L. Olds
- Schar School, George Mason University, Arlington, VA, United States
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Steffen L, Reichard D, Weinland J, Kaiser J, Roennau A, Dillmann R. Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms. Front Neurorobot 2019; 13:28. [PMID: 31191287 PMCID: PMC6546825 DOI: 10.3389/fnbot.2019.00028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 05/07/2019] [Indexed: 11/16/2022] Open
Abstract
Any visual sensor, whether artificial or biological, maps the 3D-world on a 2D-representation. The missing dimension is depth and most species use stereo vision to recover it. Stereo vision implies multiple perspectives and matching, hence it obtains depth from a pair of images. Algorithms for stereo vision are also used prosperously in robotics. Although, biological systems seem to compute disparities effortless, artificial methods suffer from high energy demands and latency. The crucial part is the correspondence problem; finding the matching points of two images. The development of event-based cameras, inspired by the retina, enables the exploitation of an additional physical constraint—time. Due to their asynchronous course of operation, considering the precise occurrence of spikes, Spiking Neural Networks take advantage of this constraint. In this work, we investigate sensors and algorithms for event-based stereo vision leading to more biologically plausible robots. Hereby, we focus mainly on binocular stereo vision.
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Affiliation(s)
- Lea Steffen
- FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Daniel Reichard
- FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Jakob Weinland
- FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Jacques Kaiser
- FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Arne Roennau
- FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Rüdiger Dillmann
- FZI Research Center for Information Technology, Karlsruhe, Germany.,Humanoids and Intelligence Systems Lab, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Pedroni BU, Joshi S, Deiss SR, Sheik S, Detorakis G, Paul S, Augustine C, Neftci EO, Cauwenberghs G. Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity. Front Neurosci 2019; 13:357. [PMID: 31110470 PMCID: PMC6499189 DOI: 10.3389/fnins.2019.00357] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 03/28/2019] [Indexed: 11/13/2022] Open
Abstract
Spike-Timing-Dependent Plasticity (STDP) is a bio-inspired local incremental weight update rule commonly used for online learning in spike-based neuromorphic systems. In STDP, the intensity of long-term potentiation and depression in synaptic efficacy (weight) between neurons is expressed as a function of the relative timing between pre- and post-synaptic action potentials (spikes), while the polarity of change is dependent on the order (causality) of the spikes. Online STDP weight updates for causal and acausal relative spike times are activated at the onset of post- and pre-synaptic spike events, respectively, implying access to synaptic connectivity both in forward (pre-to-post) and reverse (post-to-pre) directions. Here we study the impact of different arrangements of synaptic connectivity tables on weight storage and STDP updates for large-scale neuromorphic systems. We analyze the memory efficiency for varying degrees of density in synaptic connectivity, ranging from crossbar arrays for full connectivity to pointer-based lookup for sparse connectivity. The study includes comparison of storage and access costs and efficiencies for each memory arrangement, along with a trade-off analysis of the benefits of each data structure depending on application requirements and budget. Finally, we present an alternative formulation of STDP via a delayed causal update mechanism that permits efficient weight access, requiring no more than forward connectivity lookup. We show functional equivalence of the delayed causal updates to the original STDP formulation, with substantial savings in storage and access costs and efficiencies for networks with sparse synaptic connectivity as typically encountered in large-scale models in computational neuroscience.
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Affiliation(s)
- Bruno U Pedroni
- Integrated Systems Neuroengineering Laboratory, Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Siddharth Joshi
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Stephen R Deiss
- Integrated Systems Neuroengineering Laboratory, Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | | | - Georgios Detorakis
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Somnath Paul
- Intel Corporation - Circuit Research Lab, Hillsboro, OR, United States
| | - Charles Augustine
- Intel Corporation - Circuit Research Lab, Hillsboro, OR, United States
| | - Emre O Neftci
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Gert Cauwenberghs
- Integrated Systems Neuroengineering Laboratory, Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
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Frenkel C, Lefebvre M, Legat JD, Bol D. A 0.086-mm 2 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:145-158. [PMID: 30418919 DOI: 10.1109/tbcas.2018.2880425] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cognitive SNNs requires the design of compact, low-power and versatile experimentation platforms with the key requirement of online learning in order to adapt and learn new features in uncontrolled environments. However, embedding online learning in SNNs is currently hindered by high incurred complexity and area overheads. In this paper, we present ODIN, a 0.086-mm 2 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm FDSOI CMOS achieving a minimum energy per synaptic operation (SOP) of 12.7 pJ. It leverages an efficient implementation of the spike-driven synaptic plasticity (SDSP) learning rule for high-density embedded online learning with only 0.68 μm 2 per 4-bit synapse. Neurons can be independently configured as a standard leaky integrate-and-fire model or as a custom phenomenological model that emulates the 20 Izhikevich behaviors found in biological spiking neurons. Using a single presentation of 6k 16 × 16 MNIST training images to a single-layer fully-connected 10-neuron network with on-chip SDSP-based learning, ODIN achieves a classification accuracy of 84.5%, while consuming only 15 nJ/inference at 0.55 V using rank order coding. ODIN thus enables further developments toward cognitive neuromorphic devices for low-power, adaptive and low-cost processing.
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Kreiser R, Aathmani D, Qiao N, Indiveri G, Sandamirskaya Y. Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields. Front Neurosci 2018; 12:717. [PMID: 30524218 PMCID: PMC6262404 DOI: 10.3389/fnins.2018.00717] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/19/2018] [Indexed: 11/26/2022] Open
Abstract
Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to physically emulate the functionality of biological neurons and synapses enables faithful modeling of neural and synaptic dynamics at ultra low power consumption in real-time, and thus may serve as computational substrate for a new generation of efficient neural controllers for artificial intelligent systems. Although one of the main advantages of neural networks is their ability to perform on-line learning, only a small number of neuromorphic hardware devices implement this feature on-chip. In this work, we use a reconfigurable on-line learning spiking (ROLLS) neuromorphic processor chip to build a neuronal architecture for sequence learning. The proposed neuronal architecture uses the attractor properties of winner-takes-all (WTA) dynamics to cope with mismatch and noise in the ROLLS analog computing elements, and it uses its on-chip plasticity features to store sequences of states. We demonstrate, with a proof-of-concept feasibility study how this architecture can store, replay, and update sequences of states, induced by external inputs. Controlled by the attractor dynamics and an explicit destabilizing signal, the items in a sequence can last for varying amounts of time and thus reliable sequence learning and replay can be robustly implemented in a real sensorimotor system.
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Affiliation(s)
- Raphaela Kreiser
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Dora Aathmani
- The School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Ning Qiao
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yulia Sandamirskaya
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
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Detorakis G, Sheik S, Augustine C, Paul S, Pedroni BU, Dutt N, Krichmar J, Cauwenberghs G, Neftci E. Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning. Front Neurosci 2018; 12:583. [PMID: 30210274 PMCID: PMC6123384 DOI: 10.3389/fnins.2018.00583] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 08/03/2018] [Indexed: 11/13/2022] Open
Abstract
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large scales that are both flexible and efficient have been hindered by a lack of a suitable algorithmic framework. As a result, most neuromorphic hardware are trained off-line on large clusters of dedicated processors or GPUs and transferred post hoc to the device. We address this by introducing the neural and synaptic array transceiver (NSAT), a neuromorphic computational framework facilitating flexible and efficient embedded learning by matching algorithmic requirements and neural and synaptic dynamics. NSAT supports event-driven supervised, unsupervised and reinforcement learning algorithms including deep learning. We demonstrate the NSAT in a wide range of tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural fields, event-driven random back-propagation for event-based deep learning, event-based contrastive divergence for unsupervised learning, and voltage-based learning rules for sequence learning. We anticipate that this contribution will establish the foundation for a new generation of devices enabling adaptive mobile systems, wearable devices, and robots with data-driven autonomy.
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Affiliation(s)
- Georgios Detorakis
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Sadique Sheik
- Biocircuits Institute, University of California, San Diego, La Jolla, CA, United States
| | - Charles Augustine
- Intel Corporation-Circuit Research Lab, Hillsboro, OR, United States
| | - Somnath Paul
- Intel Corporation-Circuit Research Lab, Hillsboro, OR, United States
| | - Bruno U. Pedroni
- Department of Bioengineering and Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Nikil Dutt
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Jeffrey Krichmar
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Gert Cauwenberghs
- Department of Bioengineering and Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Emre Neftci
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
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Sherfey JS, Ardid S, Hass J, Hasselmo ME, Kopell NJ. Flexible resonance in prefrontal networks with strong feedback inhibition. PLoS Comput Biol 2018; 14:e1006357. [PMID: 30091975 PMCID: PMC6103521 DOI: 10.1371/journal.pcbi.1006357] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 08/21/2018] [Accepted: 07/11/2018] [Indexed: 11/24/2022] Open
Abstract
Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.
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Affiliation(s)
- Jason S. Sherfey
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
- Department of Psychological and Brain Sciences, Center for Systems Neuroscience, Boston University, Massachusetts, United States of America
| | - Salva Ardid
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
| | - Joachim Hass
- Department of Theoretical Neuroscience, Bernstein Center for Computational Neuroscience, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
- Faculty of Applied Psychology, SRH University for Applied Sciences Heidelberg, Heidelberg, Germany
| | - Michael E. Hasselmo
- Department of Psychological and Brain Sciences, Center for Systems Neuroscience, Boston University, Massachusetts, United States of America
| | - Nancy J. Kopell
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America
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