1
|
Philip P, Jainwal K, van Schaik A, Thakur CS. Tau-Cell-Based Analog Silicon Retina With Spatio- Temporal Filtering and Contrast Gain Control. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:423-437. [PMID: 37956014 DOI: 10.1109/tbcas.2023.3332117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Developing precise artificial retinas is crucial because they hold the potential to restore vision, improve visual prosthetics, and enhance computer vision systems. Emulating the luminance and contrast adaption features of the retina is essential to improve visual perception and efficiency to provide an environment realistic representation to the user. In this article, we introduce an artificial retina model that leverages its potent adaptation to luminance and contrast to enhance vision sensing and information processing. The model has the ability to achieve the realization of both tonic and phasic cells in the simplest manner. We have implemented the retina model using 0.18 μm process technology and validated the accuracy of the hardware implementation through circuit simulation that closely matches the software retina model. Additionally, we have characterized a single pixel fabricated using the same 0.18 μm process. This pixel demonstrates an 87.7-% ratio of variance with the temporal software model and operates with a power consumption of 369 nW.
Collapse
|
2
|
Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
Collapse
Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
| |
Collapse
|
3
|
Nazari S, Jamshidi S. Efficient digital design of the nonlinear behavior of Hindmarsh-Rose neuron model in large-scale neural population. Sci Rep 2024; 14:3833. [PMID: 38360852 PMCID: PMC10869816 DOI: 10.1038/s41598-024-54525-8] [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: 11/05/2023] [Accepted: 02/13/2024] [Indexed: 02/17/2024] Open
Abstract
Spiking networks, as the third generation of neural networks, are of great interest today due to their low power consumption in cognitive processes. This important characteristic has caused the hardware implementation techniques of spiking networks in the form of neuromorphic systems attract a lot of attention. For the first time, the focus is on the digital implementation based on CORDIC approximation of the Hindmarsh-Rose (HR) neuron so that the hardware implementation cost is lower than previous studies. If the digital design of a neuron is done efficient, the possibility of implementing a population of neurons is provided for the feasibility of low-consumption implementation of high-level cognitive processes in hardware, which is considered in this paper through edge detector, noise removal and image magnification spiking networks based on the proposed CORDIC_HR model. While using less hardware resources, the proposed HR neuron model follows the behavior of the original neuron model in the time domain with much less error than previous study. Also, the complex nonlinear behavior of the original and the proposed model of HR neuron through the bifurcation diagram, phase space and nullcline space analysis under different system parameters was investigated and the good follow-up of the proposed model was confirmed from the original model. In addition to the fact that the individual behavior of the original and the proposed neurons is the same, the functional and behavioral performance of the randomly connected neuronal population of original and proposed neuron model is equal. In general, the main contribution of the paper is in presenting an efficient hardware model, which consumes less hardware resources, follows the behavior of the original model with high accuracy, and has an acceptable performance in image processing applications such as noise removal and edge detection.
Collapse
Affiliation(s)
- Soheila Nazari
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
| | - Shabnam Jamshidi
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
| |
Collapse
|
4
|
Zhu R, Lilak S, Loeffler A, Lizier J, Stieg A, Gimzewski J, Kuncic Z. Online dynamical learning and sequence memory with neuromorphic nanowire networks. Nat Commun 2023; 14:6697. [PMID: 37914696 PMCID: PMC10620219 DOI: 10.1038/s41467-023-42470-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 10/11/2023] [Indexed: 11/03/2023] Open
Abstract
Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switching in response to electrical inputs due to synapse-like changes in conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how the neuromorphic dynamics generated by NWNs can be harnessed for temporal learning tasks. This study extends these findings further by demonstrating online learning from spatiotemporal dynamical features using image classification and sequence memory recall tasks implemented on an NWN device. Applied to the MNIST handwritten digit classification task, online dynamical learning with the NWN device achieves an overall accuracy of 93.4%. Additionally, we find a correlation between the classification accuracy of individual digit classes and mutual information. The sequence memory task reveals how memory patterns embedded in the dynamical features enable online learning and recall of a spatiotemporal sequence pattern. Overall, these results provide proof-of-concept of online learning from spatiotemporal dynamics using NWNs and further elucidate how memory can enhance learning.
Collapse
Affiliation(s)
- Ruomin Zhu
- School of Physics, The University of Sydney, Sydney, NSW, Australia.
| | - Sam Lilak
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, US
| | - Alon Loeffler
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Joseph Lizier
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Adam Stieg
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, US.
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.
| | - James Gimzewski
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, US.
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, US.
- WPI Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.
- Research Center for Neuromorphic AI Hardware, Kyutech, Kitakyushu, Japan.
| | - Zdenka Kuncic
- School of Physics, The University of Sydney, Sydney, NSW, Australia.
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
- The University of Sydney Nano Institute, Sydney, NSW, Australia.
| |
Collapse
|
5
|
Li J, Abbas H, Ang DS, Ali A, Ju X. Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era. NANOSCALE HORIZONS 2023; 8:1456-1484. [PMID: 37615055 DOI: 10.1039/d3nh00180f] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.
Collapse
Affiliation(s)
- Jiayi Li
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Haider Abbas
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Diing Shenp Ang
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Asif Ali
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Xin Ju
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634
| |
Collapse
|
6
|
Park J, Ha S, Yu T, Neftci E, Cauwenberghs G. A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane dynamics. Front Neurosci 2023; 17:1198306. [PMID: 37700751 PMCID: PMC10493285 DOI: 10.3389/fnins.2023.1198306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 07/07/2023] [Indexed: 09/14/2023] Open
Abstract
Neuromorphic cognitive computing offers a bio-inspired means to approach the natural intelligence of biological neural systems in silicon integrated circuits. Typically, such circuits either reproduce biophysical neuronal dynamics in great detail as tools for computational neuroscience, or abstract away the biology by simplifying the functional forms of neural computation in large-scale systems for machine intelligence with high integration density and energy efficiency. Here we report a hybrid which offers biophysical realism in the emulation of multi-compartmental neuronal network dynamics at very large scale with high implementation efficiency, and yet with high flexibility in configuring the functional form and the network topology. The integrate-and-fire array transceiver (IFAT) chip emulates the continuous-time analog membrane dynamics of 65 k two-compartment neurons with conductance-based synapses. Fired action potentials are registered as address-event encoded output spikes, while the four types of synapses coupling to each neuron are activated by address-event decoded input spikes for fully reconfigurable synaptic connectivity, facilitating virtual wiring as implemented by routing address-event spikes externally through synaptic routing table. Peak conductance strength of synapse activation specified by the address-event input spans three decades of dynamic range, digitally controlled by pulse width and amplitude modulation (PWAM) of the drive voltage activating the log-domain linear synapse circuit. Two nested levels of micro-pipelining in the IFAT architecture improve both throughput and efficiency of synaptic input. This two-tier micro-pipelining results in a measured sustained peak throughput of 73 Mspikes/s and overall chip-level energy efficiency of 22 pJ/spike. Non-uniformity in digitally encoded synapse strength due to analog mismatch is mitigated through single-point digital offset calibration. Combined with the flexibly layered and recurrent synaptic connectivity provided by hierarchical address-event routing of registered spike events through external memory, the IFAT lends itself to efficient large-scale emulation of general biophysical spiking neural networks, as well as rate-based mapping of rectified linear unit (ReLU) neural activations.
Collapse
Affiliation(s)
- Jongkil Park
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Sohmyung Ha
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Theodore Yu
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Electrical and Computer Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
| | - Emre Neftci
- Peter Grünberg Institute, Forschungszentrum Jülich, RWTH, Aachen, Germany
| | - Gert Cauwenberghs
- Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States
| |
Collapse
|
7
|
Dey S, Dimitrov AG. Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware. Front Neurosci 2023; 17:1198282. [PMID: 37694108 PMCID: PMC10484528 DOI: 10.3389/fnins.2023.1198282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
With the ongoing growth in the field of neuro-inspired computing, newly arriving computational architectures demand extensive validation and testing against existing benchmarks to establish their competence and value. In our work, we break down the validation step into two parts-(1) establishing a methodological and numerical groundwork to establish a comparison between neuromorphic and conventional platforms and, (2) performing a sensitivity analysis on the obtained model regime to assess its robustness. We study the neuronal dynamics based on the Leaky Integrate and Fire (LIF) model, which is built upon data from the mouse visual cortex spanning a set of anatomical and physiological constraints. Intel Corp.'s first neuromorphic chip "Loihi" serves as our neuromorphic platform and results on it are validated against the classical simulations. After setting up a model that allows a seamless mapping between the Loihi and the classical simulations, we find that Loihi replicates classical simulations very efficiently with high precision. This model is then subjected to the second phase of validation, through sensitivity analysis, by assessing the impact on the cost function as values of the significant model parameters are varied. The work is done in two steps-(1) assessing the impact while changing one parameter at a time, (2) assessing the impact while changing two parameters at a time. We observe that the model is quite robust for majority of the parameters with slight change in the cost function. We also identify a subset of the model parameters changes which make the model more sensitive and thus, need to be defined more precisely.
Collapse
Affiliation(s)
- Srijanie Dey
- Department of Mathematics, Washington State University, Vancouver, WA, United States
| | - Alexander G. Dimitrov
- Department of Mathematics, Washington State University, Vancouver, WA, United States
| |
Collapse
|
8
|
Dallmann CJ, Dickerson BH, Simpson JH, Wyart C, Jayaram K. Mechanosensory Control of Locomotion in Animals and Robots: Moving Forward. Integr Comp Biol 2023; 63:450-463. [PMID: 37279901 PMCID: PMC10445419 DOI: 10.1093/icb/icad057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/10/2023] [Accepted: 05/24/2023] [Indexed: 06/08/2023] Open
Abstract
While animals swim, crawl, walk, and fly with apparent ease, building robots capable of robust locomotion remains a significant challenge. In this review, we draw attention to mechanosensation-the sensing of mechanical forces generated within and outside the body-as a key sense that enables robust locomotion in animals. We discuss differences between mechanosensation in animals and current robots with respect to (1) the encoding properties and distribution of mechanosensors and (2) the integration and regulation of mechanosensory feedback. We argue that robotics would benefit greatly from a detailed understanding of these aspects in animals. To that end, we highlight promising experimental and engineering approaches to study mechanosensation, emphasizing the mutual benefits for biologists and engineers that emerge from moving forward together.
Collapse
Affiliation(s)
- Chris J Dallmann
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Bradley H Dickerson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Julie H Simpson
- Department of Molecular, Cellular, and Developmental Biology and Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA
| | - Claire Wyart
- Institut du Cerveau et de la Moelle épinière (ICM), Sorbonne Université, Paris 75005, France
| | - Kaushik Jayaram
- Paul M Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
Collapse
Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
| |
Collapse
|
11
|
Gautam A, Kohno T. Adaptive STDP-based on-chip spike pattern detection. Front Neurosci 2023; 17:1203956. [PMID: 37521704 PMCID: PMC10374023 DOI: 10.3389/fnins.2023.1203956] [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: 04/11/2023] [Accepted: 06/15/2023] [Indexed: 08/01/2023] Open
Abstract
A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated via numerical simulation that adaptive STDP (using only 4-bit fixed-point synaptic efficacy) performs similarly to STDP learning (using 64-bit floating-point precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry.
Collapse
|
12
|
Dumont NSY, Furlong PM, Orchard J, Eliasmith C. Exploiting semantic information in a spiking neural SLAM system. Front Neurosci 2023; 17:1190515. [PMID: 37476829 PMCID: PMC10354246 DOI: 10.3389/fnins.2023.1190515] [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: 03/21/2023] [Accepted: 06/16/2023] [Indexed: 07/22/2023] Open
Abstract
To navigate in new environments, an animal must be able to keep track of its position while simultaneously creating and updating an internal map of features in the environment, a problem formulated as simultaneous localization and mapping (SLAM) in the field of robotics. This requires integrating information from different domains, including self-motion cues, sensory, and semantic information. Several specialized neuron classes have been identified in the mammalian brain as being involved in solving SLAM. While biology has inspired a whole class of SLAM algorithms, the use of semantic information has not been explored in such work. We present a novel, biologically plausible SLAM model called SSP-SLAM-a spiking neural network designed using tools for large scale cognitive modeling. Our model uses a vector representation of continuous spatial maps, which can be encoded via spiking neural activity and bound with other features (continuous and discrete) to create compressed structures containing semantic information from multiple domains (e.g., spatial, temporal, visual, conceptual). We demonstrate that the dynamics of these representations can be implemented with a hybrid oscillatory-interference and continuous attractor network of head direction cells. The estimated self-position from this network is used to learn an associative memory between semantically encoded landmarks and their positions, i.e., an environment map, which is used for loop closure. Our experiments demonstrate that environment maps can be learned accurately and their use greatly improves self-position estimation. Furthermore, grid cells, place cells, and object vector cells are observed by this model. We also run our path integrator network on the NengoLoihi neuromorphic emulator to demonstrate feasibility for a full neuromorphic implementation for energy efficient SLAM.
Collapse
|
13
|
Tominov RV, Vakulov ZE, Avilov VI, Shikhovtsov IA, Varganov VI, Kazantsev VB, Gupta LR, Prakash C, Smirnov VA. Approaches for Memristive Structures Using Scratching Probe Nanolithography: Towards Neuromorphic Applications. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:nano13101583. [PMID: 37242000 DOI: 10.3390/nano13101583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023]
Abstract
This paper proposes two different approaches to studying resistive switching of oxide thin films using scratching probe nanolithography of atomic force microscopy (AFM). These approaches allow us to assess the effects of memristor size and top-contact thickness on resistive switching. For that purpose, we investigated scratching probe nanolithography regimes using the Taguchi method, which is known as a reliable method for improving the reliability of the result. The AFM parameters, including normal load, scratch distance, probe speed, and probe direction, are optimized on the photoresist thin film by the Taguchi method. As a result, the pinholes with diameter ranged from 25.4 ± 2.2 nm to 85.1 ± 6.3 nm, and the groove array with a depth of 40.5 ± 3.7 nm and a roughness at the bottom of less than a few nanometers was formed. Then, based on the Si/TiN/ZnO/photoresist structures, we fabricated and investigated memristors with different spot sizes and TiN top contact thickness. As a result, the HRS/LRS ratio, USET, and ILRS are well controlled for a memristor size from 27 nm to 83 nm and ranged from ~8 to ~128, from 1.4 ± 0.1 V to 1.8 ± 0.2 V, and from (1.7 ± 0.2) × 10-10 A to (4.2 ± 0.6) × 10-9 A, respectively. Furthermore, the HRS/LRS ratio and USET are well controlled at a TiN top contact thickness from 8.3 ± 1.1 nm to 32.4 ± 4.2 nm and ranged from ~22 to ~188 and from 1.15 ± 0.05 V to 1.62 ± 0.06 V, respectively. The results can be used in the engineering and manufacturing of memristive structures for neuromorphic applications of brain-inspired artificial intelligence systems.
Collapse
Affiliation(s)
- Roman V Tominov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Department of Radioelectronics and Nanoelectronics, Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Zakhar E Vakulov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Vadim I Avilov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Ivan A Shikhovtsov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Vadim I Varganov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| | - Victor B Kazantsev
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
| | - Lovi Raj Gupta
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Division of Research and Development, Lovely Professional University, Phagwara 144411, Panjab, India
| | - Chander Prakash
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, Panjab, India
| | - Vladimir A Smirnov
- Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
- Department of Radioelectronics and Nanoelectronics, Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog 347922, Russia
| |
Collapse
|
14
|
Michaelis C, Lehr AB, Oed W, Tetzlaff C. Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian. Front Neuroinform 2022; 16:1015624. [PMID: 36439945 PMCID: PMC9682266 DOI: 10.3389/fninf.2022.1015624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/12/2022] [Indexed: 11/11/2022] Open
Abstract
Developing intelligent neuromorphic solutions remains a challenging endeavor. It requires a solid conceptual understanding of the hardware's fundamental building blocks. Beyond this, accessible and user-friendly prototyping is crucial to speed up the design pipeline. We developed an open source Loihi emulator based on the neural network simulator Brian that can easily be incorporated into existing simulation workflows. We demonstrate errorless Loihi emulation in software for a single neuron and for a recurrently connected spiking neural network. On-chip learning is also reviewed and implemented, with reasonable discrepancy due to stochastic rounding. This work provides a coherent presentation of Loihi's computational unit and introduces a new, easy-to-use Loihi prototyping package with the aim to help streamline conceptualization and deployment of new algorithms.
Collapse
Affiliation(s)
- Carlo Michaelis
- Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany
- *Correspondence: Carlo Michaelis
| | - Andrew B. Lehr
- Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany
| | - Winfried Oed
- Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany
| | - Christian Tetzlaff
- Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany
| |
Collapse
|
15
|
Kleijnen R, Robens M, Schiek M, van Waasen S. Verification of a neuromorphic computing network simulator using experimental traffic data. Front Neurosci 2022; 16:958343. [PMID: 36003958 PMCID: PMC9393391 DOI: 10.3389/fnins.2022.958343] [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: 05/31/2022] [Accepted: 07/08/2022] [Indexed: 12/05/2022] Open
Abstract
Simulations are a powerful tool to explore the design space of hardware systems, offering the flexibility to analyze different designs by simply changing parameters within the simulator setup. A precondition for the effectiveness of this methodology is that the simulation results accurately represent the real system. In a previous study, we introduced a simulator specifically designed to estimate the network load and latency to be observed on the connections in neuromorphic computing (NC) systems. The simulator was shown to be especially valuable in the case of large scale heterogeneous neural networks (NNs). In this work, we compare the network load measured on a SpiNNaker board running a NN in different configurations reported in the literature to the results obtained with our simulator running the same configurations. The simulated network loads show minor differences from the values reported in the ascribed publication but fall within the margin of error, considering the generation of the test case NN based on statistics that introduced variations. Having shown that the network simulator provides representative results for this type of —biological plausible—heterogeneous NNs, it also paves the way to further use of the simulator for more complex network analyses.
Collapse
Affiliation(s)
- Robert Kleijnen
- Central Institute of Engineering, Electronics and Analytics–Electronic Systems (ZEA-2), Forschungszentrum Jülich GmbH, Julich, Germany
- *Correspondence: Robert Kleijnen
| | - Markus Robens
- Central Institute of Engineering, Electronics and Analytics–Electronic Systems (ZEA-2), Forschungszentrum Jülich GmbH, Julich, Germany
| | - Michael Schiek
- Central Institute of Engineering, Electronics and Analytics–Electronic Systems (ZEA-2), Forschungszentrum Jülich GmbH, Julich, Germany
| | - Stefan van Waasen
- Central Institute of Engineering, Electronics and Analytics–Electronic Systems (ZEA-2), Forschungszentrum Jülich GmbH, Julich, Germany
- Faculty of Engineering, Communication Systems, University of Duisburg-Essen, Duisburg, Germany
| |
Collapse
|
16
|
Müller E, Schmitt S, Mauch C, Billaudelle S, Grübl A, Güttler M, Husmann D, Ilmberger J, Jeltsch S, Kaiser J, Klähn J, Kleider M, Koke C, Montes J, Müller P, Partzsch J, Passenberg F, Schmidt H, Vogginger B, Weidner J, Mayr C, Schemmel J. The operating system of the neuromorphic BrainScaleS-1 system. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
17
|
Udaya Mohanan K, Cho S, Park BG. Optimization of the structural complexity of artificial neural network for hardware-driven neuromorphic computing application. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03783-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractThis work focuses on the optimization of the structural complexity of a single-layer feedforward neural network (SLFN) for neuromorphic hardware implementation. The singular value decomposition (SVD) method is used for the determination of the effective number of neurons in the hidden layer for Modified National Institute of Standards and Technology (MNIST) dataset classification. The proposed method is also verified on a SLFN using weights derived from a synaptic transistor device. The effectiveness of this methodology in estimating the reduced number of neurons in the hidden layer makes this method highly useful in optimizing complex neural network architectures for their hardware realization.
Collapse
|
18
|
Yang S, Wang J, Hao X, Li H, Wei X, Deng B, Loparo KA. BiCoSS: Toward Large-Scale Cognition Brain With Multigranular Neuromorphic Architecture. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:2801-2815. [PMID: 33428574 DOI: 10.1109/tnnls.2020.3045492] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the corresponding computing platforms. Neuromorphic engineering provides approaches to high-performance biologically plausible computational paradigms inspired by neural systems. In this article, we present a biological-inspired cognitive supercomputing system (BiCoSS) that integrates multiple granules (GRs) of SNNs to realize a hybrid compatible neuromorphic platform. A scalable hierarchical heterogeneous multicore architecture is presented, and a synergistic routing scheme for hybrid neural information is proposed. The BiCoSS system can accommodate different levels of GRs and biological plausibility of SNN models in an efficient and scalable manner. Over four million neurons can be realized on BiCoSS with a power efficiency of 2.8k larger than the GPU platform, and the average latency of BiCoSS is 3.62 and 2.49 times higher than conventional architectures of digital neuromorphic systems. For the verification, BiCoSS is used to replicate various biological cognitive activities, including motor learning, action selection, context-dependent learning, and movement disorders. Comprehensively considering the programmability, biological plausibility, learning capability, computational power, and scalability, BiCoSS is shown to outperform the alternative state-of-the-art works for large-scale SNN, while its real-time computational capability enables a wide range of potential applications.
Collapse
|
19
|
High-Performance Graphene FET Integrated Front-End Amplifier Using Pseudo-resistor Technique for Neuro-prosthetic Diagnosis. BIOCHIP JOURNAL 2022. [DOI: 10.1007/s13206-022-00060-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
20
|
Dey S, Dimitrov A. Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi. Front Neuroinform 2022; 16:883360. [PMID: 35712458 PMCID: PMC9197133 DOI: 10.3389/fnins.2022.883360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, we ought to perform rigorous simulation and consequent validation of neuromorphic models against their conventional implementations. In this work, we lay out the numeric groundwork to enable a comparison between neuromorphic and conventional platforms. “Loihi”—Intel's fifth generation neuromorphic chip, which is based on the idea of Spiking Neural Networks (SNNs) emulating the activity of neurons in the brain, serves as our neuromorphic platform. The work here focuses on Leaky Integrate and Fire (LIF) models based on neurons in the mouse primary visual cortex and matched to a rich data set of anatomical, physiological and behavioral constraints. Simulations on classical hardware serve as the validation platform for the neuromorphic implementation. We find that Loihi replicates classical simulations very efficiently with high precision. As a by-product, we also investigate Loihi's potential in terms of scalability and performance and find that it scales notably well in terms of run-time performance as the simulated networks become larger.
Collapse
Affiliation(s)
- Srijanie Dey
- Department of Mathematics, Washington State University, Vancouver, WA, United States
| | - Alexander Dimitrov
- Department of Mathematics, Washington State University, Vancouver, WA, United States
| |
Collapse
|
21
|
Liu F, Deswal S, Christou A, Sandamirskaya Y, Kaboli M, Dahiya R. Neuro-inspired electronic skin for robots. Sci Robot 2022; 7:eabl7344. [PMID: 35675450 DOI: 10.1126/scirobotics.abl7344] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.
Collapse
Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | | | - Mohsen Kaboli
- Department of Research, New Technologies, Innovation, BMW Group, Parkring 19, 85748 Garching bei Munchen, Germany.,Cognitive Robotics and Tactile Intelligence Group, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| |
Collapse
|
22
|
Gupta S, Chakraborty S, Thakur CS. Neuromorphic Time-Multiplexed Reservoir Computing With On-the-Fly Weight Generation for Edge Devices. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:2676-2685. [PMID: 34125686 DOI: 10.1109/tnnls.2021.3085165] [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
The human brain has evolved to perform complex and computationally expensive cognitive tasks, such as audio-visual perception and object detection, with ease. For instance, the brain can recognize speech in different dialects and perform other cognitive tasks, such as attention, memory, and motor control, with just 20 W of power consumption. Taking inspiration from neural systems, we propose a low-power neuromorphic hardware architecture to perform classification on temporal data at the edge. The proposed architecture uses a neuromorphic cochlea model for feature extraction and reservoir computing (RC) framework as a classifier. In the proposed hardware architecture, the RC framework is modified for on-the-fly generation of reservoir connectivity, along with binary feedforward and reservoir weights. Also, a large reservoir is split into multiple small reservoirs for efficient use of hardware resources. These modifications reduce the computational and memory resources required, thereby resulting in a lower power budget. The proposed classifier is validated for speech and human activity recognition (HAR) tasks. We have prototyped our hardware architecture using Intel's cyclone-10 low-power series field-programmable gate array (FPGA), consuming only 4790 logic elements (LEs) and 34.9-kB memory, making it a perfect candidate for edge computing applications. Moreover, we have implemented a complete system for speech recognition with the feature extraction block (cochlea model) and the proposed classifier, utilizing 15 532 LEs and 38.4-kB memory. By using the proposed idea of multiple small reservoirs along with on-the-fly generation of reservoir binary weights, our architecture can reduce the power consumption and memory requirement by order of magnitude compared to existing FPGA models for speech recognition tasks with similar complexity.
Collapse
|
23
|
Dey S, Dimitrov A. Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi. Front Neuroinform 2022; 16:883360. [PMID: 36726406 PMCID: PMC9886005 DOI: 10.3389/fninf.2022.883360] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/25/2022] [Indexed: 01/18/2023] Open
Abstract
Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, we ought to perform rigorous simulation and consequent validation of neuromorphic models against their conventional implementations. In this work, we lay out the numeric groundwork to enable a comparison between neuromorphic and conventional platforms. "Loihi"-Intel's fifth generation neuromorphic chip, which is based on the idea of Spiking Neural Networks (SNNs) emulating the activity of neurons in the brain, serves as our neuromorphic platform. The work here focuses on Leaky Integrate and Fire (LIF) models based on neurons in the mouse primary visual cortex and matched to a rich data set of anatomical, physiological and behavioral constraints. Simulations on classical hardware serve as the validation platform for the neuromorphic implementation. We find that Loihi replicates classical simulations very efficiently with high precision. As a by-product, we also investigate Loihi's potential in terms of scalability and performance and find that it scales notably well in terms of run-time performance as the simulated networks become larger.
Collapse
|
24
|
A Network Simulator for the Estimation of Bandwidth Load and Latency Created by Heterogeneous Spiking Neural Networks on Neuromorphic Computing Communication Networks. JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS 2022. [DOI: 10.3390/jlpea12020023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Accelerated simulations of biological neural networks are in demand to discover the principals of biological learning. Novel many-core simulation platforms, e.g., SpiNNaker, BrainScaleS and Neurogrid, allow one to study neuron behavior in the brain at an accelerated rate, with a high level of detail. However, they do not come anywhere near simulating the human brain. The massive amount of spike communication has turned out to be a bottleneck. We specifically developed a network simulator to analyze in high detail the network loads and latencies caused by different network topologies and communication protocols in neuromorphic computing communication networks. This simulator allows simulating the impacts of heterogeneous neural networks and evaluating neuron mapping algorithms, which is a unique feature among state-of-the-art network models and simulators. The simulator was cross-checked by comparing the results of a homogeneous neural network-based run with corresponding bandwidth load results from comparable works. Additionally, the increased level of detail achieved by the new simulator is presented. Then, we show the impact heterogeneous connectivity can have on the network load, first for a small-scale test case, and later for a large-scale test case, and how different neuron mapping algorithms can influence this effect. Finally, we look at the latency estimations performed by the simulator for different mapping algorithms, and the impact of the node size.
Collapse
|
25
|
Abstract
New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realize this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing?
Collapse
|
26
|
Deiana AM, Tran N, Agar J, Blott M, Di Guglielmo G, Duarte J, Harris P, Hauck S, Liu M, Neubauer MS, Ngadiuba J, Ogrenci-Memik S, Pierini M, Aarrestad T, Bähr S, Becker J, Berthold AS, Bonventre RJ, Müller Bravo TE, Diefenthaler M, Dong Z, Fritzsche N, Gholami A, Govorkova E, Guo D, Hazelwood KJ, Herwig C, Khan B, Kim S, Klijnsma T, Liu Y, Lo KH, Nguyen T, Pezzullo G, Rasoulinezhad S, Rivera RA, Scholberg K, Selig J, Sen S, Strukov D, Tang W, Thais S, Unger KL, Vilalta R, von Krosigk B, Wang S, Warburton TK. Applications and Techniques for Fast Machine Learning in Science. Front Big Data 2022; 5:787421. [PMID: 35496379 PMCID: PMC9041419 DOI: 10.3389/fdata.2022.787421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/31/2020] [Indexed: 01/10/2023] Open
Abstract
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
Collapse
Affiliation(s)
| | - Nhan Tran
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Joshua Agar
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, United States
| | | | | | - Javier Duarte
- Department of Physics, University of California, San Diego, San Diego, CA, United States
| | - Philip Harris
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Scott Hauck
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Mia Liu
- Department of Physics and Astronomy, Purdue University, West Lafayette, IN, United States
| | - Mark S. Neubauer
- Department of Physics, University of Illinois Urbana-Champaign, Champaign, IL, United States
| | | | - Seda Ogrenci-Memik
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Maurizio Pierini
- European Organization for Nuclear Research (CERN), Meyrin, Switzerland
| | - Thea Aarrestad
- European Organization for Nuclear Research (CERN), Meyrin, Switzerland
| | - Steffen Bähr
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jürgen Becker
- Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Anne-Sophie Berthold
- Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
| | | | - Tomás E. Müller Bravo
- Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
| | - Markus Diefenthaler
- Thomas Jefferson National Accelerator Facility, Newport News, VA, United States
| | - Zhen Dong
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Nick Fritzsche
- Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
| | - Amir Gholami
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | | | - Dongning Guo
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | | | - Christian Herwig
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Babar Khan
- Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
| | - Sehoon Kim
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Thomas Klijnsma
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Yaling Liu
- Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Kin Ho Lo
- Department of Physics, University of Florida, Gainesville, FL, United States
| | - Tri Nguyen
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | | | - Ryan A. Rivera
- Fermi National Accelerator Laboratory, Batavia, IL, United States
| | - Kate Scholberg
- Department of Physics, Duke University, Durham, NC, United States
| | | | - Sougata Sen
- Birla Institute of Technology and Science, Pilani, India
| | - Dmitri Strukov
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States
| | - William Tang
- Department of Physics, Princeton University, Princeton, NJ, United States
| | - Savannah Thais
- Department of Physics, Princeton University, Princeton, NJ, United States
| | | | - Ricardo Vilalta
- Department of Computer Science, University of Houston, Houston, TX, United States
| | - Belina von Krosigk
- Karlsruhe Institute of Technology, Karlsruhe, Germany
- Department of Physics, Universität Hamburg, Hamburg, Germany
| | - Shen Wang
- Department of Physics, University of Florida, Gainesville, FL, United States
| | - Thomas K. Warburton
- Department of Physics and Astronomy, Iowa State University, Ames, IA, United States
| |
Collapse
|
27
|
Annamalai L, Ramanathan V, Thakur CS. Event-LSTM: An Unsupervised and Asynchronous Learning-Based Representation for Event-Based Data. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3151426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
28
|
Vogginger B, Kreutz F, López-Randulfe J, Liu C, Dietrich R, Gonzalez HA, Scholz D, Reeb N, Auge D, Hille J, Arsalan M, Mirus F, Grassmann C, Knoll A, Mayr C. Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges. Front Neurosci 2022; 16:851774. [PMID: 35431782 PMCID: PMC9012531 DOI: 10.3389/fnins.2022.851774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.
Collapse
Affiliation(s)
- Bernhard Vogginger
- Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany
- *Correspondence: Bernhard Vogginger
| | - Felix Kreutz
- Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany
- Infineon Technologies Dresden GmbH & Co., KG, Dresden, Germany
| | | | - Chen Liu
- Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany
| | - Robin Dietrich
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Hector A. Gonzalez
- Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany
| | - Daniel Scholz
- Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany
- Infineon Technologies Dresden GmbH & Co., KG, Dresden, Germany
| | - Nico Reeb
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Daniel Auge
- Department of Informatics, Technical University of Munich, Munich, Germany
- Infineon Technologies AG, Munich, Germany
| | - Julian Hille
- Department of Informatics, Technical University of Munich, Munich, Germany
- Infineon Technologies AG, Munich, Germany
| | | | - Florian Mirus
- BMW Group, Research, New Technologies, Garching, Germany
| | | | - Alois Knoll
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Christian Mayr
- Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany
- Centre for Tactile Internet (CeTI) With Human-In-The-Loop, Cluster of Excellence, Technische Universität Dresden, Dresden, Germany
| |
Collapse
|
29
|
Yu Q, Li S, Tang H, Wang L, Dang J, Tan KC. Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:1364-1376. [PMID: 32356771 DOI: 10.1109/tcyb.2020.2984888] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing. However, efficient processing and learning of discrete spikes still remain a challenging problem. In this article, we make our contributions toward this direction. A simplified spiking neuron model is first introduced with the effects of both synaptic input and firing output on the membrane potential being modeled with an impulse function. An event-driven scheme is then presented to further improve the processing efficiency. Based on the neuron model, we propose two new multispike learning rules which demonstrate better performance over other baselines on various tasks, including association, classification, and feature detection. In addition to efficiency, our learning rules demonstrate high robustness against the strong noise of different types. They can also be generalized to different spike coding schemes for the classification task, and notably, the single neuron is capable of solving multicategory classifications with our learning rules. In the feature detection task, we re-examine the ability of unsupervised spike-timing-dependent plasticity with its limitations being presented, and find a new phenomenon of losing selectivity. In contrast, our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied. Moreover, our rules cannot only detect features but also discriminate them. The improved performance of our methods would contribute to neuromorphic computing as a preferable choice.
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Abstract
Neuromorphic systems aim to accomplish efficient computation in electronics by mirroring neurobiological principles. Taking advantage of neuromorphic technologies requires effective learning algorithms capable of instantiating high-performing neural networks, while also dealing with inevitable manufacturing variations of individual components, such as memristors or analog neurons. We present a learning framework resulting in bioinspired spiking neural networks with high performance, low inference latency, and sparse spike-coding schemes, which also self-corrects for device mismatch. We validate our approach on the BrainScaleS-2 analog spiking neuromorphic system, demonstrating state-of-the-art accuracy, low latency, and energy efficiency. Our work sketches a path for building powerful neuromorphic processors that take advantage of emerging analog technologies. To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy efficiency. However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. Surrogate gradient learning has emerged as a promising training strategy for spiking networks, but its applicability for analog neuromorphic systems has not been demonstrated. Here, we demonstrate surrogate gradient learning on the BrainScaleS-2 analog neuromorphic system using an in-the-loop approach. We show that learning self-corrects for device mismatch, resulting in competitive spiking network performance on both vision and speech benchmarks. Our networks display sparse spiking activity with, on average, less than one spike per hidden neuron and input, perform inference at rates of up to 85,000 frames per second, and consume less than 200 mW. In summary, our work sets several benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms.
Collapse
|
32
|
Choi HW, Song KW, Kim SH, Nguyen KT, Eadi SB, Kwon HM, Lee HD. Zinc oxide and indium-gallium-zinc-oxide bi-layer synaptic device with highly linear long-term potentiation and depression characteristics. Sci Rep 2022; 12:1259. [PMID: 35075173 PMCID: PMC8786833 DOI: 10.1038/s41598-022-05150-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/04/2022] [Indexed: 11/09/2022] Open
Abstract
The electrical properties, resistive switching behavior, and long-term potentiation/depression (LTP/LTD) in a single indium-gallium-zinc-oxide (IGZO) and bi-layer IGZO/ZnO (ZnO: zinc oxide) memristors were investigated for synapse application. The use of the oxide bi-layer memristors, in particular, improved electrical properties such as stability, memristor reliability, and an increase in synaptic weight states. The set voltage of bi-layer IGZO/ZnO memristors was 0.9 V, and the reset voltage was around - 0.7 V, resulting in a low-operating voltage for neuromorphic systems. The oxygen vacancies in the X-ray photoelectron spectroscopy analysis played a role in the modulation of the high-resistance state (HRS) (oxygen-deficient) and the low-resistance state (oxygen-rich) region. The VRESET of the bi-layer IGZO/ZnO memristors was lower than that of a single IGZO, which implied that oxygen-vacancy filaments could be easily ruptured due to the higher oxygen vacancy peak HRS layer. The nonlinearity of the LTP and LTD characteristics in a bi-layer IGZO/ZnO memristor was 6.77% and 11.49%, respectively, compared to those of 20.03% and 51.1% in a single IGZO memristor, respectively. Therefore, the extra ZnO layer in the bi-layer memristor with IGZO was potentially significant and essential to achieve a small set voltage and a reset voltage, and the switching behavior to form the conductive path.
Collapse
Affiliation(s)
- Hyun-Woong Choi
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Ki-Woo Song
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Seong-Hyun Kim
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Kim Thanh Nguyen
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Sunil Babu Eadi
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Hyuk-Min Kwon
- Department of Semiconductor Processing Equipment, Semiconductor Convergence Campus of Korea Polytechnic College, 41-12, Songwon-Gil, Kongdo-Eup, Anseong, Kyunggi-Do, Republic of Korea.
| | - Hi-Deok Lee
- Department of Electronics Engineering, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea.
| |
Collapse
|
33
|
Schuman CD, Kulkarni SR, Parsa M, Mitchell JP, Date P, Kay B. Opportunities for neuromorphic computing algorithms and applications. NATURE COMPUTATIONAL SCIENCE 2022; 2:10-19. [PMID: 38177712 DOI: 10.1038/s43588-021-00184-y] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 12/07/2021] [Indexed: 01/06/2024]
Abstract
Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.
Collapse
Affiliation(s)
- Catherine D Schuman
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA.
| | - Shruti R Kulkarni
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Maryam Parsa
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
| | - J Parker Mitchell
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Prasanna Date
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Bill Kay
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| |
Collapse
|
34
|
Bagheriye L, Kwisthout J. Brain-Inspired Hardware Solutions for Inference in Bayesian Networks. Front Neurosci 2021; 15:728086. [PMID: 34924925 PMCID: PMC8677599 DOI: 10.3389/fnins.2021.728086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 10/11/2021] [Indexed: 11/23/2022] Open
Abstract
The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating-point operations. A departure from conventional computing systems to make use of the high parallelism of Bayesian inference has attracted recent attention, particularly in the hardware implementation of Bayesian networks. These efforts lead to several implementations ranging from digital circuits, mixed-signal circuits, to analog circuits by leveraging new emerging nonvolatile devices. Several stochastic computing architectures using Bayesian stochastic variables have been proposed, from FPGA-like architectures to brain-inspired architectures such as crossbar arrays. This comprehensive review paper discusses different hardware implementations of Bayesian networks considering different devices, circuits, and architectures, as well as a more futuristic overview to solve existing hardware implementation problems.
Collapse
Affiliation(s)
- Leila Bagheriye
- Foundations of Natural and Stochastic Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | | |
Collapse
|
35
|
Chevtchenko SF, Ludermir TB. Combining STDP and binary networks for reinforcement learning from images and sparse rewards. Neural Netw 2021; 144:496-506. [PMID: 34601362 DOI: 10.1016/j.neunet.2021.09.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 11/15/2022]
Abstract
Spiking neural networks (SNNs) aim to replicate energy efficiency, learning speed and temporal processing of biological brains. However, accuracy and learning speed of such networks is still behind reinforcement learning (RL) models based on traditional neural models. This work combines a pre-trained binary convolutional neural network with an SNN trained online through reward-modulated STDP in order to leverage advantages of both models. The spiking network is an extension of its previous version, with improvements in architecture and dynamics to address a more challenging task. We focus on extensive experimental evaluation of the proposed model with optimized state-of-the-art baselines, namely proximal policy optimization (PPO) and deep Q network (DQN). The models are compared on a grid-world environment with high dimensional observations, consisting of RGB images with up to 256 × 256 pixels. The experimental results show that the proposed architecture can be a competitive alternative to deep reinforcement learning (DRL) in the evaluated environment and provide a foundation for more complex future applications of spiking networks.
Collapse
Affiliation(s)
- Sérgio F Chevtchenko
- Centro de Informática - CIn, Universidade Federal de Pernambuco, Av. Jornalista Aníbal Fernandes, s/n, Cidade Universitária, 50.740-560, Brazil.
| | - Teresa B Ludermir
- Centro de Informática - CIn, Universidade Federal de Pernambuco, Av. Jornalista Aníbal Fernandes, s/n, Cidade Universitária, 50.740-560, Brazil.
| |
Collapse
|
36
|
|
37
|
Krause R, van Bavel JJA, Wu C, Vos MA, Nogaret A, Indiveri G. Robust neuromorphic coupled oscillators for adaptive pacemakers. Sci Rep 2021; 11:18073. [PMID: 34508121 PMCID: PMC8433448 DOI: 10.1038/s41598-021-97314-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 08/20/2021] [Indexed: 11/09/2022] Open
Abstract
Neural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently in biologically realistic simulations of spiking neural networks. The advent of mixed-signal analog/digital neuromorphic electronic circuits provides new means for implementing neural coupled oscillators on compact, low-power, spiking neural network hardware platforms. However, their implementation on this noisy, low-precision and inhomogeneous computing substrate raises new challenges with regards to stability and controllability. In this work, we present a robust, spiking neural network model of neural coupled oscillators and validate it with an implementation on a mixed-signal neuromorphic processor. We demonstrate its robustness showing how to reliably control and modulate the oscillator's frequency and phase shift, despite the variability of the silicon synapse and neuron properties. We show how this ultra-low power neural processing system can be used to build an adaptive cardiac pacemaker modulating the heart rate with respect to the respiration phases and compare it with surface ECG and respiratory signal recordings from dogs at rest. The implementation of our model in neuromorphic electronic hardware shows its robustness on a highly variable substrate and extends the toolbox for applications requiring rhythmic outputs such as pacemakers.
Collapse
Affiliation(s)
- Renate Krause
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Joanne J A van Bavel
- Division Heart and Lungs, Department of Medical Physiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Chenxi Wu
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Marc A Vos
- Division Heart and Lungs, Department of Medical Physiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| |
Collapse
|
38
|
Vanattou-Saïfoudine N, Han C, Krause R, Vasilaki E, von der Behrens W, Indiveri G. A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware. Sci Rep 2021; 11:17904. [PMID: 34504155 PMCID: PMC8429557 DOI: 10.1038/s41598-021-97217-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/10/2021] [Indexed: 02/08/2023] Open
Abstract
Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this "neuromorphic engineering" approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.
Collapse
Affiliation(s)
- Natacha Vanattou-Saïfoudine
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
- Department of Computer Science, University of Sheffield, Sheffield, UK.
| | - Chao Han
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Renate Krause
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Eleni Vasilaki
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| |
Collapse
|
39
|
Annamalai L, Chakraborty A, Thakur CS. EvAn: Neuromorphic Event-Based Sparse Anomaly Detection. Front Neurosci 2021; 15:699003. [PMID: 34393712 PMCID: PMC8358807 DOI: 10.3389/fnins.2021.699003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/15/2021] [Indexed: 11/28/2022] Open
Abstract
Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application-video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on the motion history at each pixel. To enable this, we also put forward a generic unsupervised deep learning solution to learn a novel memory surface known as Deep Learning (DL) memory surface. DL memory surface encodes the temporal information readily available from these sensors while retaining the sparsity of event data. Since there is no existing dataset for anomaly detection in the event domain, we also provide an anomaly detection event dataset with a set of anomalies. We empirically validate our anomaly detection architecture, composed of sparse convolutional layers, on this proposed and online dataset. Careful analysis of the anomaly detection network reveals that the presented method results in a massive reduction in computational complexity with good performance compared to previous state-of-the-art conventional frame-based anomaly detection networks.
Collapse
Affiliation(s)
- Lakshmi Annamalai
- Defence Research and Development Organisation (DRDO), Bangalore, India
- Department of Electronic Systems Engineering, Indian Institute of Science (IISc), Bangalore, India
| | - Anirban Chakraborty
- Department of Computational and Data Sciences, Indian Institute of Science (IISc), Bangalore, India
| | - Chetan Singh Thakur
- Department of Electronic Systems Engineering, Indian Institute of Science (IISc), Bangalore, India
| |
Collapse
|
40
|
Du N, Zhao X, Chen Z, Choubey B, Di Ventra M, Skorupa I, Bürger D, Schmidt H. Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs. Front Neurosci 2021; 15:660894. [PMID: 34335153 PMCID: PMC8316997 DOI: 10.3389/fnins.2021.660894] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/17/2021] [Indexed: 11/30/2022] Open
Abstract
Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input.
Collapse
Affiliation(s)
- Nan Du
- Department Nano Device Technology, Fraunhofer Institute for Electronic Nano Systems, Chemnitz, Germany.,Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany.,Department of Quantum Detection, Leibniz Institute of Photonic Technology, Jena, Germany.,Institute for Solid State Physics, Friedrich Schiller University Jena, Jena, Germany
| | - Xianyue Zhao
- Department Nano Device Technology, Fraunhofer Institute for Electronic Nano Systems, Chemnitz, Germany.,Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany
| | - Ziang Chen
- Department Nano Device Technology, Fraunhofer Institute for Electronic Nano Systems, Chemnitz, Germany.,Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany
| | - Bhaskar Choubey
- Analogue Circuits and Image Sensors, Universität Siegen, Siegen, Germany.,Fraunhofer Institute of Microelectronics Circuits & Systems, ATTRACT Group Microelectronic Intelligence, Duisburg, Germany
| | | | - Ilona Skorupa
- Institute of Ion Beam Physics and Materials Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Danilo Bürger
- Department Nano Device Technology, Fraunhofer Institute for Electronic Nano Systems, Chemnitz, Germany.,Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany
| | - Heidemarie Schmidt
- Department Nano Device Technology, Fraunhofer Institute for Electronic Nano Systems, Chemnitz, Germany.,Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany.,Department of Quantum Detection, Leibniz Institute of Photonic Technology, Jena, Germany.,Institute for Solid State Physics, Friedrich Schiller University Jena, Jena, Germany
| |
Collapse
|
41
|
Stapmanns J, Hahne J, Helias M, Bolten M, Diesmann M, Dahmen D. Event-Based Update of Synapses in Voltage-Based Learning Rules. Front Neuroinform 2021; 15:609147. [PMID: 34177505 PMCID: PMC8222618 DOI: 10.3389/fninf.2021.609147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 04/07/2021] [Indexed: 11/13/2022] Open
Abstract
Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. In some learning rules membrane potentials not only influence synaptic weight changes at the time points of spike events but in a continuous manner. In these cases, synapses therefore require information on the full time course of membrane potentials to update their strength which a priori suggests a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks.
Collapse
Affiliation(s)
- Jonas Stapmanns
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, Germany
| | - Jan Hahne
- School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany
| | - Moritz Helias
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, Germany
| | - Matthias Bolten
- School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - David Dahmen
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
| |
Collapse
|
42
|
Molin J, Thakur C, Niebur E, Etienne-Cummings R. A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model With a Hybrid FPGA Implementation. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:580-594. [PMID: 34133287 PMCID: PMC8407057 DOI: 10.1109/tbcas.2021.3089622] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Computing and attending to salient regions of a visual scene is an innate and necessary preprocessing step for both biological and engineered systems performing high-level visual tasks including object detection, tracking, and classification. Computational bandwidth and speed are improved by preferentially devoting computational resources to salient regions of the visual field. The human brain computes saliency effortlessly, but modeling this task in engineered systems is challenging. We first present a neuromorphic dynamic saliency model, which is bottom-up, feed-forward, and based on the notion of proto-objects with neurophysiological spatio-temporal features requiring no training. Our neuromorphic model outperforms state-of-the-art dynamic visual saliency models in predicting human eye fixations (i.e., ground truth saliency). Secondly, we present a hybrid FPGA implementation of the model for real-time applications, capable of processing 112×84 resolution frames at 18.71 Hz running at a 100 MHz clock rate - a 23.77× speedup from the software implementation. Additionally, our fixed-point model of the FPGA implementation yields comparable results to the software implementation.
Collapse
|
43
|
Sharifshazileh M, Burelo K, Sarnthein J, Indiveri G. An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG. Nat Commun 2021; 12:3095. [PMID: 34035249 PMCID: PMC8149394 DOI: 10.1038/s41467-021-23342-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/20/2021] [Indexed: 02/04/2023] Open
Abstract
The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.
Collapse
Affiliation(s)
- Mohammadali Sharifshazileh
- grid.5801.c0000 0001 2156 2780Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland ,grid.412004.30000 0004 0478 9977Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Karla Burelo
- grid.5801.c0000 0001 2156 2780Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland ,grid.412004.30000 0004 0478 9977Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Johannes Sarnthein
- grid.412004.30000 0004 0478 9977Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Giacomo Indiveri
- grid.5801.c0000 0001 2156 2780Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| |
Collapse
|
44
|
Sun F, Lu Q, Feng S, Zhang T. Flexible Artificial Sensory Systems Based on Neuromorphic Devices. ACS NANO 2021; 15:3875-3899. [PMID: 33507725 DOI: 10.1021/acsnano.0c10049] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.
Collapse
Affiliation(s)
- Fuqin Sun
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
- School of Nano Technology and Nano Bionics, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, P. R. China
| | - Qifeng Lu
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
| | - Simin Feng
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
| | - Ting Zhang
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
- School of Nano Technology and Nano Bionics, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, P. R. China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| |
Collapse
|
45
|
Wen Y, Avrillon S, Hernandez-Pavon JC, Kim SJ, Hug F, Pons JL. A convolutional neural network to identify motor units from high-density surface electromyography signals in real time. J Neural Eng 2021; 18. [PMID: 33721852 DOI: 10.1088/1741-2552/abeead] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 03/15/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVES This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. APPROACH The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: 1) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and 2) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. MAIN RESULTS The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2 to 21.4 s/epoch vs. 6.5 to 47.8 s/epoch, respectively) and prediction time (0.04 vs. 0.27 s/sample, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88-0.95). SIGNIFICANCE We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.
Collapse
Affiliation(s)
- Yue Wen
- Legs and Walking Lab, Shirley Ryan AbilityLab, 355 East Erie Street, Chicago, Illinois, 60611-2654, UNITED STATES
| | - Simon Avrillon
- Shirley Ryan AbilityLab, 355 E Erie St, Chicago, Illinois, 60601, UNITED STATES
| | - Julio Cesar Hernandez-Pavon
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, 251 E Huron St, Chicago, Illinois, 60611, UNITED STATES
| | - Sangjoon Jonathan Kim
- Shirley Ryan AbilityLab, 355 E Erie St, Chicago, Illinois, 60611-2654, UNITED STATES
| | - Francois Hug
- Laboratoire 'Motricite, Interactions, Performance', Universite de Nantes, JE 2438 UFRSTAPS,, 25 bis Guy Mollet BP 72206, Nantes, F-44000 France, Nantes, 72206, FRANCE
| | - Jose Luis Pons
- Bioengineering Group, Spanish Research Council, Serrano 117, Arganda del Rey (Madrid), 28006, SPAIN
| |
Collapse
|
46
|
Experimental quantum speed-up in reinforcement learning agents. Nature 2021; 591:229-233. [PMID: 33692560 PMCID: PMC7612051 DOI: 10.1038/s41586-021-03242-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 01/15/2021] [Indexed: 11/10/2022]
Abstract
As the field of artificial intelligence advances, the demand for
algorithms that can learn quickly and efficiently increases. An important
paradigm within artificial intelligence is reinforcement learning [1], where decision-making entities called
agents interact with environments and learn by updating their behaviour based on
obtained feedback. The crucial question for practical applications is how fast
agents learn [2]. While various works have
made use of quantum mechanics to speed up the agent’s decision-making
process [3, 4], a reduction in learning time has not been demonstrated yet.
Here, we present a reinforcement learning experiment where the learning process
of an agent is sped up by utilizing a quantum communication channel with the
environment. We further show that combining this scenario with classical
communication enables the evaluation of such an improvement, and additionally
allows for optimal control of the learning progress. We implement this learning
protocol on a compact and fully tuneable integrated nanophotonic processor. The
device interfaces with telecom-wavelength photons and features a fast active
feedback mechanism, allowing us to demonstrate the agent’s systematic
quantum ad-vantage in a setup that could be readily integrated within future
large-scale quantum communication networks.
Collapse
|
47
|
Krishna A, Mittal D, Virupaksha SG, Nair AR, Narayanan R, Thakur CS. Biomimetic FPGA-based spatial navigation model with grid cells and place cells. Neural Netw 2021; 139:45-63. [PMID: 33677378 DOI: 10.1016/j.neunet.2021.01.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 01/15/2021] [Accepted: 01/25/2021] [Indexed: 12/22/2022]
Abstract
The mammalian spatial navigation system is characterized by an initial divergence of internal representations, with disparate classes of neurons responding to distinct features including location, speed, borders and head direction; an ensuing convergence finally enables navigation and path integration. Here, we report the algorithmic and hardware implementation of biomimetic neural structures encompassing a feed-forward trimodular, multi-layer architecture representing grid-cell, place-cell and decoding modules for navigation. The grid-cell module comprised of neurons that fired in a grid-like pattern, and was built of distinct layers that constituted the dorsoventral span of the medial entorhinal cortex. Each layer was built as an independent continuous attractor network with distinct grid-field spatial scales. The place-cell module comprised of neurons that fired at one or few spatial locations, organized into different clusters based on convergent modular inputs from different grid-cell layers, replicating the gradient in place-field size along the hippocampal dorso-ventral axis. The decoding module, a two-layer neural network that constitutes the convergence of the divergent representations in preceding modules, received inputs from the place-cell module and provided specific coordinates of the navigating object. After vital design optimizations involving all modules, we implemented the tri-modular structure on Zynq Ultrascale+ field-programmable gate array silicon chip, and demonstrated its capacity in precisely estimating the navigational trajectory with minimal overall resource consumption involving a mere 2.92% Look Up Table utilization. Our implementation of a biomimetic, digital spatial navigation system is stable, reliable, reconfigurable, real-time with execution time of about 32 s for 100k input samples (in contrast to 40 minutes on Intel Core i7-7700 CPU with 8 cores clocking at 3.60 GHz) and thus can be deployed for autonomous-robotic navigation without requiring additional sensors.
Collapse
Affiliation(s)
- Adithya Krishna
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
| | - Divyansh Mittal
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
| | - Siri Garudanagiri Virupaksha
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
| | - Abhishek Ramdas Nair
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
| | - Chetan Singh Thakur
- NeuRonICS Lab, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.
| |
Collapse
|
48
|
Stoliar P, Schneegans O, Rozenberg MJ. A Functional Spiking Neural Network of Ultra Compact Neurons. Front Neurosci 2021; 15:635098. [PMID: 33716656 PMCID: PMC7947689 DOI: 10.3389/fnins.2021.635098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 01/22/2021] [Indexed: 11/24/2022] Open
Abstract
We demonstrate that recently introduced ultra-compact neurons (UCN) with a minimal number of components can be interconnected to implement a functional spiking neural network. For concreteness we focus on the Jeffress model, which is a classic neuro-computational model proposed in the 40’s to explain the sound directionality detection by animals and humans. In addition, we introduce a long-axon neuron, whose architecture is inspired by the Hodgkin-Huxley axon delay-line and where the UCNs implement the nodes of Ranvier. We then interconnect two of those neurons to an output layer of UCNs, which detect coincidences between spikes propagating down the long-axons. This functional spiking neural neuron circuit with biological relevance is built from identical UCN blocks, which are simple enough to be made with off-the-shelf electronic components. Our work realizes a new, accessible and affordable physical model platform, where neuroscientists can construct arbitrary mid-size spiking neuronal networks in a lego-block like fashion that work in continuous time. This should enable them to address in a novel experimental manner fundamental questions about the nature of the neural code and to test predictions from mathematical models and algorithms of basic neurobiology research. The present work aims at opening a new experimental field of basic research in Spiking Neural Networks to a potentially large community, which is at the crossroads of neurobiology, dynamical systems, theoretical neuroscience, condensed matter physics, neuromorphic engineering, artificial intelligence, and complex systems.
Collapse
Affiliation(s)
- Pablo Stoliar
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Olivier Schneegans
- Université Paris-Saclay, Sorbonne Université, CentraleSupélec, CNRS, Laboratoire de Génie Électrique et Électronique de Paris, Gif-sur-Yvette, France
| | - Marcelo J Rozenberg
- Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, Orsay, France
| |
Collapse
|
49
|
Frenkel C, Lefebvre M, Bol D. Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks. Front Neurosci 2021; 15:629892. [PMID: 33642986 PMCID: PMC7902857 DOI: 10.3389/fnins.2021.629892] [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: 11/16/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Abstract
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of low-cost adaptive smart sensors at the edge, as they severely constrain memory accesses and entail buffering overhead. In this work, we show that the one-hot-encoded labels provided in supervised classification problems, denoted as targets, can be viewed as a proxy for the error sign. Therefore, their fixed random projections enable a layerwise feedforward training of the hidden layers, thus solving the weight transport and update locking problems while relaxing the computational and memory requirements. Based on these observations, we propose the direct random target projection (DRTP) algorithm and demonstrate that it provides a tradeoff between accuracy and computational cost that is suitable for adaptive edge computing devices.
Collapse
Affiliation(s)
- Charlotte Frenkel
- Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zurich, Switzerland.,ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Martin Lefebvre
- ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - David Bol
- ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| |
Collapse
|
50
|
Abstract
The expeditious development of information technology has led to the rise of artificial intelligence (AI). However, conventional computing systems are prone to volatility, high power consumption, and even delay between the processor and memory, which is referred to as the von Neumann bottleneck, in implementing AI. To address these issues, memristor-based neuromorphic computing systems inspired by the human brain have been proposed. A memristor can store numerous values by changing its resistance and emulate artificial synapses in brain-inspired computing. Here, we introduce six types of memristors classified according to their operation mechanisms: ionic migration, phase change, spin, ferroelectricity, intercalation, and ionic gating. We review how memristor-based neuromorphic computing can learn, infer, and even create, using various artificial neural networks. Finally, the challenges and perspectives in the competing memristor technology for neuromorphic computing systems are discussed.
Collapse
Affiliation(s)
- Seung Ju Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
| | - Sang Bum Kim
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
| |
Collapse
|