1
|
Shu F, Chen W, Chen Y, Liu G. 2D Atomic-Molecular Heterojunctions toward Brainoid Applications. Macromol Rapid Commun 2024:e2400529. [PMID: 39101667 DOI: 10.1002/marc.202400529] [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: 06/29/2024] [Revised: 07/23/2024] [Indexed: 08/06/2024]
Abstract
Brainoid computing using 2D atomic crystals and their heterostructures, by emulating the human brain's remarkable efficiency and minimal energy consumption in information processing, poses a formidable solution to the energy-efficiency and processing speed constraints inherent in the von Neumann architecture. However, conventional 2D material based heterostructures employed in brainoid devices are beset with limitations, performance uniformity, fabrication intricacies, and weak interfacial adhesion, which restrain their broader application. The introduction of novel 2D atomic-molecular heterojunctions (2DAMH), achieved through covalent functionalization of 2D materials with functional molecules, ushers in a new era for brain-like devices by providing both stability and tunability of functionalities. This review chiefly delves into the electronic attributes of 2DAMH derived from the synergy of polymer materials with 2D materials, emphasizing the most recent advancements in their utilization within memristive devices, particularly their potential in replicating the functionality of biological synapses. Despite ongoing challenges pertaining to precision in modification, scalability in production, and the refinement of underlying theories, the proliferation of innovative research is actively pursuing solutions. These endeavors illuminate the vast potential for incorporating 2DAMH within brain-inspired intelligent systems, highlighting the prospect of achieving a more efficient and energy-conserving computing paradigm.
Collapse
Affiliation(s)
- Fan Shu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Weilin Chen
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yu Chen
- School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Gang Liu
- Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| |
Collapse
|
2
|
Kasabov NK, Bahrami H, Doborjeh M, Wang A. Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI. Bioengineering (Basel) 2023; 10:1341. [PMID: 38135932 PMCID: PMC10741022 DOI: 10.3390/bioengineering10121341] [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: 08/18/2023] [Revised: 10/16/2023] [Accepted: 11/14/2023] [Indexed: 12/24/2023] Open
Abstract
Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.
Collapse
Affiliation(s)
- Nikola K. Kasabov
- Knowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand; (H.B.); (M.D.)
- Intelligent Systems Research Center, University of Ulster, Londonderry BT48 7JL, UK
- Institute for Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
- Computer Science and Engineering Department, Dalian University, Dalian 116622, China
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
- Knowledge Engineering Consulting Ltd., Auckland 1071, New Zealand
| | - Helena Bahrami
- Knowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand; (H.B.); (M.D.)
- Core & Innovation, Wine-Searcher, Auckland 0640, New Zealand
- Royal Society Te Apārangi, Wellington 6011, New Zealand
- Research Association New Zealand (RANZ), Auckland 1010, New Zealand
| | - Maryam Doborjeh
- Knowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand; (H.B.); (M.D.)
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
- Faculty of Medical and Health Sciences, University of Auckland, Auckland 1010, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1010, New Zealand
| |
Collapse
|
3
|
Zhou K, Jia Z, Zhou Y, Ding G, Ma XQ, Niu W, Han ST, Zhao J, Zhou Y. Covalent Organic Frameworks for Neuromorphic Devices. J Phys Chem Lett 2023; 14:7173-7192. [PMID: 37540588 DOI: 10.1021/acs.jpclett.3c01711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
Neuromorphic computing could enable the potential to break the inherent limitations of conventional von Neumann architectures, which has led to widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), as crystalline porous polymers, have tailorable skeletons and pores, providing unique platforms for the interplay with photons, excitons, electrons, holes, ions, spins, and molecules. Such features encourage the rising research interest in COF materials in neuromorphic electronics. To develop high-performance COF-based neuromorphic memory devices, it is necessary to comprehensively understand materials, devices, and applications. Therefore, this Perspective focuses on discussing the use of COF materials for neuromorphic memory devices in terms of molecular design, thin-film processing, and neuromorphic applications. Finally, we provide an outlook for future directions and potential applications of COF-based neuromorphic electronics.
Collapse
Affiliation(s)
- Kui Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Ziqi Jia
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Yao Zhou
- College of Materials Science and Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Guanglong Ding
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Xin-Qi Ma
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Wenbiao Niu
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Jiyu Zhao
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, 2 Linggong Road, Dalian 116024, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| |
Collapse
|
4
|
Fields C, Levin M. Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments. ENTROPY 2022; 24:e24060819. [PMID: 35741540 PMCID: PMC9222757 DOI: 10.3390/e24060819] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/26/2022] [Accepted: 06/08/2022] [Indexed: 12/20/2022]
Abstract
One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings.
Collapse
Affiliation(s)
- Chris Fields
- Allen Discovery Center at Tufts University, Science and Engineering Complex, 200 College Ave., Medford, MA 02155, USA;
| | - Michael Levin
- Allen Discovery Center at Tufts University, Science and Engineering Complex, 200 College Ave., Medford, MA 02155, USA;
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA 02115, USA
- Correspondence:
| |
Collapse
|
5
|
Yoon C, Oh G, Park BH. Ion-Movement-Based Synaptic Device for Brain-Inspired Computing. NANOMATERIALS 2022; 12:nano12101728. [PMID: 35630952 PMCID: PMC9148095 DOI: 10.3390/nano12101728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 02/04/2023]
Abstract
As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.
Collapse
|
6
|
Kwon KC, Baek JH, Hong K, Kim SY, Jang HW. Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing. NANO-MICRO LETTERS 2022; 14:58. [PMID: 35122527 PMCID: PMC8818077 DOI: 10.1007/s40820-021-00784-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/03/2021] [Indexed: 05/21/2023]
Abstract
Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.
Collapse
Affiliation(s)
- Ki Chang Kwon
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
- Interdisciplinary Materials Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34133 Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
| | - Kootak Hong
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
| | - Soo Young Kim
- Department of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seoul, 02841 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
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229 Korea
| |
Collapse
|
7
|
Wang X, Lu Y, Zhang J, Zhang S, Chen T, Ou Q, Huang J. Highly Sensitive Artificial Visual Array Using Transistors Based on Porphyrins and Semiconductors. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2005491. [PMID: 33325607 DOI: 10.1002/smll.202005491] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/20/2020] [Indexed: 06/12/2023]
Abstract
Artificial visual systems with image sensing and storage functions have considerable potential in the field of artificial intelligence. Light-stimulated synaptic devices can be applied for neuromorphic computing to build artificial visual systems. Here, optoelectronic synaptic transistors based on 5,15-(2-hydroxyphenyl)-10,20-(4-nitrophenyl)porphyrin (TPP) and dinaphtho[2,3-b:2',3'-f ]thieno[3,2-b]thiophene (DNTT) are demonstrated. By utilizing stable TPP with high light absorption, the number of photogenerated carriers in the transport layer can be increased significantly. The devices exhibit high photosensitivity and tunable synaptic plasticity. The synaptic weight can be effectively modulated by the intensity, width, and wavelength of the light signals. Due to the high light absorption of TPP, an ultrasensitive artificial visual array based on these devices is developed, which can detect weak light signals as low as 1 µW cm-2 . Low-voltage operation is further demonstrated. Even with applied voltages as low as -70 µV, the devices can still show obvious responses, leading to an ultralow energy consumption of 1.4 fJ. The devices successfully demonstrate image sensing and storage functions, which can accurately identify visual information. In addition, the devices can preprocess information and achieve noise reduction. The excellent synaptic behavior of the TPP-based electronics suggests their good potential in the development of new intelligent visual systems.
Collapse
Affiliation(s)
- Xin Wang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Yang Lu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Shiqi Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Tianqi Chen
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Qingqing Ou
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Jia Huang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University, Shanghai, 200434, P. R. China
| |
Collapse
|
8
|
Sangwan VK, Hersam MC. Neuromorphic nanoelectronic materials. NATURE NANOTECHNOLOGY 2020; 15:517-528. [PMID: 32123381 DOI: 10.1038/s41565-020-0647-z] [Citation(s) in RCA: 199] [Impact Index Per Article: 49.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 01/23/2020] [Indexed: 05/10/2023]
Abstract
Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials.
Collapse
Affiliation(s)
- Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
| |
Collapse
|
9
|
Kreiser R, Renner A, Leite VRC, Serhan B, Bartolozzi C, Glover A, Sandamirskaya Y. An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot. Front Neurosci 2020; 14:551. [PMID: 32655350 PMCID: PMC7325709 DOI: 10.3389/fnins.2020.00551] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/04/2020] [Indexed: 11/17/2022] Open
Abstract
In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates the issued motor commands to estimate the iCub's head pose in a neuronal path-integration process. The neuromorphic vision system of the iCub is used to correct for drift in the pose estimation. Positions of objects in front of the robot are memorized using on-chip synaptic plasticity. We present real-time robotic experiments using 2 degrees of freedom (DoF) of the robot's head and show precise path integration, visual reset, and object position learning on-chip. We discuss the requirements for integrating the robotic system and neuromorphic hardware with current technologies.
Collapse
Affiliation(s)
- Raphaela Kreiser
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Alpha Renner
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Vanessa R. C. Leite
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Baris Serhan
- Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln, United Kingdom
| | | | | | - Yulia Sandamirskaya
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| |
Collapse
|
10
|
Yin L, Huang W, Xiao R, Peng W, Zhu Y, Zhang Y, Pi X, Yang D. Optically Stimulated Synaptic Devices Based on the Hybrid Structure of Silicon Nanomembrane and Perovskite. NANO LETTERS 2020; 20:3378-3387. [PMID: 32212734 DOI: 10.1021/acs.nanolett.0c00298] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Optoelectronic synaptic devices have been attracting increasing attention due to their critical role in the development of neuromorphic computing based on optoelectronic integration. Here we start with silicon nanomembrane (Si NM) to fabricate optoelectronic synaptic devices. Organolead halide perovskite (MAPbI3) is exploited to form a hybrid structure with Si NM. We demonstrate that synaptic transistors based on the hybrid structure are very sensitive to optical stimulation with low energy consumption. Synaptic functionalities such as excitatory post-synaptic current (EPSC), paired-pulse facilitation, and transition from short-term memory to long-term memory (LTM) are all successfully mimicked by using these optically stimulated synaptic transistors. The backgate-enabled tunability of the EPSC of these devices further leads to the LTM-based mimicking of visual learning and memory processes under different mood states. This work contributes to the development of Si-based optoelectronic synaptic devices for neuromorphic computing.
Collapse
Affiliation(s)
- Lei Yin
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Wen Huang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Rulei Xiao
- National Laboratory of Solid State Microstructures, Collaborative Innovation Center of Advanced Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China
| | - Wenbing Peng
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yiyue Zhu
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yiqiang Zhang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- School of Materials Science and Engineering, Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Deren Yang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| |
Collapse
|
11
|
Frenkel C, Lefebvre M, Legat JD, Bol D. A 0.086-mm 2 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:145-158. [PMID: 30418919 DOI: 10.1109/tbcas.2018.2880425] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cognitive SNNs requires the design of compact, low-power and versatile experimentation platforms with the key requirement of online learning in order to adapt and learn new features in uncontrolled environments. However, embedding online learning in SNNs is currently hindered by high incurred complexity and area overheads. In this paper, we present ODIN, a 0.086-mm 2 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm FDSOI CMOS achieving a minimum energy per synaptic operation (SOP) of 12.7 pJ. It leverages an efficient implementation of the spike-driven synaptic plasticity (SDSP) learning rule for high-density embedded online learning with only 0.68 μm 2 per 4-bit synapse. Neurons can be independently configured as a standard leaky integrate-and-fire model or as a custom phenomenological model that emulates the 20 Izhikevich behaviors found in biological spiking neurons. Using a single presentation of 6k 16 × 16 MNIST training images to a single-layer fully-connected 10-neuron network with on-chip SDSP-based learning, ODIN achieves a classification accuracy of 84.5%, while consuming only 15 nJ/inference at 0.55 V using rank order coding. ODIN thus enables further developments toward cognitive neuromorphic devices for low-power, adaptive and low-cost processing.
Collapse
|
12
|
Kreiser R, Aathmani D, Qiao N, Indiveri G, Sandamirskaya Y. Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields. Front Neurosci 2018; 12:717. [PMID: 30524218 PMCID: PMC6262404 DOI: 10.3389/fnins.2018.00717] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/19/2018] [Indexed: 11/26/2022] Open
Abstract
Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to physically emulate the functionality of biological neurons and synapses enables faithful modeling of neural and synaptic dynamics at ultra low power consumption in real-time, and thus may serve as computational substrate for a new generation of efficient neural controllers for artificial intelligent systems. Although one of the main advantages of neural networks is their ability to perform on-line learning, only a small number of neuromorphic hardware devices implement this feature on-chip. In this work, we use a reconfigurable on-line learning spiking (ROLLS) neuromorphic processor chip to build a neuronal architecture for sequence learning. The proposed neuronal architecture uses the attractor properties of winner-takes-all (WTA) dynamics to cope with mismatch and noise in the ROLLS analog computing elements, and it uses its on-chip plasticity features to store sequences of states. We demonstrate, with a proof-of-concept feasibility study how this architecture can store, replay, and update sequences of states, induced by external inputs. Controlled by the attractor dynamics and an explicit destabilizing signal, the items in a sequence can last for varying amounts of time and thus reliable sequence learning and replay can be robustly implemented in a real sensorimotor system.
Collapse
Affiliation(s)
- Raphaela Kreiser
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Dora Aathmani
- The School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Ning Qiao
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yulia Sandamirskaya
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| |
Collapse
|
13
|
Rutishauser U, Slotine JJ, Douglas RJ. Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks. Neural Comput 2018; 30:1359-1393. [PMID: 29566357 PMCID: PMC5930080 DOI: 10.1162/neco_a_01074] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSP's planar four-color graph coloring, maximum independent set, and sudoku on this substrate and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of nonsaturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by nonlinear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation and offer insight into the computational role of dual inhibitory mechanisms in neural circuits.
Collapse
Affiliation(s)
- Ueli Rutishauser
- Computation and Neural Systems, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, U.S.A., and Cedars-Sinai Medical Center, Departments of Neurosurgery, Neurology and Biomedical Sciences, Los Angeles, CA 90048, U.S.A.
| | - Jean-Jacques Slotine
- Nonlinear Systems Laboratory, Department of Mechanical Engineering and Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, U.S.A.
| | - Rodney J Douglas
- Institute of Neuroinformatics, University and ETH Zurich, Zurich 8057, Switzerland
| |
Collapse
|
14
|
Yang S, Wei X, Wang J, Deng B, Liu C, Yu H, Li H. Efficient hardware implementation of the subthalamic nucleus–external globus pallidus oscillation system and its dynamics investigation. Neural Netw 2017; 94:220-238. [DOI: 10.1016/j.neunet.2017.07.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 05/26/2017] [Accepted: 07/13/2017] [Indexed: 12/20/2022]
|
15
|
Cheng Z, Ríos C, Pernice WHP, Wright CD, Bhaskaran H. On-chip photonic synapse. SCIENCE ADVANCES 2017; 3:e1700160. [PMID: 28959725 PMCID: PMC5617375 DOI: 10.1126/sciadv.1700160] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 09/05/2017] [Indexed: 05/22/2023]
Abstract
The search for new "neuromorphic computing" architectures that mimic the brain's approach to simultaneous processing and storage of information is intense. Because, in real brains, neuronal synapses outnumber neurons by many orders of magnitude, the realization of hardware devices mimicking the functionality of a synapse is a first and essential step in such a search. We report the development of such a hardware synapse, implemented entirely in the optical domain via a photonic integrated-circuit approach. Using purely optical means brings the benefits of ultrafast operation speed, virtually unlimited bandwidth, and no electrical interconnect power losses. Our synapse uses phase-change materials combined with integrated silicon nitride waveguides. Crucially, we can randomly set the synaptic weight simply by varying the number of optical pulses sent down the waveguide, delivering an incredibly simple yet powerful approach that heralds systems with a continuously variable synaptic plasticity resembling the true analog nature of biological synapses.
Collapse
Affiliation(s)
- Zengguang Cheng
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
| | - Carlos Ríos
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
| | - Wolfram H. P. Pernice
- Institute of Physics, University of Muenster, Heisenbergstrasse 11, 48149 Muenster, Germany
| | - C. David Wright
- Department of Engineering, University of Exeter, Exeter EX4 QF, UK
| | - Harish Bhaskaran
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
- Corresponding author.
| |
Collapse
|
16
|
Milde MB, Blum H, Dietmüller A, Sumislawska D, Conradt J, Indiveri G, Sandamirskaya Y. Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System. Front Neurorobot 2017; 11:28. [PMID: 28747883 PMCID: PMC5507184 DOI: 10.3389/fnbot.2017.00028] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.
Collapse
Affiliation(s)
- Moritz B Milde
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Hermann Blum
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Alexander Dietmüller
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Dora Sumislawska
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Jörg Conradt
- Neuroscientific System Theory, Department of Electrical and Computer Engineering, Technical University of MunichMunich, Germany
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Yulia Sandamirskaya
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| |
Collapse
|
17
|
Broccard FD, Joshi S, Wang J, Cauwenberghs G. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems. J Neural Eng 2017; 14:041002. [PMID: 28573983 DOI: 10.1088/1741-2552/aa67a9] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. APPROACH This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. MAIN RESULTS Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. SIGNIFICANCE Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.
Collapse
Affiliation(s)
- Frédéric D Broccard
- Institute for Neural Computation, UC San Diego, United States of America. Department of Bioengineering, UC San Diego, United States of America
| | | | | | | |
Collapse
|
18
|
|
19
|
Mégardon G, Tandonnet C, Sumner P, Guillaume A. Limitations of short range Mexican hat connection for driving target selection in a 2D neural field: activity suppression and deviation from input stimuli. Front Comput Neurosci 2015; 9:128. [PMID: 26539103 PMCID: PMC4611141 DOI: 10.3389/fncom.2015.00128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 10/02/2015] [Indexed: 11/13/2022] Open
Abstract
Dynamic Neural Field models (DNF) often use a kernel of connection with short range excitation and long range inhibition. This organization has been suggested as a model for brain structures or for artificial systems involved in winner-take-all processes such as saliency localization, perceptual decision or target/action selection. A good example of such a DNF is the superior colliculus (SC), a key structure for eye movements. Recent results suggest that the superficial layers of the SC (SCs) exhibit relatively short range inhibition with a longer time constant than excitation. The aim of the present study was to further examine the properties of a DNF with such an inhibition pattern in the context of target selection. First we tested the effects of stimulus size and shape on when and where self-maintained clusters of firing neurons appeared, using three variants of the model. In each model variant, small stimuli led to rapid formation of a spiking cluster, a range of medium sizes led to the suppression of any activity on the network and hence to no target selection, while larger sizes led to delayed selection of multiple loci. Second, we tested the model with two stimuli separated by a varying distance. Again single, none, or multiple spiking clusters could occur, depending on distance and relative stimulus strength. For short distances, activity attracted toward the strongest stimulus, reminiscent of well-known behavioral data for saccadic eye movements, while for larger distances repulsion away from the second stimulus occurred. All these properties predicted by the model suggest that the SCs, or any other neural structure thought to implement a short range MH, is an imperfect winner-take-all system. Although, those properties call for systematic testing, the discussion gathers neurophysiological and behavioral data suggesting that such properties are indeed present in target selection for saccadic eye movements.
Collapse
Affiliation(s)
- Geoffrey Mégardon
- School of Psychology, Cardiff UniversityCardiff, UK
- Laboratoire de Neurobiologie de la Cognition, UMR 6155, Centre National de la Recherche Scientifique, Aix-Marseille UniversitéMarseille, France
| | - Christophe Tandonnet
- Faculté de Psychologie et des Sciences de l'Education, Université de GenèveGenève, Switzerland
- Laboratoire de Psychologie Cognitive, UMR 7290, Centre National de la Recherche Scientifique, Aix-Marseille UniversitéMarseille, France
| | | | - Alain Guillaume
- Laboratoire de Neurobiologie de la Cognition, UMR 6155, Centre National de la Recherche Scientifique, Aix-Marseille UniversitéMarseille, France
- Department of Psychology, New York UniversityNew York, NY, USA
| |
Collapse
|
20
|
Cost-efficient FPGA implementation of basal ganglia and their Parkinsonian analysis. Neural Netw 2015; 71:62-75. [PMID: 26318085 DOI: 10.1016/j.neunet.2015.07.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 07/24/2015] [Accepted: 07/30/2015] [Indexed: 11/23/2022]
Abstract
The basal ganglia (BG) comprise multiple subcortical nuclei, which are responsible for cognition and other functions. Developing a brain-machine interface (BMI) demands a suitable solution for the real-time implementation of a portable BG. In this study, we used a digital hardware implementation of a BG network containing 256 modified Izhikevich neurons and 2048 synapses to reliably reproduce the biological characteristics of BG on a single field programmable gate array (FPGA) core. We also highlighted the role of Parkinsonian analysis by considering neural dynamics in the design of the hardware-based architecture. Thus, we developed a multi-precision architecture based on a precise analysis using the FPGA-based platform with fixed-point arithmetic. The proposed embedding BG network can be applied to intelligent agents and neurorobotics, as well as in BMI projects with clinical applications. Although we only characterized the BG network with Izhikevich models, the proposed approach can also be extended to more complex neuron models and other types of functional networks.
Collapse
|
21
|
Lee JH, Delbruck T, Pfeiffer M, Park PKJ, Shin CW, Ryu HE, Kang BC. Real-time gesture interface based on event-driven processing from stereo silicon retinas. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2014; 25:2250-2263. [PMID: 25420246 DOI: 10.1109/tnnls.2014.2308551] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We propose a real-time hand gesture interface based on combining a stereo pair of biologically inspired event-based dynamic vision sensor (DVS) silicon retinas with neuromorphic event-driven postprocessing. Compared with conventional vision or 3-D sensors, the use of DVSs, which output asynchronous and sparse events in response to motion, eliminates the need to extract movements from sequences of video frames, and allows significantly faster and more energy-efficient processing. In addition, the rate of input events depends on the observed movements, and thus provides an additional cue for solving the gesture spotting problem, i.e., finding the onsets and offsets of gestures. We propose a postprocessing framework based on spiking neural networks that can process the events received from the DVSs in real time, and provides an architecture for future implementation in neuromorphic hardware devices. The motion trajectories of moving hands are detected by spatiotemporally correlating the stereoscopically verged asynchronous events from the DVSs by using leaky integrate-and-fire (LIF) neurons. Adaptive thresholds of the LIF neurons achieve the segmentation of trajectories, which are then translated into discrete and finite feature vectors. The feature vectors are classified with hidden Markov models, using a separate Gaussian mixture model for spotting irrelevant transition gestures. The disparity information from stereovision is used to adapt LIF neuron parameters to achieve recognition invariant of the distance of the user to the sensor, and also helps to filter out movements in the background of the user. Exploiting the high dynamic range of DVSs, furthermore, allows gesture recognition over a 60-dB range of scene illuminance. The system achieves recognition rates well over 90% under a variety of variable conditions with static and dynamic backgrounds with naïve users.
Collapse
|
22
|
Petrovici MA, Vogginger B, Müller P, Breitwieser O, Lundqvist M, Muller L, Ehrlich M, Destexhe A, Lansner A, Schüffny R, Schemmel J, Meier K. Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms. PLoS One 2014; 9:e108590. [PMID: 25303102 PMCID: PMC4193761 DOI: 10.1371/journal.pone.0108590] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Accepted: 08/22/2014] [Indexed: 11/18/2022] Open
Abstract
Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks.
Collapse
Affiliation(s)
- Mihai A. Petrovici
- Ruprecht-Karls-Universität Heidelberg, Kirchhoff Institute for Physics, Heidelberg, Germany
| | - Bernhard Vogginger
- Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany
| | - Paul Müller
- Ruprecht-Karls-Universität Heidelberg, Kirchhoff Institute for Physics, Heidelberg, Germany
| | - Oliver Breitwieser
- Ruprecht-Karls-Universität Heidelberg, Kirchhoff Institute for Physics, Heidelberg, Germany
| | - Mikael Lundqvist
- Department of Computational Biology, School of Computer Science and Communication, Stockholm University and Royal Institute of Technology, Stockholm, Sweden
| | - Lyle Muller
- CNRS, Unité de Neuroscience, Information et Complexité, Gif sur Yvette, France
| | - Matthias Ehrlich
- Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany
| | - Alain Destexhe
- CNRS, Unité de Neuroscience, Information et Complexité, Gif sur Yvette, France
| | - Anders Lansner
- Department of Computational Biology, School of Computer Science and Communication, Stockholm University and Royal Institute of Technology, Stockholm, Sweden
| | - René Schüffny
- Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany
| | - Johannes Schemmel
- Ruprecht-Karls-Universität Heidelberg, Kirchhoff Institute for Physics, Heidelberg, Germany
| | - Karlheinz Meier
- Ruprecht-Karls-Universität Heidelberg, Kirchhoff Institute for Physics, Heidelberg, Germany
| |
Collapse
|
23
|
Gupta P, Markan CM. An adaptable neuromorphic model of orientation selectivity based on floating gate dynamics. Front Neurosci 2014; 8:54. [PMID: 24765062 PMCID: PMC3980111 DOI: 10.3389/fnins.2014.00054] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2013] [Accepted: 03/09/2014] [Indexed: 11/21/2022] Open
Abstract
The biggest challenge that the neuromorphic community faces today is to build systems that can be considered truly cognitive. Adaptation and self-organization are the two basic principles that underlie any cognitive function that the brain performs. If we can replicate this behavior in hardware, we move a step closer to our goal of having cognitive neuromorphic systems. Adaptive feature selectivity is a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features. Developing neuromorphic feature maps can help design generic machines that can emulate this adaptive behavior. Most neuromorphic models that have attempted to build self-organizing systems, follow the approach of modeling abstract theoretical frameworks in hardware. While this is good from a modeling and analysis perspective, it may not lead to the most efficient hardware. On the other hand, exploiting hardware dynamics to build adaptive systems rather than forcing the hardware to behave like mathematical equations, seems to be a more robust methodology when it comes to developing actual hardware for real world applications. In this paper we use a novel time-staggered Winner Take All circuit, that exploits the adaptation dynamics of floating gate transistors, to model an adaptive cortical cell that demonstrates Orientation Selectivity, a well-known biological phenomenon observed in the visual cortex. The cell performs competitive learning, refining its weights in response to input patterns resembling different oriented bars, becoming selective to a particular oriented pattern. Different analysis performed on the cell such as orientation tuning, application of abnormal inputs, response to spatial frequency and periodic patterns reveal close similarity between our cell and its biological counterpart. Embedded in a RC grid, these cells interact diffusively exhibiting cluster formation, making way for adaptively building orientation selective maps in silicon.
Collapse
Affiliation(s)
- Priti Gupta
- VLSI Design Technology Lab, Department of Physics and Computer Science, Dayalbagh Educational Institute Agra, Uttar Pradesh, India
| | - C M Markan
- VLSI Design Technology Lab, Department of Physics and Computer Science, Dayalbagh Educational Institute Agra, Uttar Pradesh, India
| |
Collapse
|
24
|
Kasabov NK. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw 2014; 52:62-76. [PMID: 24508754 DOI: 10.1016/j.neunet.2014.01.006] [Citation(s) in RCA: 132] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Revised: 12/29/2013] [Accepted: 01/07/2014] [Indexed: 11/24/2022]
|
25
|
Abstract
Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using "virtual receptors" (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems.
Collapse
|
26
|
da Costa NM, Martin KA. Sparse reconstruction of brain circuits: Or, how to survive without a microscopic connectome. Neuroimage 2013; 80:27-36. [DOI: 10.1016/j.neuroimage.2013.04.054] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2013] [Revised: 04/10/2013] [Accepted: 04/15/2013] [Indexed: 11/30/2022] Open
|
27
|
|
28
|
Markan CM, Gupta P, Bansal M. An adaptive neuromorphic model of ocular dominance map using floating gate 'synapse'. Neural Netw 2013; 45:117-33. [PMID: 23648171 DOI: 10.1016/j.neunet.2013.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2012] [Revised: 04/02/2013] [Accepted: 04/04/2013] [Indexed: 11/28/2022]
Abstract
A novel analogue CMOS design of a cortical cell, that computes weighted sum of inputs, is presented. The cell's feedback regime exploits the adaptation dynamics of floating gate pFET 'synapse' to perform competitive learning amongst input weights as time-staggered winner take all. A learning rate parameter regulates adaptation time and a bias enforces resource limitation by restricting the number of input branches and winners in a competition. When learning ends, the cell's response favours one input pattern over others to exhibit feature selectivity. Embedded in a 2-D RC grid, these feature selective cells are capable of performing a symmetry breaking pattern formation, observed in some reaction-diffusion models of cortical feature map formation, e.g. ocular dominance. Close similarity with biological networks in terms of adaptability and long term memory indicates that the cell's design is ideally suited for analogue VLSI implementation of Self-Organizing Feature Map (SOFM) models of cortical feature maps.
Collapse
Affiliation(s)
- C M Markan
- Department of Physics & Computer Science, Dayalbagh Educational Institute (Deemed University), Dayalbagh, Agra-282005, India.
| | | | | |
Collapse
|
29
|
Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Netw 2013; 41:188-201. [DOI: 10.1016/j.neunet.2012.11.014] [Citation(s) in RCA: 235] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2012] [Revised: 11/20/2012] [Accepted: 11/25/2012] [Indexed: 11/21/2022]
|
30
|
Seok Jeong D, Kim I, Ziegler M, Kohlstedt H. Towards artificial neurons and synapses: a materials point of view. RSC Adv 2013. [DOI: 10.1039/c2ra22507g] [Citation(s) in RCA: 146] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
|
31
|
|
32
|
|
33
|
Garg V, Shekhar R, Harris JG. Spiking neuron computation with the time machine. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2012; 6:142-155. [PMID: 23852979 DOI: 10.1109/tbcas.2011.2179544] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The Time Machine (TM) is a spike-based computation architecture that represents synaptic weights in time. This choice of weight representation allows the use of virtual synapses, providing an excellent tradeoff in terms of flexibility, arbitrary weight connections and hardware usage compared to dedicated synapse architectures. The TM supports an arbitrary number of synapses and is limited only by the number of simultaneously active synapses to each neuron. SpikeSim, a behavioral hardware simulator for the architecture, is described along with example algorithms for edge detection and objection recognition. The TM can implement traditional spike-based processing as well as recently developed time mode operations where step functions serve as the input and output of each neuron block. A custom hybrid digital/analog implementation and a fully digital realization of the TM are discussed. An analog chip with 32 neurons, 1024 synapses and an address event representation (AER) block has been fabricated in 0.5 μm technology. A fully digital field-programmable gate array (FPGA)-based implementation of the architecture has 6,144 neurons and 100,352 simultaneously active synapses. Both implementations utilize a digital controller for routing spikes that can process up to 34 million synapses per second.
Collapse
Affiliation(s)
- Vaibhav Garg
- Texas Instruments Incorpoarted, Dallas, TX 75266, USA.
| | | | | |
Collapse
|
34
|
NeuCube EvoSpike Architecture for Spatio-temporal Modelling and Pattern Recognition of Brain Signals. ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION 2012. [DOI: 10.1007/978-3-642-33212-8_21] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
35
|
Kasabov N. Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition. ADVANCES IN COMPUTATIONAL INTELLIGENCE 2012. [DOI: 10.1007/978-3-642-30687-7_12] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
36
|
Brüderle D, Petrovici MA, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grübl A, Wendt K, Müller E, Schwartz MO, de Oliveira DH, Jeltsch S, Fieres J, Schilling M, Müller P, Breitwieser O, Petkov V, Muller L, Davison AP, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zühl L, Mayr C, Destexhe A, Diesmann M, Potjans TC, Lansner A, Schüffny R, Schemmel J, Meier K. A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. BIOLOGICAL CYBERNETICS 2011; 104:263-296. [PMID: 21618053 DOI: 10.1007/s00422-011-0435-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2010] [Accepted: 04/19/2011] [Indexed: 05/30/2023]
Abstract
In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.
Collapse
Affiliation(s)
- Daniel Brüderle
- Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
37
|
Evolving Probabilistic Spiking Neural Networks for Spatio-temporal Pattern Recognition: A Preliminary Study on Moving Object Recognition. ACTA ACUST UNITED AC 2011. [DOI: 10.1007/978-3-642-24965-5_25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
|
38
|
|
39
|
Nuntalid N, Dhoble K, Kasabov N. EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network. NEURAL INFORMATION PROCESSING 2011. [DOI: 10.1007/978-3-642-24955-6_54] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
|
40
|
Rutishauser U, Douglas RJ, Slotine JJ. Collective stability of networks of winner-take-all circuits. Neural Comput 2010; 23:735-73. [PMID: 21162667 DOI: 10.1162/neco_a_00091] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The neocortex has a remarkably uniform neuronal organization, suggesting that common principles of processing are employed throughout its extent. In particular, the patterns of connectivity observed in the superficial layers of the visual cortex are consistent with the recurrent excitation and inhibitory feedback required for cooperative-competitive circuits such as the soft winner-take-all (WTA). WTA circuits offer interesting computational properties such as selective amplification, signal restoration, and decision making. But these properties depend on the signal gain derived from positive feedback, and so there is a critical trade-off between providing feedback strong enough to support the sophisticated computations while maintaining overall circuit stability. The issue of stability is all the more intriguing when one considers that the WTAs are expected to be densely distributed through the superficial layers and that they are at least partially interconnected. We consider how to reason about stability in very large distributed networks of such circuits. We approach this problem by approximating the regular cortical architecture as many interconnected cooperative-competitive modules. We demonstrate that by properly understanding the behavior of this small computational module, one can reason over the stability and convergence of very large networks composed of these modules. We obtain parameter ranges in which the WTA circuit operates in a high-gain regime, is stable, and can be aggregated arbitrarily to form large, stable networks. We use nonlinear contraction theory to establish conditions for stability in the fully nonlinear case and verify these solutions using numerical simulations. The derived bounds allow modes of operation in which the WTA network is multistable and exhibits state-dependent persistent activities. Our approach is sufficiently general to reason systematically about the stability of any network, biological or technological, composed of networks of small modules that express competition through shared inhibition.
Collapse
Affiliation(s)
- Ueli Rutishauser
- Department of Neural Systems and Coding, Max Planck Institute for Brain Research, Frankfurt am Main, Hessen 60528, Germany
| | | | | |
Collapse
|
41
|
|