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Islam M, Hasan Majumder M, Hussein M, Hossain KM, Miah M. A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets. Heliyon 2024; 10:e25469. [PMID: 38356538 PMCID: PMC10865258 DOI: 10.1016/j.heliyon.2024.e25469] [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: 07/24/2023] [Revised: 11/30/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024] Open
Abstract
Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with significant clinical implications. Early and accurate diagnosis of PD is crucial for timely intervention and personalized treatment. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promis-ing tools for improving PD diagnosis. This review paper presents a detailed analysis of the current state of ML and DL-based PD diagnosis, focusing on voice, handwriting, and wave spiral datasets. The study also evaluates the effectiveness of various ML and DL algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical decision-making. Additionally, the paper explores the identifi-cation of biomarkers using these techniques, offering insights into improving the diagnostic process. The discussion encompasses different data formats and commonly employed ML and DL methods in PD diagnosis, providing a comprehensive overview of the field. This review serves as a roadmap for future research, guiding the development of ML and DL-based tools for PD detection. It is expected to benefit both the scientific community and medical practitioners by advancing our understanding of PD diagnosis and ultimately improving patient outcomes.
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Affiliation(s)
- Md.Ariful Islam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Nilkhet Rd, Dhaka, 1000, Bangladesh
| | - Md.Ziaul Hasan Majumder
- Institute of Electronics, Bangladesh Atomic Energy Commission, Dhaka, 1207, Bangladesh
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md.Alomgeer Hussein
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Khondoker Murad Hossain
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md.Sohel Miah
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
- Moulvibazar Polytechnic Institute, Bangladesh
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Yang S, Wang J, Deng B, Azghadi MR, Linares-Barranco B. Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:7126-7140. [PMID: 34115596 DOI: 10.1109/tnnls.2021.3084250] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain-machine integration, and the investigation of brain cognition during learning.
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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.
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Learning dynamic causal mechanisms from non-stationary data. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03843-3] [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]
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Gandolfi D, Puglisi FM, Boiani GM, Pagnoni G, Friston KJ, D'Angelo EU, Mapelli J. Emergence of associative learning in a neuromorphic inference network. J Neural Eng 2022; 19. [PMID: 35508120 DOI: 10.1088/1741-2552/ac6ca7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/04/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.
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Affiliation(s)
- Daniela Gandolfi
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Francesco Maria Puglisi
- DIEF, Universita degli Studi di Modena e Reggio Emilia, Via P. Vivarelli 10/1, Modena, MO, 41121, ITALY
| | - Giulia Maria Boiani
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Giuseppe Pagnoni
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Karl J Friston
- Institute of Neurology, University College London, 23 Queen Square, LONDON, WC1N 3BG, London, WC1N 3AR, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Egidio Ugo D'Angelo
- Department Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Pavia, Lombardia, 27100, ITALY
| | - Jonathan Mapelli
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, 41125, ITALY
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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.
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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
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Yang S, Gao T, Wang J, Deng B, Lansdell B, Linares-Barranco B. Efficient Spike-Driven Learning With Dendritic Event-Based Processing. Front Neurosci 2021; 15:601109. [PMID: 33679295 PMCID: PMC7933681 DOI: 10.3389/fnins.2021.601109] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/21/2021] [Indexed: 11/22/2022] Open
Abstract
A critical challenge in neuromorphic computing is to present computationally efficient algorithms of learning. When implementing gradient-based learning, error information must be routed through the network, such that each neuron knows its contribution to output, and thus how to adjust its weight. This is known as the credit assignment problem. Exactly implementing a solution like backpropagation involves weight sharing, which requires additional bandwidth and computations in a neuromorphic system. Instead, models of learning from neuroscience can provide inspiration for how to communicate error information efficiently, without weight sharing. Here we present a novel dendritic event-based processing (DEP) algorithm, using a two-compartment leaky integrate-and-fire neuron with partially segregated dendrites that effectively solves the credit assignment problem. In order to optimize the proposed algorithm, a dynamic fixed-point representation method and piecewise linear approximation approach are presented, while the synaptic events are binarized during learning. The presented optimization makes the proposed DEP algorithm very suitable for implementation in digital or mixed-signal neuromorphic hardware. The experimental results show that spiking representations can rapidly learn, achieving high performance by using the proposed DEP algorithm. We find the learning capability is affected by the degree of dendritic segregation, and the form of synaptic feedback connections. This study provides a bridge between the biological learning and neuromorphic learning, and is meaningful for the real-time applications in the field of artificial intelligence.
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Affiliation(s)
- Shuangming Yang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Tian Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Bin Deng
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Benjamin Lansdell
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
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Parvizi-Fard A, Salimi-Nezhad N, Amiri M, Falotico E, Laschi C. Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors. Sci Rep 2021; 11:2109. [PMID: 33483529 PMCID: PMC7822817 DOI: 10.1038/s41598-021-81199-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 01/04/2021] [Indexed: 01/30/2023] Open
Abstract
Touch and pain sensations are complementary aspects of daily life that convey crucial information about the environment while also providing protection to our body. Technological advancements in prosthesis design and control mechanisms assist amputees to regain lost function but often they have no meaningful tactile feedback or perception. In the present study, we propose a bio-inspired tactile system with a population of 23 digital afferents: 12 RA-I, 6 SA-I, and 5 nociceptors. Indeed, the functional concept of the nociceptor is implemented on the FPGA for the first time. One of the main features of biological tactile afferents is that their distal axon branches in the skin, creating complex receptive fields. Given these physiological observations, the bio-inspired afferents are randomly connected to the several neighboring mechanoreceptors with different weights to form their own receptive field. To test the performance of the proposed neuromorphic chip in sharpness detection, a robotic system with three-degree of freedom equipped with the tactile sensor indents the 3D-printed objects. Spike responses of the biomimetic afferents are then collected for analysis by rate and temporal coding algorithms. In this way, the impact of the innervation mechanism and collaboration of afferents and nociceptors on sharpness recognition are investigated. Our findings suggest that the synergy between sensory afferents and nociceptors conveys more information about tactile stimuli which in turn leads to the robustness of the proposed neuromorphic system against damage to the taxels or afferents. Moreover, it is illustrated that spiking activity of the biomimetic nociceptors is amplified as the sharpness increases which can be considered as a feedback mechanism for prosthesis protection. This neuromorphic approach advances the development of prosthesis to include the sensory feedback and to distinguish innocuous (non-painful) and noxious (painful) stimuli.
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Affiliation(s)
- Adel Parvizi-Fard
- grid.412112.50000 0001 2012 5829Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Nima Salimi-Nezhad
- grid.412112.50000 0001 2012 5829Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahmood Amiri
- grid.412112.50000 0001 2012 5829Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Parastar Ave., Kermanshah, Iran
| | - Egidio Falotico
- grid.263145.70000 0004 1762 600XThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy ,grid.263145.70000 0004 1762 600XDepartment of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Cecilia Laschi
- grid.263145.70000 0004 1762 600XThe BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy ,grid.263145.70000 0004 1762 600XDepartment of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy ,grid.4280.e0000 0001 2180 6431Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore
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Hao X, Yang S, Deng B, Wang J, Wei X, Che Y. A CORDIC based real-time implementation and analysis of a respiratory central pattern generator. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.10.101] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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10
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Chen M, Zu L, Wang H, Su F. FPGA-Based Real-Time Simulation Platform for Large-Scale STN-GPe Network. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2537-2547. [PMID: 32991283 DOI: 10.1109/tnsre.2020.3027546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The real-time simulation of large-scale subthalamic nucleus (STN)-external globus pallidus (GPe) network model is of great significance for the mechanism analysis and performance improvement of deep brain stimulation (DBS) for Parkinson's states. This paper implements the real-time simulation of a large-scale STN-GPe network containing 512 single-compartment Hodgkin-Huxley type neurons on the Altera Stratix IV field programmable gate array (FPGA) hardware platform. At the single neuron level, some resource optimization schemes such as multiplier substitution, fixed-point operation, nonlinear function approximation and function recombination are adopted, which consists the foundation of the large-scale network realization. At the network level, the simulation scale of network is expanded using module reuse method at the cost of simulation time. The correlation coefficient between the neuron firing waveform of the FPGA platform and the MATLAB software simulation waveform is 0.9756. Under the same physiological time, the simulation speed of FPGA platform is 75 times faster than the Intel Core i7-8700K 3.70 GHz CPU 32GB RAM computer simulation speed. In addition, the established platform is used to analyze the effects of temporal pattern DBS on network firing activities. The proposed large-scale STN-GPe network meets the need of real time simulation, which would be rather helpful in designing closed-loop DBS improvement strategies.
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Aslam AR, Altaf MAB. An On-Chip Processor for Chronic Neurological Disorders Assistance Using Negative Affectivity Classification. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:838-851. [PMID: 32746354 DOI: 10.1109/tbcas.2020.3008766] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Chronic neurological disorders (CND's) are lifelong diseases and cannot be eradicated, but their severe effects can be alleviated by early preemptive measures. CND's, such as Alzheimer's, Autism Spectrum Disorder (ASD), and Amyotrophic Lateral Sclerosis (ALS), are the chronic ailment of the central nervous system that causes the degradation of emotional and cognitive abilities. Long term continuous monitoring with neuro-feedback of human emotions for patients with CND's is crucial in mitigating its harmful effect. This paper presents hardware efficient and dedicated human emotion classification processor for CND's. Scalp EEG is used for the emotion's classification using the valence and arousal scales. A linear support vector machine classifier is used with power spectral density, logarithmic interhemispheric power spectral ratio, and the interhemispheric power spectral difference of eight EEG channel locations suitable for a wearable non-invasive classification system. A look-up-table based logarithmic division unit (LDU) is to represent the division features in machine learning (ML) applications. The implemented LDU minimizes the cost of integer division by 34% for ML applications. The implemented emotion's classification processor achieved an accuracy of 72.96% and 73.14%, respectively, for the valence and arousal classification on multiple publicly available datasets. The 2 x 3mm2 processor is fabricated using a 0.18 μm 1P6M CMOS process with power and energy utilization of 2.04 mW and 16 μJ/classification, respectively, for 8-channel operation.
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Abstract
This study presents a computational model to reproduce the biological dynamics of "listening to music." A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.
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Affiliation(s)
- Frank Klefenz
- Fraunhofer Institute for Digital Media Technology IDMT, Ilmenau, Germany
| | - Tamas Harczos
- Fraunhofer Institute for Digital Media Technology IDMT, Ilmenau, Germany
- Auditory Neuroscience and Optogenetics Laboratory, German Primate Center, Göttingen, Germany
- audifon GmbH & Co. KG, Kölleda, Germany
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Scalable Implementation of Hippocampal Network on Digital Neuromorphic System towards Brain-Inspired Intelligence. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10082857] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In this paper, an expanded digital hippocampal spurt neural network (HSNN) is innovatively proposed to simulate the mammalian cognitive system and to perform the neuroregulatory dynamics that play a critical role in the cognitive processes of the brain, such as memory and learning. The real-time computation of a large-scale peak neural network can be realized by the scalable on-chip network and parallel topology. By exploring the latest research in the field of neurons and comparing with the results of this paper, it can be found that the implementation of the hippocampal neuron model using the coordinate rotation numerical calculation algorithm can significantly reduce the cost of hardware resources. In addition, the rational use of on-chip network technology can further improve the performance of the system, and even significantly improve the network scalability on a single field programmable gate array chip. The neuromodulation dynamics are considered in the proposed system, which can replicate more relevant biological dynamics. Based on the analysis of biological theory and the theory of hardware integration, it is shown that the innovative system proposed in this paper can reproduce the biological characteristics of the hippocampal network and may be applied to brain-inspired intelligent subjects. The study in this paper will have an unexpected effect on the future research of digital neuromorphic design of spike neural network and the dynamics of the hippocampal network.
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Shama F, Haghiri S, Imani MA. FPGA Realization of Hodgkin-Huxley Neuronal Model. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1059-1068. [PMID: 32175866 DOI: 10.1109/tnsre.2020.2980475] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
One of the appealing cases of the neuromorphic research area is the implementation of biological neural networks. The current study offers Multiplierless Hodgkin-Huxley Model (MHHM). This modified model may reproduce various spiking behaviors, like the biological HH neurons, with high accuracy. The presented modified model, in comparison to the original HH model, due to its exact similarity to the original model, has more top performances in the case of FPGA saving and more achievable frequency (speed-up). In this approach, the proposed model has a 69 % saving in FPGA resources and also the maximum frequency of 85 MHz that is more than other similar works. In this modification, all spiking behaviors of the original model have been generated with low error calculations. To validate the MHHM neuron, this proposed model has been implemented on digital hardware FPGA. This approach demonstrates that the original HH model and the proposed model have high similarity in terms of higher performance and digital hardware cost reduction.
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Yavari F, Amiri M, Rahatabad FN, Falotico E, Laschi C. Spike train analysis in a digital neuromorphic system of cutaneous mechanoreceptor. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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16
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Yang S, Deng B, Wang J, Li H, Lu M, Che Y, Wei X, Loparo KA. Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:148-162. [PMID: 30892250 DOI: 10.1109/tnnls.2019.2899936] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.
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Faramarzi F, Azad F, Amiri M, Linares-Barranco B. A Neuromorphic Digital Circuit for Neuronal Information Encoding Using Astrocytic Calcium Oscillations. Front Neurosci 2019; 13:998. [PMID: 31649494 PMCID: PMC6794439 DOI: 10.3389/fnins.2019.00998] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 09/03/2019] [Indexed: 01/30/2023] Open
Abstract
Neurophysiological observations are clarifying how astrocytes can actively participate in information processing and how they can encode information through frequency and amplitude modulation of intracellular Ca2+ signals. Consequently, hardware realization of astrocytes is important for developing the next generation of bio-inspired computing systems. In this paper, astrocytic calcium oscillations and neuronal firing dynamics are presented by De Pittà and IF (Integrated & Fire) models, respectively. Considering highly nonlinear equations of the astrocyte model, linear approximation and single constant multiplication (SCM) techniques are employed for efficient hardware execution while maintaining the dynamic of the original models. This low-cost hardware architecture for the astrocyte model is able to show the essential features of different types of Ca2+ modulation such as amplitude modulation (AM), frequency modulation (FM), or both modes (AFM). To show good agreement between the results of original models simulated in MATLAB and the proposed digital circuits executed on FPGA, quantitative, and qualitative analyses including phase plane are done. This new neuromorphic circuit of astrocyte is able to successfully demonstrate AM/FM/AFM calcium signaling in its real operation on FPGA and has applications in self-repairing systems. It also can be employed as a subsystem for linking biological cells to artificial neuronal networks using astrocytic calcium oscillations in future research.
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Affiliation(s)
- Farnaz Faramarzi
- Department of Electronics, Amirkabir University of Technology, Tehran, Iran
| | - Fatemeh Azad
- Medical Technology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mahmood Amiri
- Medical Technology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Bernabé Linares-Barranco
- Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC and Univesity of Seville, Sevilla, Spain
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Yang S, Wang J, Deng B, Liu C, Li H, Fietkiewicz C, Loparo KA. Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:2490-2503. [PMID: 29993922 DOI: 10.1109/tcyb.2018.2823730] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The investigation of the human intelligence, cognitive systems and functional complexity of human brain is significantly facilitated by high-performance computational platforms. In this paper, we present a real-time digital neuromorphic system for the simulation of large-scale conductance-based spiking neural networks (LaCSNN), which has the advantages of both high biological realism and large network scale. Using this system, a detailed large-scale cortico-basal ganglia-thalamocortical loop is simulated using a scalable 3-D network-on-chip (NoC) topology with six Altera Stratix III field-programmable gate arrays simulate 1 million neurons. Novel router architecture is presented to deal with the communication of multiple data flows in the multinuclei neural network, which has not been solved in previous NoC studies. At the single neuron level, cost-efficient conductance-based neuron models are proposed, resulting in the average utilization of 95% less memory resources and 100% less DSP resources for multiplier-less realization, which is the foundation of the large-scale realization. An analysis of the modified models is conducted, including investigation of bifurcation behaviors and ionic dynamics, demonstrating the required range of dynamics with a more reduced resource cost. The proposed LaCSNN system is shown to outperform the alternative state-of-the-art approaches previously used to implement the large-scale spiking neural network, and enables a broad range of potential applications due to its real-time computational power.
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Frenkel C, Lefebvre M, Legat JD, Bol D. A 0.086-mm 2 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:145-158. [PMID: 30418919 DOI: 10.1109/tbcas.2018.2880425] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cognitive SNNs requires the design of compact, low-power and versatile experimentation platforms with the key requirement of online learning in order to adapt and learn new features in uncontrolled environments. However, embedding online learning in SNNs is currently hindered by high incurred complexity and area overheads. In this paper, we present ODIN, a 0.086-mm 2 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm FDSOI CMOS achieving a minimum energy per synaptic operation (SOP) of 12.7 pJ. It leverages an efficient implementation of the spike-driven synaptic plasticity (SDSP) learning rule for high-density embedded online learning with only 0.68 μm 2 per 4-bit synapse. Neurons can be independently configured as a standard leaky integrate-and-fire model or as a custom phenomenological model that emulates the 20 Izhikevich behaviors found in biological spiking neurons. Using a single presentation of 6k 16 × 16 MNIST training images to a single-layer fully-connected 10-neuron network with on-chip SDSP-based learning, ODIN achieves a classification accuracy of 84.5%, while consuming only 15 nJ/inference at 0.55 V using rank order coding. ODIN thus enables further developments toward cognitive neuromorphic devices for low-power, adaptive and low-cost processing.
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Yang S, Wang J, Lin Q, Deng B, Wei X, Liu C, Li H. Cost-efficient FPGA implementation of a biologically plausible dopamine neural network and its application. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Yang S, Deng B, Li H, Liu C, Wang J, Yu H, Qin Y. FPGA implementation of hippocampal spiking network and its real-time simulation on dynamical neuromodulation of oscillations. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.12.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Rahimian E, Zabihi S, Amiri M, Linares-Barranco B. Digital Implementation of the Two-Compartmental Pinsky-Rinzel Pyramidal Neuron Model. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:47-57. [PMID: 29028209 DOI: 10.1109/tbcas.2017.2753541] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
It is believed that brain-like computing system can be achieved by the fusion of electronics and neuroscience. In this way, the optimized digital hardware implementation of neurons, primary units of nervous system, play a vital role in neuromorphic applications. Moreover, one of the main features of pyramidal neurons in cortical areas is bursting activities that has a critical role in synaptic plasticity. The Pinsky-Rinzel model is a nonlinear two-compartmental model for CA3 pyramidal cell that is widely used in neuroscience. In this paper, a modified Pinsky-Rinzel pyramidal model is proposed by replacing its complex nonlinear equations with piecewise linear approximation. Next, a digital circuit is designed for the simplified model to be able to implement on a low-cost digital hardware, such as field-programmable gate array (FPGA). Both original and proposed models are simulated in MATLAB and next digital circuit simulated in Vivado is compared to show that obtained results are in good agreement. Finally, the results of physical implementation on FPGA are also illustrated. The presented circuit advances preceding designs with regards to the ability to replicate essential characteristics of different firing responses including bursting and spiking in the compartmental model. This new circuit has various applications in neuromorphic engineering, such as developing new neuroinspired chips.
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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]
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A real-time FPGA implementation of a biologically inspired central pattern generator network. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Yang S, Deng B, Wang J, Li H, Liu C, Fietkiewicz C, Loparo KA. Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties. Sci Rep 2017; 7:40152. [PMID: 28065938 PMCID: PMC5220381 DOI: 10.1038/srep40152] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 12/01/2016] [Indexed: 12/13/2022] Open
Abstract
Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.
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Affiliation(s)
- Shuangming Yang
- School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China
| | - Huiyan Li
- School of Automation and Electrical Engineering, Tianjin University of Technology and Educations, 300222, Tianjin, China
| | - Chen Liu
- School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China.,Department of Electrical Engineering and Computer Science, Case Western Reserve University, 44106, Cleveland, Ohio, USA
| | - Chris Fietkiewicz
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 44106, Cleveland, Ohio, USA
| | - Kenneth A Loparo
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 44106, Cleveland, Ohio, USA
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Héricé C, Khalil R, Moftah M, Boraud T, Guthrie M, Garenne A. Decision making under uncertainty in a spiking neural network model of the basal ganglia. J Integr Neurosci 2016; 15:515-538. [PMID: 28002987 DOI: 10.1142/s021963521650028x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbic modalities of decision-making. We have used these properties to develop and extend a connectionist model at a spiking neuron level based on a previous rate model approach. This model is demonstrated on decision-making tasks that have been studied in primates and the electrophysiology interpreted to show that the decision is made in two steps. To model this, we have used two parallel loops, each of which performs decision-making based on interactions between positive and negative feedback pathways. This model is able to perform two-level decision-making as in primates. We show here that, before learning, synaptic noise is sufficient to drive the decision-making process and that, after learning, the decision is based on the choice that has proven most likely to be rewarded. The model is then submitted to lesion tests, reversal learning and extinction protocols. We show that, under these conditions, it behaves in a consistent manner and provides predictions in accordance with observed experimental data.
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Affiliation(s)
- Charlotte Héricé
- * University de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France.,† CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France
| | - Radwa Khalil
- † CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France
| | | | - Thomas Boraud
- * University de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France.,† CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France
| | - Martin Guthrie
- * University de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France.,† CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France
| | - André Garenne
- * University de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France.,† CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux, France
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