Log in

Relevant bibliographies by topics / SNN / Journal articles

To see the other types of publications on this topic, follow the link: SNN.

Journal articles on the topic 'SNN'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'SNN.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Novy, Diane. "Effectiveness of Splanchnic Nerve Neurolysis for Targeting Location of Cancer Pain: Using the Pain Drawing as an Outcome Variable." July 2016 6;19, no. 6;7 (2016): 397–403. http://dx.doi.org/10.36076/ppj/2016.19.397.

Full text

Abstract:

The effectiveness of splanchnic nerve neurolysis (SNN) for cancer-related abdominal pain has been investigated using numeric pain intensity rating as an outcome variable. The outcome variable in this study used the grid method for obtaining a targeted pain drawing score on 60 patients with pain from pancreatic or gastro-intestinal primary cancers or metastatic disease to the abdominal region. Results demonstrate excellent inter-rater agreement (intra-class correlation [ICC] coefficient at pre-SNN = 0.97 and ICC at within one month post-SNN = 0.98) for the grid method of scoring the pain drawin

APA, Harvard, Vancouver, ISO, and other styles

2

Moritz, Christian P., Yannick Tholance, Pierre-Baptiste Vallayer, et al. "Anti-AGO1 Antibodies Identify a Subset of Autoimmune Sensory Neuronopathy." Neurology - Neuroimmunology Neuroinflammation 10, no. 3 (2023): e200105. http://dx.doi.org/10.1212/nxi.0000000000200105.

Full text

Abstract:

Background and ObjectivesAutoantibodies (Abs) improve diagnosis and treatment decisions of idiopathic neurologic disorders. Recently, we identified Abs against Argonaute (AGO) proteins as potential autoimmunity biomarkers in neurologic disorders. In this study, we aim to reveal (1) the frequency of AGO1 Abs in sensory neuronopathy (SNN), (2) titers and IgG subclasses, and (3) their clinical pattern including response to treatment.MethodsThis retrospective multicentric case/control study screened 132 patients with SNN, 301 with non-SNN neuropathies, 274 with autoimmune diseases (AIDs), and 116

APA, Harvard, Vancouver, ISO, and other styles

3

Al-Hamid, Ali A., and HyungWon Kim. "Optimization of Spiking Neural Networks Based on Binary Streamed Rate Coding." Electronics 9, no. 10 (2020): 1599. http://dx.doi.org/10.3390/electronics9101599.

Full text

Abstract:

Spiking neural networks (SNN) increasingly attract attention for their similarity to the biological neural system. Hardware implementation of spiking neural networks, however, remains a great challenge due to their excessive complexity and circuit size. This work introduces a novel optimization method for hardware friendly SNN architecture based on a modified rate coding scheme called Binary Streamed Rate Coding (BSRC). BSRC combines the features of both rate and temporal coding. In addition, by employing a built-in randomizer, the BSRC SNN model provides a higher accuracy and faster training.

APA, Harvard, Vancouver, ISO, and other styles

4

Pawar, Anuradha, and Nidhi Tiwari. "A Novel Approach of DDOS Attack Classification with Genetic Algorithm-optimized Spiking Neural Network." International Journal of Computer Network and Information Security 16, no. 2 (2024): 103–16. http://dx.doi.org/10.5815/ijcnis.2024.02.09.

Full text

Abstract:

Spiking Neural Network (SNN) use spiking neurons that transmit information through discrete spikes, similar to the way biological neurons communicate through action potentials. This unique property of SNNs makes them suitable for applications that require real-time processing and low power consumption. This paper proposes a new method for detecting DDoS attacks using a spiking neural network (SNN) with a distance-based rate coding mechanism and optimizing the SNN using a genetic algorithm (GA). The proposed GA-SNN approach achieved a remarkable accuracy rate of 99.98% in detecting DDoS attacks

APA, Harvard, Vancouver, ISO, and other styles

5

Neiva, Flávia Cristina Brisque, and Cléa Rodrigues Leone. "Sucção em recém-nascidos pré-termo e estimulação da sucção." Pró-Fono Revista de Atualização Científica 18, no. 2 (2006): 141–50. http://dx.doi.org/10.1590/s0104-56872006000200003.

Full text

Abstract:

TEMA: a estimulação da sucção não-nutritiva pode antecipar o início da alimentação por via oral e influenciar a evolução da sucção em recém-nascidos pré-termo. OBJETIVO: descrever a evolução do padrão de sucção e os efeitos da estimulação da sucção não-nutritiva (SNN). MÉTODO: foram estudados 95 recém-nascidos pré-termo (RNPT) adequados para a idade gestacional (IG), com IG ao nascer menor ou igual a 33 semanas, distribuídos de forma aleatória em três grupos: Grupo 1 (G1), grupo controle, sem estimulação da SNN; Grupo 2 (G2), com estimulação da SNN com chupeta ortodôntica para prematuros NUK®

APA, Harvard, Vancouver, ISO, and other styles

6

Galán-Prado, Fabio, Alejandro Morán, Joan Font, Miquel Roca, and Josep L. Rosselló. "Compact Hardware Synthesis of Stochastic Spiking Neural Networks." International Journal of Neural Systems 29, no. 08 (2019): 1950004. http://dx.doi.org/10.1142/s0129065719500047.

Full text

Abstract:

Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation. The proposed stochastic SNN mo

APA, Harvard, Vancouver, ISO, and other styles

7

Putra, Rizal Kusuma, Gusti Ahmad Fanshuri Alfarisy, Faizal Widya Nugraha, and Aninditya Anggari Nuryono. "Automatic Plant Disease Classification with Unknown Class Rejection using Siamese Networks." Buletin Ilmiah Sarjana Teknik Elektro 6, no. 3 (2024): 308–16. https://doi.org/10.12928/biste.v6i3.11619.

Full text

Abstract:

Potatoes are one of the horticultural commodities with significant trade value both domestically and internationally. To produce high-quality potatoes, healthy and disease-free potato plants are essential. The most common diseases affecting potato plants are late blight and early blight. These diseases appear randomly in different positions and sizes on potato leaves, resulting in numerous combinations of infected leaves. This study proposes an architecture focused on a similarity-based approach, namely the Siamese Neural Network (SNN). SNN can recognize images by comparing two or more images

APA, Harvard, Vancouver, ISO, and other styles

8

Liu, Yang, Meng Tian, Ruijia Liu, et al. "Spike-Based Approximate Backpropagation Algorithm of Brain-Inspired Deep SNN for Sonar Target Classification." Computational Intelligence and Neuroscience 2022 (October 20, 2022): 1–11. http://dx.doi.org/10.1155/2022/1633946.

Full text

Abstract:

With the development of neuromorphic computing, more and more attention has been paid to a brain-inspired spiking neural network (SNN) because of its ultralow energy consumption and high-performance spatiotemporal information processing. Due to the discontinuity of the spiking neuronal activation function, it is still a difficult problem to train brain-inspired deep SNN directly, so SNN has not yet shown performance comparable to that of an artificial neural network. For this reason, the spike-based approximate backpropagation (SABP) algorithm and a general brain-inspired SNN framework are pro

APA, Harvard, Vancouver, ISO, and other styles

9

Ma, Zansong, Xiangbing Shu, Jie Huang, Haiyan Zhang, Zhen Xiao, and Li Zhang. "Salvia-Nelumbinis Naturalis Formula Improved Inflammation in LPS Stressed Macrophages via Upregulating MicroRNA-152." Mediators of Inflammation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5842747.

Full text

Abstract:

Salvia-Nelumbinis naturalis (SNN) formula is an effective agent in treating nonalcoholic steatohepatitis (NASH); however, the precise mechanisms are still undefined. Activation of Kupffer cells by gut-derived lipopolysaccharide (LPS) plays a central role in the pathogenesis of NASH. In the present study, we aimed to explore the epigenetic regulation of microRNAs under the beneficial effects of SNN-containing serum in LPS stressed macrophages. Kupffer cells were isolated from C57BL/6 mice and treated with LPS or LPS and SNN-containing serum; the mRNA expression of tumor necrosis factor-α (TNF-α

APA, Harvard, Vancouver, ISO, and other styles

10

Li, Cuixia, Zhiquan Shang, Li Shi, Wenlong Gao, and Shuyan Zhang. "IC-SNN: Optimal ANN2SNN Conversion at Low Latency." Mathematics 11, no. 1 (2022): 58. http://dx.doi.org/10.3390/math11010058.

Full text

Abstract:

The spiking neural network (SNN) has attracted the attention of many researchers because of its low energy consumption and strong bionics. However, when the network conversion method is used to solve the difficulty of network training caused by its discrete, too-long inference time, it may hinder the practical application of SNN. This paper proposes a novel model named the SNN with Initialized Membrane Potential and Coding Compensation (IC-SNN) to solve this problem. The model focuses on the effect of residual membrane potential and rate encoding on the target SNN. After analyzing the conversi

APA, Harvard, Vancouver, ISO, and other styles

11

Liu, Fangxin, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, and Li Jiang. "SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1692–701. http://dx.doi.org/10.1609/aaai.v36i2.20061.

Full text

Abstract:

Spiking Neural Networks (SNNs) have recently attracted enormous research interest since their event-driven and brain-inspired structure enables low-power computation. In image recognition tasks, the best results are achieved by SNN so far utilizing ANN-SNN conversion methods that replace activation functions in artificial neural networks~(ANNs) with integrate-and-fire neurons. Compared to source ANNs, converted SNNs usually suffer from accuracy loss and require a considerable number of time steps to achieve competitive accuracy. We find that the performance degradation of converted SNN stems f

APA, Harvard, Vancouver, ISO, and other styles

12

Chen, Long, Xuhang Li, Yaqin Zhu, et al. "Intralayer-Connected Spiking Neural Network with Hybrid Training Using Backpropagation and Probabilistic Spike-Timing Dependent Plasticity." International Journal of Intelligent Systems 2023 (July 25, 2023): 1–13. http://dx.doi.org/10.1155/2023/3135668.

Full text

Abstract:

Spiking neural networks (SNNs) are highly computationally efficient artificial intelligence methods due to their advantages in having a biologically plausible computational framework. Recent research has shown that SNN trained using backpropagation (SNN-BP) exhibits excellent performance and has shown great potential in tasks such as image classification and security detection. However, the backpropagation method limits the dynamics and biological plausibility of the neural models in SNN, which will limit the recognition and simulation performance of SNN. In order to make neural models more si

APA, Harvard, Vancouver, ISO, and other styles

13

Wang, Manman, Yuhai Yuan, and Yanfeng Jiang. "Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device." Micromachines 14, no. 10 (2023): 1820. http://dx.doi.org/10.3390/mi14101820.

Full text

Abstract:

As the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial intelligence field. In the SNN, the communication between the pre-synapse neuron (PRE) and the post-synapse neuron (POST) is conducted by the synapse. The corresponding synaptic weights are dependent on both the spiking patterns of the PRE and the POST, which are updated by spike-timing-dependent plastic

APA, Harvard, Vancouver, ISO, and other styles

14

Tripathi, G., and R. Deora. "FAUNA – ASSISTED LITTER DECOMPOSITION AND ITS IMPACT ON CHEMICAL AND BIOLOGICAL HEALTH OF BALANITES AEGYPTIACA BASED SILVIPASTURE SYSTEM." Scientific Temper 1, no. 01 (2022): 27–38. http://dx.doi.org/10.58414/scientifictemper.2010.01.1.04.

Full text

Abstract:

Interactions among soil fauna, litter diversity, soil nutrients andbiochemical properties during litter decomposition in Balanites aegyptiaca (T) basedsilvipasture system of tropical desertic land of India was studied. The system hasCenchurus ciliaris (CC) and Lasiurus sindicus (LS) grasses. Soil organic carbon (SOC),total soil nitrogen (TSN), soil ammonical nitrogen (SAN), soil nitrate nitrogen(SNN), soil available phosphorous (SAP), soil respiration (SR) and soildehydrogenase activity (SDA) were determined in litter decomposing soil. Faunalassociation and litter decomposition were maximum in

APA, Harvard, Vancouver, ISO, and other styles

15

Aoun, Mario Antoine. "A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons." International Journal of Artificial Intelligence & Applications 12, no. 03 (2021): 25–33. http://dx.doi.org/10.5121/ijaia.2021.12303.

Full text

Abstract:

We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls under our long-term investigation of STDP and chaos theory.

APA, Harvard, Vancouver, ISO, and other styles

16

NICHOLS, ERIC, L. J. McDAID, and N. H. SIDDIQUE. "CASE STUDY ON A SELF-ORGANIZING SPIKING NEURAL NETWORK FOR ROBOT NAVIGATION." International Journal of Neural Systems 20, no. 06 (2010): 501–8. http://dx.doi.org/10.1142/s0129065710002577.

Full text

Abstract:

This paper presents a Spiking Neural Network (SNN) architecture for mobile robot navigation. The SNN contains 4 layers where dynamic synapses route information to the appropriate neurons in each layer and the neurons are modeled using the Leaky Integrate and Fire (LIF) model. The SNN learns by self-organizing its connectivity as new environmental conditions are experienced and consequently knowledge about its environment is stored in the connectivity. Also a novel feature of the proposed SNN architecture is that it uses working memory, where present and previous sensor states are stored. Resul

APA, Harvard, Vancouver, ISO, and other styles

17

Wang, Junyi. "A Review of Spiking Neural Networks." SHS Web of Conferences 144 (2022): 03004. http://dx.doi.org/10.1051/shsconf/202214403004.

Full text

Abstract:

Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages in Spatio-temporal dynamics, diverse coding mechanisms, and event-driven properties. This paper is a review of SNN in order to help researchers from other areas to know and became familiar with the field of SNN or even became interested in SNN. Neuron models, coding methods, training algorithms, and neuromorphic computing platforms will be introduced in this paper. This paper analyzes the disadvantages and advantages of several kinds of neural model

APA, Harvard, Vancouver, ISO, and other styles

18

Asghar, Malik Summair, Saad Arslan, Ali A. Al-Hamid, and HyungWon Kim. "A Compact and Low-Power SoC Design for Spiking Neural Network Based on Current Multiplier Charge Injector Synapse." Sensors 23, no. 14 (2023): 6275. http://dx.doi.org/10.3390/s23146275.

Full text

Abstract:

This paper presents a compact analog system-on-chip (SoC) implementation of a spiking neural network (SNN) for low-power Internet of Things (IoT) applications. The low-power implementation of an SNN SoC requires the optimization of not only the SNN model but also the architecture and circuit designs. In this work, the SNN has been constituted from the analog neuron and synaptic circuits, which are designed to optimize both the chip area and power consumption. The proposed synapse circuit is based on a current multiplier charge injector (CMCI) circuit, which can significantly reduce power consu

APA, Harvard, Vancouver, ISO, and other styles

19

Marrero, Dailin, John Kern, and Claudio Urrea. "A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks." Sensors 24, no. 2 (2024): 491. http://dx.doi.org/10.3390/s24020491.

Full text

Abstract:

This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Ba

APA, Harvard, Vancouver, ISO, and other styles

20

Ordóñez-Miyar, Belén D., José A. Periáñez, Dorotea Blanco, et al. "Valoración logopédica de la relación entre succión no nutritiva y neurodesarrollo en el prematuro." Revista de Investigación en Logopedia 15, no. 2 (2025): e101738. https://doi.org/10.5209/rlog.101738.

Full text

Abstract:

La succión es un reflejo necesario para la supervivencia y el prematuro suele presentar problemas en el desarrollo de la succión. Además, es expresión primaria del funcionamiento cerebral, por lo que aumentan los estudios que la relacionan con los movimientos generales (MG) y el neurodesarrollo posterior. Actualmente, la herramienta más usada para evaluarla, es la escala NOMAS, pero los patrones de succión descritos en esta escala como desorganizados muestra una correlación poco fiable con el neurodesarrollo. Por esto, se planteó analizar en profundidad la evolución de la succión no nutritiva

APA, Harvard, Vancouver, ISO, and other styles

21

Shang, Ying, Yongli Li, Feng You, and RuiLian Zhao. "Conversion-based Approach to Obtain an SNN Construction." International Journal of Software Engineering and Knowledge Engineering 30, no. 11n12 (2020): 1801–18. http://dx.doi.org/10.1142/s0218194020400318.

Full text

Abstract:

Spiking Neuron Network (SNN) uses spike sequence for data processing, so it has an excellent characteristic of low power consumption. However, due to the immaturity of learning algorithm, the multiplayer network training has difficulty in convergence. Utilizing the mature learning algorithm and fast training speed of the back-propagation network, this paper proposes a method to converse the Convolutional Neural Network (CNN) to the SNN. First, the adjustment strategy for CNN is introduced. Then after training, the weight parameters in the model are extracted, which is the corresponding synapti

APA, Harvard, Vancouver, ISO, and other styles

22

Boro, Fabian Dominggus Eka, and Endang Sugiharti. "Combination of Genetic Algorithm and Spiking Neural Network Leaky Integrate-And-Fire Model in Analyzing Brain Ct Scan Image for Stroke Disease Detection." Recursive Journal of Informatics 3, no. 1 (2025): 1–14. https://doi.org/10.15294/rji.v3i1.3492.

Full text

Abstract:

Abstract. Stroke is a condition where there is impaired brain function due to lack of oxygen caused by blockage, breakdown, or blood clots inside brain. Diagnosis of stroke is usually based on symptoms, but symptoms are not always the correct measure. In examining a stroke, the most common way to examine a patient is to perform a CT scan of the brain. Purpose: This study was conducted with the aim of predicting brain scan images consisting of normal brain, ischemic stroke brain, and hemorrhagic stroke brain. It is also to understand how an algorithm works to recognize and predict an image. Met

APA, Harvard, Vancouver, ISO, and other styles

23

Lu, L., X. Wei, Y. H. Li, and W. B. Li. "Sentinel node necrosis is a negative prognostic factor in patients with nasopharyngeal carcinoma: a magnetic resonance imaging study of 252 patients." Current Oncology 24, no. 3 (2017): 220. http://dx.doi.org/10.3747/co.24.3168.

Full text

Abstract:

Purpose We explored the patterns of sentinel node metastasis and investigated the prognostic value of sentinel node necrosis (snn) in patients with nasopharyngeal carcinoma (npc), based on magnetic resonance imaging (mri).Methods This retrospective study enrolled 252 patients at our institution who had metastatic lymph nodes from biopsy-confirmed npc and who were treated with definitive radiation therapy, with or without chemotherapy. All participants underwent mri before treatment, and the resulting images were reviewed to evaluate lymph node status. The patients were divided into snn and non

APA, Harvard, Vancouver, ISO, and other styles

24

Lobov, Sergey A., Alexey I. Zharinov, Valeri A. Makarov, and Victor B. Kazantsev. "Spatial Memory in a Spiking Neural Network with Robot Embodiment." Sensors 21, no. 8 (2021): 2678. http://dx.doi.org/10.3390/s21082678.

Full text

Abstract:

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous

APA, Harvard, Vancouver, ISO, and other styles

25

Jang, Hyeonguk, Kyuseung Han, Kwang‐Il Oh, Sukho Lee, Jae‐Jin Lee, and Woojoo Lee. "SNN eXpress: Streamlining Low‐Power AI‐SoC Development With Unsigned Weight Accumulation Spiking Neural Network." ETRI Journal 46, no. 5 (2024): 829–38. http://dx.doi.org/10.4218/etrij.2024-0114.

Full text

Abstract:

AbstractSoCs with analog‐circuit‐based unsigned weight‐accumulating spiking neural networks (UWA‐SNNs) are a highly promising solution for achieving low‐power AI‐SoCs. This paper addresses the challenges that must be overcome to realize the potential of UWA‐SNNs in low‐power AI‐SoCs: (i) the absence of UWA‐SNN learning methods and the lack of an environment for developing applications based on trained SNN models and (ii) the inherent issue of testing and validating applications on the system being nearly impractical until the final chip is fabricated owing to the mixed‐signal circuit implement

APA, Harvard, Vancouver, ISO, and other styles

26

Chiu, Chih-Chin, Chen-Ya Yang, Tsui-Fen Yang, Kon-Ping Lin, Shou-Hsien Huang, and Jia-Chi Wang. "Acute Sensory Neuronopathy following Enterovirus Infection in a 3-Year-Old Girl." Neuropediatrics 48, no. 03 (2017): 190–93. http://dx.doi.org/10.1055/s-0037-1601323.

Full text

Abstract:

AbstractAcute sensory neuronopathy (SNN) is a rapidly developing peripheral nervous system disease that primarily affects sensory neurons in the dorsal root ganglion or trigeminal ganglion, leading to the impairment of sensory axons. SNN is notably uncommon in childhood; only three cases of childhood or adolescent SNN have been reported to date. Moreover, SSN has never been reported in association with enterovirus infection. Here, we report the case of a 3-year-old girl who was initially diagnosed with enterovirus infection based on the presentation of fevers, rashes on all extremities, and ul

APA, Harvard, Vancouver, ISO, and other styles

27

Hwang, Sungmin, Jeong-Jun Lee, Min-Woo Kwon, et al. "Analog Complementary Metal–Oxide–Semiconductor Integrate-and-Fire Neuron Circuit for Overflow Retaining in Hardware Spiking Neural Networks." Journal of Nanoscience and Nanotechnology 20, no. 5 (2020): 3117–22. http://dx.doi.org/10.1166/jnn.2020.17390.

Full text

Abstract:

The spiking neural network (SNN) is regarded as the third generation of an artificial neural network (ANN). In order to realize a high-performance SNN, an integrate-and-fire (I&F) neuron, one of the key elements in an SNN, must retain the overflow in its membrane after firing. This paper presents an analog CMOS I&F neuron circuit for overflow retaining. Compared with the conventional I&F neuron circuit, the basic operation of the proposed circuit is confirmed in a circuit-level simulation. Furthermore, a single-layer SNN simulation was also performed to demonstrate the effect of th

APA, Harvard, Vancouver, ISO, and other styles

28

Madenda, Sarifuddin, and Suryadi Harmanto. "NEW APPROACH OF SIGNED BINARY NUMBERS MULTIPLICATION AND ITS IMPLEMENTATION ON FPGA." Jurnal Ilmiah Teknologi dan Rekayasa 26, no. 1 (2021): 56–68. http://dx.doi.org/10.35760/tr.2021.v26i1.3703.

Full text

Abstract:

This paper proposes a new model of signed binary multiplication. This model is formulated mathematically and can handle four types of binary multipliers: signed positive numbers multiplied by signed positive numbers (SPN-by-SPN); signed positive numbers multiplied by signed negative numbers (SPN-by-SNN); signed negative numbers multiplied by signed positive numbers (SNN-by-SPN); and signed negative numbers multiplied by signed negative numbers (SNN-by-SNN). The proposed model has a low complexity algorithm, is easy to implement in software coding and integrated in a hardware FPGA (Field-Progra

APA, Harvard, Vancouver, ISO, and other styles

29

Li, Ming, Haibo Ruan, Yu Qi, Tiantian Guo, Ping Wang, and Gang Pan. "Odor Recognition with a Spiking Neural Network for Bioelectronic Nose." Sensors 19, no. 5 (2019): 993. http://dx.doi.org/10.3390/s19050993.

Full text

Abstract:

Electronic noses recognize odors using sensor arrays, and usually face difficulties for odor complicacy, while animals have their own biological sensory capabilities for various types of odors. By implanting electrodes into the olfactory bulb of mammalian animals, odors may be recognized by decoding the recorded neural signals, in order to construct a bioelectronic nose. This paper proposes a spiking neural network (SNN)-based odor recognition method from spike trains recorded by the implanted electrode array. The proposed SNN-based approach exploits rich timing information well in precise tim

APA, Harvard, Vancouver, ISO, and other styles

30

ZAVALIAGKOS, G., S. AUSTIN, J. MAKHOUL, and R. SCHWARTZ. "A HYBRID CONTINUOUS SPEECH RECOGNITION SYSTEM USING SEGMENTAL NEURAL NETS WITH HIDDEN MARKOV MODELS." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 04 (1993): 949–63. http://dx.doi.org/10.1142/s0218001493000480.

Full text

Abstract:

Up till recently, state-of-the-art, large vocabulary, continuous speech recognition (CSR) had employed hidden Markov modeling (HMM) to model speech sounds. In an attempt to improve over HMM we developed a hybrid system that integrates HMM technology with neural networks. We present the concept of a Segmental Neural Net (SNN) for phonetic modeling in CSR. By taking into account all the frames of a phonetic segment simultaneously, the SNN overcomes the well-known conditional-independence limitation of HMMs. We have developed a novel hybrid SNN/HMM system that combines the advantages of SNNs and

APA, Harvard, Vancouver, ISO, and other styles

31

Zhou, Shaoheng. "Text Classification based on Spiking Neural Network with LSTM Conversion." Applied and Computational Engineering 99, no. 1 (2024): 186–93. http://dx.doi.org/10.54254/2755-2721/99/20251772.

Full text

Abstract:

In recent years, spiking neural network (SNN) have attracted much attention due to their low energy consumption and have achieved remarkable results in the fields of vision and information processing. However, the application of SNNs in the field of natural language processing (NLP) is still relatively small. Given that current popular large-scale language models rely on huge arithmetic and energy consumption, it is of great practical importance to explore SNN-based approaches to implement NLP tasks in a more energy-efficient way. This paper investigates the conversion method from LSTM network

APA, Harvard, Vancouver, ISO, and other styles

32

Chou, Elizabeth P., and Bo-Cheng Hsieh. "Enhancing Anomaly Detection in Structured Data Using Siamese Neural Networks as a Feature Extractor." Mathematics 13, no. 7 (2025): 1090. https://doi.org/10.3390/math13071090.

Full text

Abstract:

Anomaly detection in structured data presents significant challenges, particularly in scenarios with extreme class imbalance. The Siamese Neural Network (SNN) is traditionally recognized for its ability to measure pairwise similarities, rather than being utilized as a feature extractor. However, in this study, we introduce a novel approach by leveraging the feature extraction capabilities of SNN, inspired by the powerful representation learning ability of neural networks. We integrate SNN with four different classifiers and the Synthetic Minority Over-sampling Technique (SMOTE) for supervised

APA, Harvard, Vancouver, ISO, and other styles

33

Pang, Lili, Junxiu Liu, Jim Harkin, et al. "Case Study—Spiking Neural Network Hardware System for Structural Health Monitoring." Sensors 20, no. 18 (2020): 5126. http://dx.doi.org/10.3390/s20185126.

Full text

Abstract:

This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data gen

APA, Harvard, Vancouver, ISO, and other styles

34

Mo, Lingfei, and Minghao Wang. "LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm." Electronics 10, no. 17 (2021): 2123. http://dx.doi.org/10.3390/electronics10172123.

Full text

Abstract:

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are ve

APA, Harvard, Vancouver, ISO, and other styles

35

Tan, JingDong, and RuJing Wang. "Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/295067.

Full text

Abstract:

Sharing nearest neighbor (SNN) is a novel metric measure of similarity, and it can conquer two hardships: the low similarities between samples and the different densities of classes. At present, there are two popular SNN similarity based clustering methods: JP clustering and SNN density based clustering. Their clustering results highly rely on the weighting value of the single edge, and thus they are very vulnerable. Motivated by the idea of smooth splicing in computing geometry, the authors design a novel SNN similarity based clustering algorithm within the structure of graph theory. Since it

APA, Harvard, Vancouver, ISO, and other styles

36

Asghar, Malik Summair, Saad Arslan, and Hyungwon Kim. "A Low-Power Spiking Neural Network Chip Based on a Compact LIF Neuron and Binary Exponential Charge Injector Synapse Circuits." Sensors 21, no. 13 (2021): 4462. http://dx.doi.org/10.3390/s21134462.

Full text

Abstract:

To realize a large-scale Spiking Neural Network (SNN) on hardware for mobile applications, area and power optimized electronic circuit design is critical. In this work, an area and power optimized hardware implementation of a large-scale SNN for real time IoT applications is presented. The analog Complementary Metal Oxide Semiconductor (CMOS) implementation incorporates neuron and synaptic circuits optimized for area and power consumption. The asynchronous neuronal circuits implemented benefit from higher energy efficiency and higher sensitivity. The proposed synapse circuit based on Binary Ex

APA, Harvard, Vancouver, ISO, and other styles

37

Liu, Zhetong, Qiugang Zhan, Xiurui Xie, Bingchao Wang, and Guisong Liu. "Federal SNN Distillation: A Low-Communication-Cost Federated Learning Framework for Spiking Neural Networks." Journal of Physics: Conference Series 2216, no. 1 (2022): 012078. http://dx.doi.org/10.1088/1742-6596/2216/1/012078.

Full text

Abstract:

Abstract In recent years, research on the federated spiking neural network (SNN) framework has attracted increasing attention in the area of on-chip learning for embedded devices, because of its advantages of low power consumption and privacy security. Most of the existing federated SNN frameworks are based on the classical federated learning framework -- Federated Average (FedAvg) framework, where internal communication is achieved by exchanging network parameters or gradients. However, although these frameworks take a series of methods to reduce the communication cost, the communication of f

APA, Harvard, Vancouver, ISO, and other styles

38

Ngu, Huynh Cong Viet, and Keon Myung Lee. "Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks." Applied Sciences 12, no. 11 (2022): 5749. http://dx.doi.org/10.3390/app12115749.

Full text

Abstract:

Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, the CNN–SNN conversion is considered one of the most successful approaches to training SNNs. However, previous works assume a rather long inference time period called inference latency to be allowed, while having a trade-off between inference latency and accuracy. One of the main reasons for this phenomenon stems from the difficulty in determini

APA, Harvard, Vancouver, ISO, and other styles

39

Ngu, Huynh Cong Viet, and Keon Myung Lee. "Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks." Applied Sciences 12, no. 11 (2022): 5749. http://dx.doi.org/10.3390/app12115749.

Full text

Abstract:

Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, the CNN–SNN conversion is considered one of the most successful approaches to training SNNs. However, previous works assume a rather long inference time period called inference latency to be allowed, while having a trade-off between inference latency and accuracy. One of the main reasons for this phenomenon stems from the difficulty in determini

APA, Harvard, Vancouver, ISO, and other styles

40

Mavrin, I. A., E. A. Ryndin, N. V. Andreeva, and V. V. Luchinin. "Design of spiking neural network architecture based on dendritic computation principles." Genes & Cells 18, no. 4 (2023): 821–24. http://dx.doi.org/10.17816/gc623425.

Full text

Abstract:

The paper presents the hardware architecture design of a spiking neural network (SNN) based on dendritic computation principles. The integration of active dendritic properties into the neuronal structure of SNN aims to minimize the number of functional blocks required for hardware implementation, including synaptic connections and neurons. The available memory on the neuromorphic architecture imposes limitations on implementation, hence the need to reduce the number of functional blocks. As a test task for the SNN based on dendritic computations, we selected the image classification of eight s

APA, Harvard, Vancouver, ISO, and other styles

41

Corredoira, Imanol. "Measurements of collectivity in the forward region at LHCb." EPJ Web of Conferences 276 (2023): 01022. http://dx.doi.org/10.1051/epjconf/202327601022.

Full text

Abstract:

Due to its unique pseudorapidity coverage (2 < 77 < 5) and excellent performance at low pT, the LHCb detector provides measurements of two-particle correlation in a complementary region to other LHC experiments. Ongoing studies in flow at different collision systems and energies, pPb collisions at √SNN = 8 TeV, PbPb collisions at √SNN = 5 TeV, and Bose-Einstein correlations of identical pions for pPb collisions at √SNN = 5 TeV, are summarised.

APA, Harvard, Vancouver, ISO, and other styles

42

Das, Debasish. "HBT Radii: Comparative Studies on Collision Systems and Beam Energies." Advances in High Energy Physics 2018 (July 15, 2018): 1–5. http://dx.doi.org/10.1155/2018/3794242.

Full text

Abstract:

Two-particle Hanbury-Brown-Twiss (HBT) interferometry is an important probe for understanding the space-time structure of particle emission sources in high energy heavy ion collisions. We present the comparative studies of HBT radii in Pb+Pb collisions at sNN = 17.3 GeV with Au+Au collisions at sNN = 19.6 GeV. To further understand this specific energy regime, we also compare the HBT radii for Au+Au collisions at sNN = 19.6 GeV with Cu+Cu collisions at sNN = 22.4 GeV. We have found interesting similarity in the Rout/Rside ratio with mT across the collision systems while comparing the data for

APA, Harvard, Vancouver, ISO, and other styles

43

Han, Yanan, Shuiying Xiang, Yuna Zhang, Shuang Gao, Aijun Wen, and Yue Hao. "An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning." Photonics 9, no. 2 (2022): 120. http://dx.doi.org/10.3390/photonics9020120.

Full text

Abstract:

Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects

APA, Harvard, Vancouver, ISO, and other styles

44

Liu, Qiuchen, Chang Liu, Songtao Cai, et al. "A new near-infrared fluorescent probe for sensing extreme acidity and bioimaging in lysosome." Methods and Applications in Fluorescence 10, no. 2 (2022): 024002. http://dx.doi.org/10.1088/2050-6120/ac4e73.

Full text

Abstract:

Abstract Since the intracellular pH plays an important role in the physiological and pathological processes, however, the probes that can be used for monitoring pH fluctuation under extreme acidic conditions are currently rare, so it is necessary to construct fluorescent probes for sensing pH less than 4. In this work, we developed a new near-infrared (NIR) fluorescent probe Cy-SNN for sensing pH fluctuation under extremely acidic conditions. For the preparation of this probe, benzothiozolium moiety was chosen as lysosomal targeting unit and NIR fluorophore, and barbituric acid moiety was fuse

APA, Harvard, Vancouver, ISO, and other styles

45

Fu, Qiang, and Hongbin Dong. "A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition." Computational Intelligence and Neuroscience 2022 (October 13, 2022): 1–14. http://dx.doi.org/10.1155/2022/4242235.

Full text

Abstract:

Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive

APA, Harvard, Vancouver, ISO, and other styles

46

Qin, Panke, Yongjie Ding, Ya Li, et al. "Spiking Neural Networks Optimized by Improved Cuckoo Search Algorithm: A Model for Financial Time Series Forecasting." Algorithms 18, no. 5 (2025): 262. https://doi.org/10.3390/a18050262.

Full text

Abstract:

Financial Time Series Forecasting (TSF) remains a critical challenge in Artificial Intelligence (AI) due to the inherent complexity of financial data, characterized by strong non-linearity, dynamic non-stationarity, and multi-factor coupling. To address the performance limitations of Spiking Neural Networks (SNNs) caused by hyperparameter sensitivity, this study proposes an SNN model optimized by an Improved Cuckoo Search (ICS) algorithm (termed ICS-SNN). The ICS algorithm enhances global search capability through piecewise-mapping-based population initialization and introduces a dynamic disco

APA, Harvard, Vancouver, ISO, and other styles

47

Liu, Yuqian, Chujie Zhao, Yizhou Jiang, Ying Fang, and Feng Chen. "LDD: High-Precision Training of Deep Spiking Neural Network Transformers Guided by an Artificial Neural Network." Biomimetics 9, no. 7 (2024): 413. http://dx.doi.org/10.3390/biomimetics9070413.

Full text

Abstract:

The rise of large-scale Transformers has led to challenges regarding computational costs and energy consumption. In this context, spiking neural networks (SNNs) offer potential solutions due to their energy efficiency and processing speed. However, the inaccuracy of surrogate gradients and feature space quantization pose challenges for directly training deep SNN Transformers. To tackle these challenges, we propose a method (called LDD) to align ANN and SNN features across different abstraction levels in a Transformer network. LDD incorporates structured feature knowledge from ANNs to guide SNN

APA, Harvard, Vancouver, ISO, and other styles

48

Hulea, Mircea, George Iulian Uleru, and Constantin Florin Caruntu. "Adaptive SNN for Anthropomorphic Finger Control." Sensors 21, no. 8 (2021): 2730. http://dx.doi.org/10.3390/s21082730.

Full text

Abstract:

Anthropomorphic hands that mimic the smoothness of human hand motions should be controlled by artificial units of high biological plausibility. Adaptability is among the characteristics of such control units, which provides the anthropomorphic hand with the ability to learn motions. This paper presents a simple structure of an adaptive spiking neural network implemented in analogue hardware that can be trained using Hebbian learning mechanisms to rotate the metacarpophalangeal joint of a robotic finger towards targeted angle intervals. Being bioinspired, the spiking neural network drives actua

APA, Harvard, Vancouver, ISO, and other styles

49

Nichols, Eric, Liam J. McDaid, and Nazmul Siddique. "Biologically Inspired SNN for Robot Control." IEEE Transactions on Cybernetics 43, no. 1 (2013): 115–28. http://dx.doi.org/10.1109/tsmcb.2012.2200674.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Ali, Aziz Nasser Boraik, Hassan Pyar Ali Hassan, and Hesham Bahamish. "SNN-SB: Combining Partial Alignment Using Modified SNN Algorithm with Segment-Based for Multiple Sequence Alignments." Journal of Physics: Conference Series 1962, no. 1 (2021): 012048. http://dx.doi.org/10.1088/1742-6596/1962/1/012048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!