Relevant bibliographies by topics / SNN

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'SNN'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "SNN"

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 (July 14, 2016): 397–403. http://dx.doi.org/10.36076/ppj/2016.19.397.

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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 drawing and demonstrate psychometric generalizability among patients with cancerrelated pain. Using the Wilcoxon signed rank test and associated effect sizes, results show significant improvement in dispersion of pain following SNN. Effect sizes for the difference in pre-SNN to 2 post-SNN time points were higher for the pain drawing than for pain intensity rating. Specifically, the effect size difference from pre- to within one month post-SNN was r = 0.42 for pain drawing versus r = 0.23 for pain intensity rating. Based on a smaller subset of patients who were seen within 1 – 6 months following SNN, the effect size difference from pre-SNN was r = 0.46 for pain drawing versus r = 0.00 for pain intensity rating. Collectively, these data support the use of the pain drawing as a reliable outcome measure among patients with cancer pain for procedures such as SNN that target specific location and dispersion of pain. Key words: Cancer pain, pain drawing, splanchnic block
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Al-Hamid, Ali A., and HyungWon Kim. "Optimization of Spiking Neural Networks Based on Binary Streamed Rate Coding." Electronics 9, no. 10 (September 29, 2020): 1599. http://dx.doi.org/10.3390/electronics9101599.

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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. We also present SNN optimization methods including structure optimization and weight quantization. Extensive evaluations with MNIST SNNs demonstrate that the structure optimization of SNN (81-30-20-10) provides 183.19 times reduction in hardware compared with SNN (784-800-10), while providing an accuracy of 95.25%, a small loss compared with 98.89% and 98.93% reported in the previous works. Our weight quantization reduces 32-bit weights to 4-bit integers leading to further hardware reduction of 4 times with only 0.56% accuracy loss. Overall, the SNN model (81-30-20-10) optimized by our method shrinks the SNN’s circuit area from 3089.49 mm2 for SNN (784-800-10) to 4.04 mm2—a reduction of 765 times.
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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 (August 2006): 141–50. http://dx.doi.org/10.1590/s0104-56872006000200003.

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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® e Grupo 3 (G3), com estimulação da SNN através do dedo enluvado. Os três grupos foram submetidos a avaliação semanal da SNN com dedo enluvado e, após o início da alimentação por via oral (VO), avaliação da SNN e da sucção nutritiva (SN) com mini-mamadeira. RESULTADOS: nos três grupos, com o aumento da IG corrigida, elevou-se a probabilidade de ocorrência de todas as características da sucção estudadas (SNN e SN), exceto sinais de estresse na SNN e coordenação sucção-deglutição-respiração na SN. Na SNN: sucção iniciada facilmente (SIF), ritmo, força e coordenação lábios, língua e mandíbula, sem diferenças entre os grupos; probabilidade maior de vedamento labial, acanolamento, peristaltismo no G3 e de sinais de estresse no G2 (> 37 semanas). Na sucção nutritiva (SN): SIF, coordenação movimentos de lábios-língua e mandíbula, volume de leite ingerido pelo tempo total sem diferenças entre os grupos; ritmo e coordenação sucção-deglutição-respiração superior no G3; vedamento labial superior nos G1 e 3 (< 34 semanas) e sinais de estresse superior no G2 (> 33 semanas). CONCLUSÃO: o padrão de sucção de RNPT evoluiu em função da IG corrigida, tendo a estimulação da SNN aumentado a probabilidade de ocorrência de vedamento labial, ritmo, acanolamento, peristaltismo e coordenação sucção-deglutição-respiração, sendo o dedo enluvado a forma mais eficaz de estimulação da SNN.
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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 (September 25, 2019): 1950004. http://dx.doi.org/10.1142/s0129065719500047.

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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 model (SSNN) is compared with previous SSNN models, achieving a higher processing speed. We also show how the proposed model can be scaled to high-volume neural networks trained by using back propagation and applied to a pattern classification task. The proposed model achieves better results compared with other recently-published SNN models configured with unsupervised STDP learning.
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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.

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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) and interleukin-6 (IL-6) was assessed. By using microarray chips, we investigated differentially expressed microRNA profiles to decipher the underlining mechanisms of SNN-containing serum. It was revealed that SNN-containing serum decreased TNF-α and IL-6 expression, and microRNA-152 was identified as the potential epigenetic regulator. We further verified the pharmacological effects in Raw264.7 cells; while transfection with miRNA-152 mimics could reduce TNF-α and IL-6, transfection with miRNA-152 inhibitor blocked the anti-inflammatory effect of SNN-containing serum. These results suggested that SNN-containing serum could improve inflammation in LPS stressed Kupffer cells and macrophages via upregulating microRNA-152.
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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 (December 2010): 501–8. http://dx.doi.org/10.1142/s0129065710002577.

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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. Results are presented for a wall following application.
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Aoun, Mario Antoine. "A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons." International Journal of Artificial Intelligence & Applications 12, no. 03 (May 31, 2021): 25–33. http://dx.doi.org/10.5121/ijaia.2021.12303.

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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.
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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 (November 2020): 1801–18. http://dx.doi.org/10.1142/s0218194020400318.

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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 synaptic weight in the layer of the SNN. Finally, a new threshold-setting algorithm based on feedback is proposed to solve the critical problem of the threshold setting of neurons in the SNN. We evaluate our method on the CIFAR-10 datasets released by Hinton’s team. The experimental results show that the image classification accuracy of the SNN is more than 98% of that of CNN, and the theoretical value of power consumption per second is 3.9[Formula: see text]mW.
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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 (June 28, 2017): 220. http://dx.doi.org/10.3747/co.24.3168.

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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-snn groups. Overall survival (os), tumour-free survival (tfs), regional relapse–free survival (rrfs), and distant metastasis–free survival (dmfs) were calculated by the Kaplan–Meier method, and differences were compared using the log-rank test. Factors predictive of outcome were determined by univariate and multivariate analysis.Results Of the 252 patients, 189 (75%) had retropharyngeal lymph node metastasis, and 189 (75%) had level iia or iib lymph node necrosis. The incidence of snn was 43.4% (91 of 210 patients with lymph node metastasis or necrosis, or both). After a median follow-up of 54 months, the 5-year rates of os, tfs, rrfs, and dmfs in the snn and non-snn groups were, respectively, 79.4% and 95.3%, 73.5% and 93.3%, 80.4% and 96.6%, and 75.5% and 95.3% (all p < 0.01). Age greater than 40 years, snn, T stage, and N stage were significant independent negative prognostic factors for os, tfs, rrfs, and dmfs.Conclusions Metastatic retropharyngeal lymph nodes and necrotic level ii nodes both seem to act as sentinels. Sentinel node necrosis is an negative prognostic factor in patients with npc. Patients with snn have a worse prognosis.
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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 (March 23, 2017): 190–93. http://dx.doi.org/10.1055/s-0037-1601323.

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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 ulceration over the posterior pharynx. Nine days later, she presented with ataxic and wide-based gait and dysmetria affecting the extremities, with an absence of sensory nerve action potentials in the upper and lower limbs. The patient was diagnosed with acute SNN based on the criteria developed by Camdessanché et al in 2009. To our knowledge, this is the youngest case of SNN reported to date. In addition, this case reveals that enterovirus infection can be associated with acute SNN in children in rare cases. Accurate diagnosis relies on clinical suspicion, comprehensive knowledge of the patient's history, and careful characterization of abnormal findings in electrodiagnostic studies.
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Dissertations / Theses on the topic "SNN"

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Parana-Liyanage, Krishani. "Outlier detection in spatial data using the m-SNN algorithm." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 2013. http://digitalcommons.auctr.edu/dissertations/1299.

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Outlier detection is an important topic in data analysis because of its applications to numerous domains. Its application to spatial data, and in particular spatial distribution in path distributions, has recently attracted much interest. This recent trend can be seen as a reflection of the massive amounts of spatial data being gathered through mobile devices, sensors and social networks. In this thesis we propose a nearest neighbor distance based method the Modified-Shared Nearest Neighbor outlier detection (m-SNN) developed for outlier detection in spatial domains. We modify the SNN technique for use in outlier detection, and compare our approach with the widely used outlier detection technique, the LOF Algorithm and a base Gaussian approach. It is seen that the m-SNN compares well with the LOF in simple spatial data distributions and outperforms it in more complex distributions. Experimental results of using buoy data to track the path of a hurricane are also shown.
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Itani, Aashish. "COMPARISON OF ADVERSARIAL ROBUSTNESS OF ANN AND SNN TOWARDS BLACKBOX ATTACKS." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2864.

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n recent years, the vulnerability of neural networks to adversarial samples has gained wide attention from machine learning and deep learning communities. Addition of small and imperceptible perturbations to the input samples can cause neural network models to make incorrect prediction with high confidence. As the employment of neural networks on safety critical application is rising, this vulnerability of traditional neural networks to the adversarial samples demand for more robust alternative neural network models. Spiking Neural Network (SNN), is a special class of ANN, which mimics the brain functionality by using spikes for information processing. The known advantages of SNN include fast inference, low power consumption and biologically plausible information processing. In this work, we experiment on the adversarial robustness of the SNN as compared to traditional ANN, and figure out if SNN can be a candidate to solve the security problems faced by ANN.
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Parker, William Chesluk. "CENTRALITY DEPENDENCE OF BULK FIREBALL PROPERTIES IN /Snn=62.4 and 200 GeV." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/192559.

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Zapata, Rodriguez Mireya. "Arquitectura escalable SIMD con conectividad jerárquica y reconfigurable para la emulación de SNN." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461085.

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A biological neural system consists of millions of highly integrated neurons with multiple dynamic functions operating in coordination with each other. Its structural organization is characterized by highly hierarchical assemblies. These assemblies are distinguished by locally dense and globally ispersed connections communicated by spikes traveling through the axon to the target neuron. In the last century, approaching the biological complexity of the cortex by means of hardware architectures has continued to be a challenge still unattainable. This is not only due to the massively parallel processing with support for the communication between neurons in large-scale networks, but also for the need of mechanisms that allow the evolution of the neural network efficiently. In this context, this thesis contributes to the development of an architecture called HEENS (Hardware Emulator of Evolved Neural System), which supports inter-chip connectivity with a ring topology between a Master Chip (MC) controlling one or more Neuromorphic Chips (NCs). The MC is implemented in a PSoC device that integrates a CPU ARM Dual Core together with programmable logic. The ARM is responsible for setting up the communication ring and executing the software application that controls the data configuration transmission from the algorithm and the neural parameters to all NCs in the network. Besides, the MC is in charge of activating the evolution mode of the network, as well as managing the dispatching of reconfiguration data to any of the nodes during the execution. Each NC, in turn, consists of a configurable 2D array of Processing Elements (PEs) with a SIMD-like processing scheme implemented on a Kintex7 FPGA. NCs are SNN multiprocessors that support the execution of any neural algorithm based on spikes. A set of custom instructions was designed specifically for this architecture. The NCs support a hierarchical scheme of local and global spikes to mimic the brain structural configuration. Local spikes establish inter-neuronal connectivity within a single chip and the global ones allow inter-modular communication between different chips. The NCs have fixed hub neurons that process local and global spikes, thus allowing inter-modular and intra-modular connectivity. This definition of local and global spikes allows the development of multi-level hierarchical architectures inspired by the brain topologies, and offers excellent scalability. The spike propagation through the multi-chip network is supported by an Aurora / AER-SRT protocol stack. The Aurora protocol encapsulates and de-capsulates the packets transmitted through a high-speed serial link that communicates the platform, while the Synchronous Address Event representation (AER-SRT) protocol manages the data (address events) and controls packets that allow synchronization of the operation of the neural network. Each event encapsulates the address neuron that fires a spike as result of the neural algorithm execution. The definition of local and global synaptic topology is implemented using on-chip RAM blocks, which reduces the combinational logic requirements and, in addition to allowing the dynamic connectivity configuration, permits the development of evolutionary applications by supporting the on-line reconfiguration of both the neural algorithm or the neural and synaptic parameters. HEENS also supports axon programmable delays, which incorporates dynamic features to the network.
Un sistema neuronal biológico consiste de millones de neuronas altamente integradas con múltiples funciones dinámicas operando en coordinación entre sí. Su organización estructural se caracteriza por contener agrupaciones altamente jerárquicas. Dichas agrupaciones se distinguen por conexiones localmente densas y globalmente dispersas comunicadas a través de pulsos transitorios (spikes) que viajan por el axón hasta la neurona destino. En el último siglo, aproximarse a la complejidad biológica del cortex mediante arquitecturas de hardware continúa siendo un desafío todavía inalcanzable. Esto se debe, no sólo al masivo procesamiento paralelo con soporte para la comunicación entre neuronas en redes de gran escala, sinó también a la necesidad de mecanismos que permitan la evolución de la red neuronal de forma eficiente. En este marco, esta tesis contribuye al desarrollo de una arquitectura denominada HEENS (Emulador de Hardware para Sistemas Neuronales Evolutivos, Hardware Emulator of Evolved Neural System) que soporta conectividad inter-chip con una topología de anillo entre un chip que actúa de master (MC) y uno o más Chips Neuromórficos (NCs). El MC está implementado en un dispositivo PSoC que integra un CPU ARM Dual Core junto con lógica programable. El ARM se encarga de configurar el anillo de comunicación y de ejecutar la aplicación de software que controla el envío de información de configuración del algoritmo y los parámetros neuronales a todos los NCs de la red. Además, el MC es el encargado de activar el modo de evolución de la red, así como de gestionar el envío de datos de reconfiguración a cualquiera de los nodos durante la ejecución. Cada NC a su vez, está compuesto por un arreglo 2D parametrizable de Elementos de Procesamiento (Processing Elements, PEs) con un esquema de procesamiento tipo SIMD implementado sobre una FPGA Kintex7. Los NCs son multiprocesadores SNN que soportan la ejecución de cualquier algoritmo neuronal basado en spikes. Se cuenta con un set de instrucciones personalizadas diseñadas específicamente para esta arquitectura. Imitando la configuración estructural del cerebro los NC soportan un esquema jerárquico con spikes locales y globales. Los spikes locales establecen la conectividad inter-neuronal dentro de un mismo chip, y los globales la comunicación inter-modular entre diferentes chips. Los NC cuentan con neuronas fijas tipo hub que procesan spikes locales y globales que permiten la conectividad inter e intra modulos. La definición de spikes locales y globales permite desarrollar arquitecturas jerárquicas multi-nivel que se inspiran en las topologías del cerebro y ofrecen una escalabilidad excelente. La propagación de spikes a través de la red multi-chip es soportada por una pila de protocolos Aurora/AER-SRT. El protocolo Aurora encapsula y desencapsula los paquetes transmitidos a través del enlace serial de alta velocidad que comunica la plataforma. Mientras que el protocolo Síncrono de Representación de Eventos de Dirección (AER-SRT) gestiona los datos (eventos de dirección) y los paquetes de control que permiten sincronizar la operación de la red neuronal. Cada evento encapsula la dirección de la neurona que genera un spike como resultado del procesamiento del algoritmo neuronal. La definición de topología sináptica local y global es implementada usando bloques de memoria RAM on-chip, lo que reduce los requerimientos de lógica combinacional y, además de facilitar la configuración del conexionado sin modificar el hardware, permite el desarrollo de aplicaciones evolutivas al soportar la reconfiguración on-line tanto del algoritmo neuronal como de los parámetros neuronales y sinápticos. HEENS también admite retardos programables de axón, lo cual incorpora características dinámicas a la red.
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Johnson, Ian Jeffrey. "Photon and [pi]⁰ production in ¹⁹⁷Au+¹⁹⁷Au collisions at [square root]SNN=130GeV /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.

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Quintero, Amilkar. "MEASUREMENT OF CHARM MESON PRODUCTION IN Au+Au COLLISIONS ATsqrt(SNN) =200 GeV." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1460734511.

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Appelt, Eric. "Measurements of Charged-Particle Transverse Momentum Spectra in PbPb Collisions at Square Root of SNN = 2|76 TeV and in pPb Collisions at Square Root of SNN = 5|02 TeV with the CMS Detector." Thesis, Vanderbilt University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3584408.

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Ahmed, Samah. "Measurement of Neutral Mesons in p-Pb collisions at √sNN = 8.16TeV with the ALICE detector." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31492.

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The measured transverse momentum spectra of neutral pion π 0 and η mesons are presented for p-Pb collisions at √ sNN = 8.16 TeV using the photon conversion method for the signal extraction. This method uses the tracking and particle identification capabilities of the central barrel detectors of ALICE. Signal extracted down to 0.3 GeV/c and 0.7 GeV/c for π 0 and η respectively. The resulting spectra are presented and systematic uncertainties have been evaluated. A suppression of the yield compared to pp collisions at the same center of mass energy is observed in RpA for both mesons. Comparisons to theory predictions show consistency with the spectra and RpA.
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Castillo, Javier. "Production de particules doublement étranges dans les collisions d'ions lourds ultra-relativistes à √SNN = 130 GeV." Paris 7, 2002. http://www.theses.fr/2002PA077043.

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Raisig, Pascal [Verfasser], Harald [Gutachter] Appelshäuser, and Christoph [Gutachter] Blume. "J/ψ production in √sNN = 5.02 TeV Pb−Pb collisions / Pascal Raisig ; Gutachter: Harald Appelshäuser, Christoph Blume." Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2020. http://d-nb.info/1212509749/34.

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Books on the topic "SNN"

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Schuchmann, Simone. Modification of K0s and Lambda(AntiLambda) Transverse Momentum Spectra in Pb-Pb Collisions at √sNN = 2.76 TeV with ALICE. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43458-2.

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Kappen, Bert. Neural Networks: Artificial Intelligence and Industrial Applications: Proceedings of the Third Annual SNN Symposium on Neural Networks, Nijmegen, the Netherlands, 14-15 September 1995. London: Springer London, 1995.

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SNN Symposium on Neural Networks (3rd 1995 Nijmegen, Netherlands). Neural networks: Artificial intelligence and industrial applications : proceedings of the Third Annual SNN Symposium on Neural Networks, Nijmegen, The Netherlands, 14-15 September 1995. Berlin: Springer, 1995.

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Holka, Peter. Sen o sne. Bratislava: Slovenský spisovatel̕, 1998.

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Anna, Gralak, ed. Liczenie baranów: O naturze i przyjemnościach snu. Warszawa: Warszawskie Wydawnictwo Literackie "Muza", 2011.

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Qing nian Sun Zhongshan. 2nd ed. Beijing Shi: Zhongguo she hui chu ban she, 2008.

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San min zhu yi guan li si xiang yan jiu. Taibei Shi: Jin yu chu ban she, 1985.

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yi, Duan Zhang qu, ed. Hu wa san jin sen lin. Guangzhou Shi: Xin shi ji chu ban she, 2016.

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Lian: Wang Shangzheng san sen ji. [Beijing]: Zhongguo you yi chu ban gong si, 1989.

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Xing, Heping. Jianpuzhai san chao zong li: Hong Sen. Jinbian: Jianpuzhai "Hua shang ri bao" she, 2001.

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Book chapters on the topic "SNN"

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Baumann, Norbert, Hans-Jürgen Fachmann, Reimund Jotter, and Alfons Kubny. "Dinitrogen Sulfide, SNN." In S Sulfur-Nitrogen Compounds, 51–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-06351-4_5.

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Baumann, Norbert, Hans-Jürgen Fachmann, Reimund Jotter, and Alfons Kubny. "Dinitrogen Sulfide, SNN." In Sulfur-Nitrogen Compounds, 51–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-06354-5_5.

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Aguilar, Jesús S., Roberto Ruiz, José C. Riquelme, and Raúl Giráldez. "SNN: A Supervised Clustering Algorithm." In Engineering of Intelligent Systems, 207–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45517-5_24.

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Kasabov, Nikola K. "Brain-Computer Interfaces Using Brain-Inspired SNN." In Springer Series on Bio- and Neurosystems, 479–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57715-8_14.

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Meng, Minrui, Xingbo Wang, and Xiaotao Wang. "Adaptive SNN Torque Control for Tendon-Driven Fingers." In Communications in Computer and Information Science, 231–41. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6370-1_23.

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Huang, B. Q., and M. T. Kechadi. "An HMM-SNN Method for Online Handwriting Symbol Recognition." In Lecture Notes in Computer Science, 897–905. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11867661_81.

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Shuwei, Wang, Wang Baosheng, Yong Tang, and Yu Bo. "Malware Clustering Based on SNN Density Using System Calls." In Cloud Computing and Security, 181–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27051-7_16.

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Kasabov, Nikola K. "Evolutionary- and Quantum-Inspired Computation. Applications for SNN Optimisation." In Springer Series on Bio- and Neurosystems, 245–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57715-8_7.

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Pandey, Sriniwas, Mamata Samal, and Sraban Kumar Mohanty. "An SNN-DBSCAN Based Clustering Algorithm for Big Data." In Advances in Intelligent Systems and Computing, 127–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1081-6_11.

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Antunes, Arménio, Maribel Yasmina Santos, and Adriano Moreira. "Fast SNN-Based Clustering Approach for Large Geospatial Data Sets." In Connecting a Digital Europe Through Location and Place, 179–95. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03611-3_11.

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Conference papers on the topic "SNN"

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Guo, Lilu, Shugui Bu, Yongjin Gan, Jianbo Lu, and Xiaoshu Zhu. "SNN-Cliq++." In AICSconf '20: 2020 Artificial Intelligence and Complex Systems Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3407703.3407731.

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Ding, Jianhao, Zhaofei Yu, Yonghong Tian, and Tiejun Huang. "Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/321.

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Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train deep SNNs is through ANN-SNN conversion. However, the conversion usually suffers from accuracy loss and long inference time, which impede the practical application of SNN. In this paper, we theoretically analyze ANN-SNN conversion and derive sufficient conditions of the optimal conversion. To better correlate ANN-SNN and get greater accuracy, we propose Rate Norm Layer to replace the ReLU activation function in source ANN training, enabling direct conversion from a trained ANN to an SNN. Moreover, we propose an optimal fit curve to quantify the fit between the activation value of source ANN and the actual firing rate of target SNN. We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference. Our theory can explain the existing work on fast reasoning and get better results. The experimental results show that the proposed method achieves near loss-less conversion with VGG-16, PreActResNet-18, and deeper structures. Moreover, it can reach 8.6× faster reasoning performance under 0.265× energy consumption of the typical method. The code is available at https://github.com/DingJianhao/OptSNNConvertion-RNL-RIL.
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Zeng-fang, Yang, and Tang He-wen. "SNN Neighbor and SNN Density-based co-location pattern discovery." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5885287.

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Liu, Qianhui, Dong Xing, Huajin Tang, De Ma, and Gang Pan. "Event-based Action Recognition Using Motion Information and Spiking Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/240.

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Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.
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Fang, Haowen, Amar Shrestha, Ziyi Zhao, and Qinru Qiu. "Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/388.

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The recently discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. Synapses and neurons behave as filters capable of preserving temporal information. As such neuron dynamics and filter effects are ignored in existing training algorithms, the SNN downgrades into a memoryless system and loses the ability of temporal signal processing. Furthermore, spike timing plays an important role in information representation, but conventional rate-based spike coding models only consider spike trains statistically, and discard information carried by its temporal structures. To address the above issues, and exploit the temporal dynamics of SNNs, we formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We proposed a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights. The proposed model and training algorithm are applied to construct associative memories and classifiers for synthetic and public datasets including MNIST, NMNIST, DVS 128 etc. Their accuracy outperforms state-of-the-art approaches.
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Cheng, Xiang, Yunzhe Hao, Jiaming Xu, and Bo Xu. "LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/211.

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Spiking Neural Network (SNN) is considered more biologically plausible and energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm has been utilized for training SNN, which allows SNN to go deeper and achieve higher performance. However, most existing SNN models for object recognition are mainly convolutional structures or fully-connected structures, which only have inter-layer connections, but no intra-layer connections. Inspired by Lateral Interactions in neuroscience, we propose a high-performance and noise-robust Spiking Neural Network (dubbed LISNN). Based on the convolutional SNN, we model the lateral interactions between spatially adjacent neurons and integrate it into the spiking neuron membrane potential formula, then build a multi-layer SNN on a popular deep learning framework, i.\,e., PyTorch. We utilize the pseudo-derivative method to solve the non-differentiable problem when applying backpropagation to train LISNN and test LISNN on multiple standard datasets. Experimental results demonstrate that the proposed model can achieve competitive or better performance compared to current state-of-the-art spiking neural networks on MNIST, Fashion-MNIST, and N-MNIST datasets. Besides, thanks to lateral interactions, our model processes stronger noise-robustness than other SNN. Our work brings a biologically plausible mechanism into SNN, hoping that it can help us understand the visual information processing in the brain.
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Hulea, Mircea, George-Iulian Uleru, Adrian Burlacu, and Constantin-Florin Caruntu. "Bioinspired SNN for robotic joint control." In 2020 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). IEEE, 2020. http://dx.doi.org/10.1109/aqtr49680.2020.9129887.

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Moreira, Guilherme, Maribel Yasmina Santos, and Joao Moura-Pires. "SNN Input Parameters: How Are They Related?" In 2013 International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2013. http://dx.doi.org/10.1109/icpads.2013.89.

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Bautembach, Dennis, Iason Oikonomidis, Nikolaos Kyriazis, and Antonis Argyros. "Faster and Simpler SNN Simulation with Work Queues." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206752.

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Menaka, R., Harathi Devi Nalla, V. Varsha, and M. ThangaAarthy. "SNN Based Brain Connectivity Analysis for ASD Children." In 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA). IEEE, 2019. http://dx.doi.org/10.1109/icsima47653.2019.9057348.

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Reports on the topic "SNN"

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KURIHARA, N., H. HAMAGAKI, K. OZAWA, T. SAKAGUCHI, T. CHUJO, and S. ESUMI. MEASUREMENT OF CHARGED HADRON SPECTRA IN AU+AU COLLISION AT SNN = 62.4 GEV AT RHIC-PHENIX. Office of Scientific and Technical Information (OSTI), October 2005. http://dx.doi.org/10.2172/859893.

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LINDENBAUM, S. J., and R. S. LONGACRE. PARTON BUBBLE MODEL COMPARED WITH RHIC CENTRAL AU+AU DELTA PHI DELTA ETA CORRELATIONS AT SQRT SNN = 200 GEV. Office of Scientific and Technical Information (OSTI), July 2006. http://dx.doi.org/10.2172/890922.

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Bryan, Charles R., and Eric John Schindelholz. FY18 Status Report: SNL Research into Stress Corrosion Cracking of SNF Interim Storage Canisters. Office of Scientific and Technical Information (OSTI), November 2018. http://dx.doi.org/10.2172/1481507.

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Schaller, Rebecca, Andrew William Knight, Charles R. Bryan, and Eric John Schindelholz. FY19 Status Report: SNL Research into Stress Corrosion Cracking of SNF Dry Storage Canisters. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1569157.

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Yen, Hung-Ju. Organic Synthetic Advanced Materials for Optoelectronic and Energy Applications (at National Sun Yat-sen University). Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1332217.

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Diaz, Aaron A., David L. Baldwin, Anthony D. Cinson, Anthony M. Jones, Michael R. Larche, Royce Mathews, Crystal A. Mullen, et al. Identify and Quantify the Mechanistic Sources of Sensor Performance Variation Between Individual Sensors SN1 and SN2. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1339935.

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Stevens, Michael. SSN 774 Virginia Class Submarine (SSN 774). Fort Belvoir, VA: Defense Technical Information Center, November 2015. http://dx.doi.org/10.21236/ad1019531.

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NAVY PEO (SUBMARINES) WASHINGTON NAVY YARD DC. SSN 774 Virginia Class Submarine (SSN 774). Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada615033.

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Smith, Rhett. SDN Project. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1367558.

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Behl, W., B. Sterling, and W. Teskey. Advanced SNA/IP : A Simple SNA Transport Protocol. RFC Editor, October 1993. http://dx.doi.org/10.17487/rfc1538.

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