Relevant bibliographies by topics / Pulse-Coupled Neural Networks

Academic literature on the topic 'Pulse-Coupled Neural Networks'

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Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Pulse-Coupled Neural Networks"

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Wang, Zhaobin, Yide Ma, Feiyan Cheng, and Lizhen Yang. "Review of pulse-coupled neural networks." Image and Vision Computing 28, no. 1 (January 2010): 5–13. http://dx.doi.org/10.1016/j.imavis.2009.06.007.

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Ota, Y., and B. M. Wilamowski. "Analog implementation of pulse-coupled neural networks." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 539–44. http://dx.doi.org/10.1109/72.761710.

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Ranganath, H. S., and G. Kuntimad. "Object detection using pulse coupled neural networks." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 615–20. http://dx.doi.org/10.1109/72.761720.

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Olmi, S., A. Politi, and A. Torcini. "Collective chaos in pulse-coupled neural networks." EPL (Europhysics Letters) 92, no. 6 (December 1, 2010): 60007. http://dx.doi.org/10.1209/0295-5075/92/60007.

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Monica Subashini, M., and Sarat Kumar Sahoo. "Pulse coupled neural networks and its applications." Expert Systems with Applications 41, no. 8 (June 2014): 3965–74. http://dx.doi.org/10.1016/j.eswa.2013.12.027.

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WANG, Xin, Yi-de MA, Zhi-jian XU, and Lian-feng LI. "Chaos control based on pulse-coupled neural networks." Journal of Computer Applications 29, no. 12 (March 1, 2010): 3277–79. http://dx.doi.org/10.3724/sp.j.1087.2009.03277.

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Kuntimad, G., and H. S. Ranganath. "Perfect image segmentation using pulse coupled neural networks." IEEE Transactions on Neural Networks 10, no. 3 (May 1999): 591–98. http://dx.doi.org/10.1109/72.761716.

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Kanamaru, Takashi, and Kazuyuki Aihara. "Rewiring-Induced Chaos in Pulse-Coupled Neural Networks." Neural Computation 24, no. 4 (April 2012): 1020–46. http://dx.doi.org/10.1162/neco_a_00252.

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The dependence of the dynamics of pulse-coupled neural networks on random rewiring of excitatory and inhibitory connections is examined. When both excitatory and inhibitory connections are rewired, periodic synchronization emerges with a Hopf-like bifurcation and a subsequent period-doubling bifurcation; chaotic synchronization is also observed. When only excitatory connections are rewired, periodic synchronization emerges with a saddle node–like bifurcation, and chaotic synchronization is also observed. This result suggests that randomness in the system does not necessarily contaminate the system, and sometimes it even introduces rich dynamics to the system such as chaos.
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Xu, Xinzheng, Guanying Wang, Shifei Ding, Yuhu Cheng, and Xuesong Wang. "Pulse-coupled neural networks and parameter optimization methods." Neural Computing and Applications 28, S1 (June 4, 2016): 671–81. http://dx.doi.org/10.1007/s00521-016-2397-2.

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He, Changtao, Fangnian Lang, and Hongliang Li. "Medical Image Registration using Cascaded Pulse Coupled Neural Networks." Information Technology Journal 10, no. 9 (August 15, 2011): 1733–39. http://dx.doi.org/10.3923/itj.2011.1733.1739.

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Dissertations / Theses on the topic "Pulse-Coupled Neural Networks"

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Swathanthira, Kumar Murali Murugavel M. "Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-dissertations/280.

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The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image 'signature' based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard's Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye's intensity delineation capability in grayscale image segmentation tasks.
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Wise, Raydiance (Raydiance Raychele). "Optoelectronic implementations of Pulse-Coupled Neural Networks : challenges and limitations." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40539.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (leaves 76-79).
This thesis examines Pulse Coupled Neural Networks (PCNNs) and their applications, and the feasibility of a compact, rugged, cost-efficient optoelectronic implementation. Simulation results are presented. Proposed optical architectures are discussed and analyzed. A new optoelectronic PCNN architecture is also presented. Tradeoffs of optical versus electronic implementations of PCNNs are discussed. This work combines concepts from optical information processing and pulse-coupled neural networks to examine the challenges, limitations, and opportunities of developing an optoelectronic pulse coupled neural network. The analysis finds that, despite advances in optoelectronic technology, fully electronic implementations will still outperform today's proposed optoelectronic implementations in cost, size, flexibility, and ease of implementation.
by Raydiance Wise.
S.M.
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Innes, Andrew, and andrew innes@defence gov au. "Genetic Programming for Cephalometric Landmark Detection." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080221.123310.

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The domain of medical imaging analysis has burgeoned in recent years due to the availability and affordability of digital radiographic imaging equipment and associated algorithms and, as such, there has been significant activity in the automation of the medical diagnostic process. One such process, cephalometric analysis, is manually intensive and it can take an experienced orthodontist thirty minutes to analyse one radiology image. This thesis describes an approach, based on genetic programming, neural networks and machine learning, to automate this process. A cephalometric analysis involves locating a number of points in an X-ray and determining the linear and angular relationships between them. If the points can be located accurately enough, the rest of the analysis is straightforward. The investigative steps undertaken were as follows: Firstly, a previously published method, which was claimed to be domain independent, was implemented and tested on a selection of landmarks, ranging from easy to very difficult. These included the menton, upper lip, incisal upper incisor, nose tip and sella landmarks. The method used pixel values, and pixel statistics (mean and standard deviation) of pre-determined regions as inputs to a genetic programming detector. This approach proved unsatisfactory and the second part of the investigation focused on alternative handcrafted features sets and fitness measures. This proved to be much more successful and the third part of the investigation involved using pulse coupled neural networks to replace the handcrafted features with learned ones. The fourth and final stage involved an analysis of the evolved programs to determine whether reasonable algorithms had been evolved and not just random artefacts learnt from the training images. A significant finding from the investigative steps was that the new domain independent approach, using pulse coupled neural networks and genetic programming to evolve programs, was as good as or even better than one using the handcrafted features. The advantage of this finding is that little domain knowledge is required, thus obviating the requirement to manually generate handcrafted features. The investigation revealed that some of the easy landmarks could be found with 100\% accuracy while the accuracy of finding the most difficult ones was around 78\%. An extensive analysis of evolved programs revealed underlying regularities that were captured during the evolutionary process. Even though the evolutionary process took different routes and a diverse range of programs was evolved, many of the programs with an acceptable detection rate implemented algorithms with similar characteristics. The major outcome of this work is that the method described in this thesis could be used as the basis of an automated system. The orthodontist would be required to manually correct a few errors before completing the analysis.
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Timoszczuk, Antonio Pedro. "Reconhecimento automático do locutor com redes neurais pulsadas." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26102004-195250/.

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As Redes Neurais Pulsadas são objeto de intensa pesquisa na atualidade. Neste trabalho é avaliado o potencial de aplicação deste paradigma neural, na tarefa de reconhecimento automático do locutor. Após uma revisão dos tópicos considerados importantes para o entendimento do reconhecimento automático do locutor e das redes neurais artificiais, é realizada a implementação e testes do modelo de neurônio com resposta por impulsos. A partir deste modelo é proposta uma nova arquitetura de rede com neurônios pulsados para a implementação de um sistema de reconhecimento automático do locutor. Para a realização dos testes foi utilizada a base de dados Speaker Recognition v1.0, do CSLU – Center for Spoken Language Understanding do Oregon Graduate Institute - E.U.A., contendo frases gravadas a partir de linhas telefônicas digitais. Para a etapa de classificação foi utilizada uma rede neural do tipo perceptron multicamada e os testes foram realizados no modo dependente e independente do texto. A viabilidade das Redes Neurais Pulsadas para o reconhecimento automático do locutor foi constatada, demonstrando que este paradigma neural é promissor para tratar as informações temporais do sinal de voz.
Pulsed Neural Networks have received a lot of attention from researchers. This work aims to verify the capability of this neural paradigm when applied to a speaker recognition task. After a description of the automatic speaker recognition and artificial neural networks fundamentals, a spike response model of neurons is tested. A novel neural network architecture based on this neuron model is proposed and used in a speaker recognition system. Text dependent and independent tests were performed using the Speaker Recognition v1.0 database from CSLU – Center for Spoken Language Understanding of Oregon Graduate Institute - U.S.A. A multilayer perceptron is used as a classifier. The Pulsed Neural Networks demonstrated its capability to deal with temporal information and the use of this neural paradigm in a speaker recognition task is promising.
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Sandmann, Humberto Rodrigo. "Padrões de pulsos e computação em redes neurais com dinâmica." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-05092012-165022/.

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O processamento de sinais feito pelos sistemas neurais biológicos é altamente eficiente e complexo, por isso desperta grande atenção de pesquisa. Basicamente, todo o processamento de sinais funciona com base em redes de neurônios que emitem e recebem pulsos. Portanto, de forma geral, os estímulos recebidos do sistema sensorial por uma rede neural biológica de algum modo são convertidos em trens de pulsos. Aqui, nesta tese, é apresentada uma nova arquitetura composta por duas camadas: a primeira recebe correntes de estímulos de entrada e os mapeia em trens de pulsos; a segunda recebe esses trens de pulsos e os clássica em conjuntos de estímulos. Na primeira camada, a conversão de correntes de estímulos em trens de pulso é feita através de uma rede de neurônios osciladores acoplados por pulso. Esses neurônios possuem uma frequência natural de disparo e quando são agrupados em redes podem se coordenar para apresentar uma dinâmica global a longo prazo. Por sua vez, a dinâmica global também é sensível às correntes de entrada. Na segunda camada, a classificação dos trens de pulsos em conjuntos de estímulos é implementada por um neurônio do tipo integra-e-dispara. O comportamento típico para esse neurônio é de disparar ao menos uma vez para todas as integrações de trens de pulsos de uma determinada classe; caso contrário, ele deve car em silêncio. O processo de aprendizado da segunda camada depende do conhecimento do intervalo de tempo de repetição de um trem de pulsos. Portanto, nesta tese, são apresentadas métricas para definir tal intervalo de tempo, dando, assim, autonomia para a arquitetura. É possível concluir com base nos ensaios realizados que a arquitetura desenvolvida possui uma grande capacidade para mapeamento de correntes de entradas em trens de pulsos sem a necessidade de alterações na estrutura da arquitetura; também que a adição da dimensão tempo pela primeira camada ajuda na classificação realizada pela segunda. Assim, um novo modelo para realizar processos de codificação e decodificação é apresentado, desenvolvido através de séries de ensaios computacionais e caracterizado por medidas de sua dinâmica.
The signal processing done by the neural systems is highly efficient and complex, so that it attracts a large attention for research. Basically, all the signal processing functions are based on networks of neurons that send and receive spikes. Therefore, in general, the stimuli received from the sensory system by a biological neural network somehow are converted into spike trains. Here, in this thesis, we present a new architecture composed of two layers: the first layer receives streams of input stimuli and maps them on spike trains; the second layer receives these spike trains and classifies them in a sets of stimuli. In the first layer, the conversion of currents of stimuli on spike trains is made by a pulse-coupled neural network. Neurons in this context are like oscillators and have a natural frequency to shoot; when they are grouped into networks, they can be coordinated to present a global long-term dynamics. In turn, this global dynamics is also sensible to the input currents. In the second layer, the classification of spike trains in sets of stimuli is implemented by an integrate-and-re neuron. The typical behavior for this neuron is to shoot at least once every time that it receives a known spike train; otherwise, it should be in silence. The learning process of the second layer depends on the knowledge of the time interval of repetition of a spike train. Therefore, in this thesis, metrics are presented to define this time interval, thus giving autonomy to the architecture. It can be concluded on the basis of the tests developed that the architecture has a large capacity for mapping input currents on spike trains without requiring changes in its structure; moreover, the addition of the time dimension done by the first layer helps in the classification performed by the second layer. Thus, a new model to perform the encoding and decoding processes is presented, developed through a series of computational experiments and characterized by measurements of its dynamics.
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Åberg, K. Magnus. "Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation." Doctoral thesis, Stockholm University, Department of Analytical Chemistry, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-283.

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This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods.

Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules.

Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments.

Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV.

Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed.

Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

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Hsiang, Hsi-Bao, and 向錫堡. "An improved image de-noise method based on pulse-coupled neural networks." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/45991084426879623188.

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碩士
輔仁大學
資訊工程學系碩士班
102
Image de-noise is the first step in image processing. Salt and pepper noises are common image noises. There are already many de-noise algorithms， such as the non local means (NL-means)， mean filtering， and median filtering. Although these filters can de-noise images， they may reduce image details at the same time， resulting image blur and distortion. To reduce image blur and distortion after de-noising， we use pulse-coupled neural network PCNN (Pulse Coupled Neural Network) to de-noise images. PCNN can effectively remove noises and preserve image details. PCNN generally uses a fixed pane size in image de-noise. It uses gray-scale values of pixels as input neurons to calculate whether pixels have noises. In this paper， dynamic sized panes are used instead of fixed sized panes. When there are no noises in a pane， our improved PCNN will automatically enlarge the size of the pane， to recalculate whether there are noises. Keywords:  Image De-noising， PCNN (Pulse Coupled Neural Network)， detect noise
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Timme, Marc. "Collective Dynamics in Networks of Pulse-Coupled Oscillators." Doctoral thesis, 2002. http://hdl.handle.net/11858/00-1735-0000-0006-B575-5.

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Kirst, Christoph. "Synchronization, Neuronal Excitability, and Information Flow in Networks of Neuronal Oscillators." Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-000D-F08D-2.

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Books on the topic "Pulse-Coupled Neural Networks"

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Ma, Yide. Applications of Pulse-Coupled Neural Networks. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. Applications of Pulse-Coupled Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-13745-7.

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1962-, Kinser Jason M., ed. Image processing using pulse-coupled neural networks. London: Springer, 1998.

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Lindblad, Thomas, and Jason M. Kinser. Image Processing using Pulse-Coupled Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36877-6.

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Lindblad, Thomas, and Jason M. Kinser. Image Processing using Pulse-Coupled Neural Networks. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3617-0.

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Lindblad, Thomas. Image Processing using Pulse-Coupled Neural Networks: Applications in Python. 3rd ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Brain dynamics: Synchronization and activity patterns in pulse-coupled neural nets with delays and noise. Berlin: Springer, 2002.

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Workshop, on Virtual Intelligence/Dynamic Neural Networks (9th 1998 Stockholm Sweden). Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Academic/industrial/NASA/defense technical interchange and tutorials : international conferences on virtual intelligence/dynamic neural networks--neural networks, fuzzy systems, evolutionary systems, and virtual reality/pulse coupled neural networks, 1998. Bellingham, Wash: SPIE, 1999.

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Thomas, Lindblad, Padgett Mary Lou, Kinser Jason M. 1962-, United States. National Aeronautics and Space Administration, Society of Photo-optical Instrumentation Engineers., and IEEE Industry Applications Society, eds. Proceedings, Ninth Workshop on Virtual Intelligence: Dynamic neural networks: academic/industrial/NASA/defense : technical interchange and tutorials : international conferences on virtual intelligence/dynamic neural networks: neural networks, fuzzy systems, evolutionary systems and virtual reality/pulse coupled neural networks, 1998. Bellingham, Wash., USA: SPIE, 1999.

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Image Processing Using Pulse-Coupled Neural Networks. 2nd ed. Springer, 2005.

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Book chapters on the topic "Pulse-Coupled Neural Networks"

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Pulse-Coupled Neural Networks." In Applications of Pulse-Coupled Neural Networks, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_1.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Filtering." In Applications of Pulse-Coupled Neural Networks, 11–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_2.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Segmentation." In Applications of Pulse-Coupled Neural Networks, 27–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_3.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Coding." In Applications of Pulse-Coupled Neural Networks, 43–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_4.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Enhancement." In Applications of Pulse-Coupled Neural Networks, 61–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_5.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Image Fusion." In Applications of Pulse-Coupled Neural Networks, 83–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_6.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Feature Extraction." In Applications of Pulse-Coupled Neural Networks, 111–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_7.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "Combinatorial Optimization." In Applications of Pulse-Coupled Neural Networks, 147–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_8.

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Ma, Yide, Kun Zhan, and Zhaobin Wang. "FPGA Implementation of PCNN Algorithm." In Applications of Pulse-Coupled Neural Networks, 167–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13745-7_9.

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Li, Min, Wei Cai, and Zheng Tan. "Pulse Coupled Neural Network Based Image Fusion." In Advances in Neural Networks – ISNN 2005, 741–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427445_119.

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Conference papers on the topic "Pulse-Coupled Neural Networks"

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Johnson, John L. "Pulse-coupled neural networks." In Critical Review Collection. SPIE, 1994. http://dx.doi.org/10.1117/12.171194.

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Timoszczuk, Antonio Pedro, and Euvaldo F. Cabral. "Speaker Recognition Using Pulse Coupled Neural Networks." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371259.

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Johnson, John L. "Pulse-coupled neural networks can benefit ATR." In Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, edited by Thomas Lindblad, Mary Lou Padgett, and Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343032.

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Ranganath, Heggere S., Michele R. Banish, John R. Karpinsky, Rodney L. Clark, Glynn A. Germany, and Philip G. Richards. "Three applications of pulse-coupled neural networks." In Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, edited by Thomas Lindblad, Mary Lou Padgett, and Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343055.

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Yourui, Huang, and Wang Shuang. "Image Segmentation Using Pulse Coupled Neural Networks." In 2008 International Conference on MultiMedia and Information Technology (MMIT). IEEE, 2008. http://dx.doi.org/10.1109/mmit.2008.121.

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Inguva, Ramarao, John L. Johnson, and Marius P. Schamschula. "Multifeature fusion using pulse-coupled neural networks." In AeroSense '99, edited by Belur V. Dasarathy. SPIE, 1999. http://dx.doi.org/10.1117/12.341357.

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Ranganath, Heggere S., and Govindaraj Kuntimad. "Iterative segmentation using pulse-coupled neural networks." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers and Dennis W. Ruck. SPIE, 1996. http://dx.doi.org/10.1117/12.235943.

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Malkani, Mohan, Mohammad Bodruzzaman, John L. Johnson, and Joel Davis. "Center for Neural Engineering: applications of pulse-coupled neural networks." In Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, edited by Thomas Lindblad, Mary Lou Padgett, and Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343042.

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Hisamatsu, Kozo, and Toshimichi Saito. "Delay-induced order in pulse-coupled bifurcating neurons." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596846.

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Szekely, Geza, and Thomas Lindblad. "Parameter adaptation in a simplified pulse-coupled neural network." In Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, edited by Thomas Lindblad, Mary Lou Padgett, and Jason M. Kinser. SPIE, 1999. http://dx.doi.org/10.1117/12.343046.

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