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Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation [article]

Iulia M. Comsa, Krzysztof Potempa, Luca Versari, Thomas Fischbacher, Andrea Gesmundo, Jyrki Alakuijala
<span title="2020-11-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The network operates using a biologically-plausible alpha synaptic transfer function.  ...  However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks.  ...  Discussion In this paper, we proposed a spiking neural network with biologically-plausible alpha synaptic function that encodes information in the relative timing of individual spikes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1907.13223v3">arXiv:1907.13223v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y7vbsfjhsvgpbergydf6bxms5e">fatcat:y7vbsfjhsvgpbergydf6bxms5e</a> </span>
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Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation

Iulia-Maria Comsa, Krzysztof Potempa, Luca Versari, Thomas Fischbacher, Andrea Gesmundo, Jyrki Alakuijala
<span title="2021-04-26">2021</span> <i title="Institute of Electrical and Electronics Engineers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/j6amxna35bbs5p42wy5crllu2i" style="color: black;">IEEE Transactions on Neural Networks and Learning Systems</a> </i> &nbsp;
However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks.  ...  By studying temporal coding in spiking networks, we aim to create building blocks toward energy-efficient, state-based biologically inspired neural architectures.  ...  ACKNOWLEDGMENT The authors would like to thank Robert Obryk for the valuable contributions in the early phase of this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2021.3071976">doi:10.1109/tnnls.2021.3071976</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33900924">pmid:33900924</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ce6bbvtc7ze3xovswn4wioq6ei">fatcat:ce6bbvtc7ze3xovswn4wioq6ei</a> </span>
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Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [article]

Malu Zhang, Jiadong Wang, Burin Amornpaisannon, Zhixuan Zhang, VPK Miriyala, Ammar Belatreche, Hong Qu, Jibin Wu, Yansong Chua, Trevor E. Carlson, Haizhou Li
<span title="2020-11-04">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner.  ...  Spiking Neural Networks (SNNs) use spatio-temporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic  ...  (c) The alpha-PSP neuron with weak synaptic weights is susceptible to be a dead neuron . Figure 1 : 1 (a) Alpha shape PSP function.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.11837v2">arXiv:2003.11837v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lke7bezyhzbmpewpaactmbfyhm">fatcat:lke7bezyhzbmpewpaactmbfyhm</a> </span>
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Zero-Lag Long Range Synchronization of Neurons Is Enhanced by Dynamical Relaying [chapter]

Raul Vicente, Gordon Pipa, Ingo Fischer, Claudio R. Mirasso
<span title="">2007</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Here we propose a simple network module that naturally accounts for zero-lag neural synchronization for a wide range of temporal delays.  ...  How can two distant neural assemblies synchronize their firings at zero-lag even in the presence of non-negligible delays in the transfer of information between them?  ...  The colors in panels c) and d) code for alpha-functions of the same color shown in b). gmax = 0.2.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-74690-4_92">doi:10.1007/978-3-540-74690-4_92</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mh4hgwl2ofdqxlzx3ks5r7dq64">fatcat:mh4hgwl2ofdqxlzx3ks5r7dq64</a> </span>
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Fluctuations in Oscillation Frequency Control Spike Timing and Coordinate Neural Networks

M. X. Cohen
<span title="2014-07-02">2014</span> <i title="Society for Neuroscience"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s7bticdwizdmhll4taefg57jde" style="color: black;">Journal of Neuroscience</a> </i> &nbsp;
Frequency sliding is demonstrated in simulated neural networks and in human EEG data during a visual task.  ...  Frequency sliding appears to be a general principle that regulates brain function on multiple spatial and temporal scales, from modulating spike timing in individual neurons to coordinating large-scale  ...  In the living brain, neurons and neural networks are bombarded with synaptic input that varies over time, and thus their firing rates also vary over time.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1523/jneurosci.0261-14.2014">doi:10.1523/jneurosci.0261-14.2014</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24990919">pmid:24990919</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6608248/">pmcid:PMC6608248</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pvuh5cklzned7owa2p67h5jtm4">fatcat:pvuh5cklzned7owa2p67h5jtm4</a> </span>
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Synthesis of neural networks for spatio-temporal spike pattern recognition and processing [article]

J. Tapson, G. Cohen, S. Afshar, K. Stiefel, Y. Buskila, R. Wang, T.J. Hamilton, A. van Schaik
<span title="2013-04-26">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.  ...  We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons.  ...  Acknowledgements The authors thank James Wright for help with data preparation, and the organizers of the CapoCaccia and Telluride Cognitive Neuromorphic Engineering Workshops, where these ideas were formulated  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1304.7118v1">arXiv:1304.7118v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2swownnv5fdkfojm27c6jpc52m">fatcat:2swownnv5fdkfojm27c6jpc52m</a> </span>
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Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

Jonathan C. Tapson, Greg K. Cohen, Saeed Afshar, Klaus M. Stiefel, Yossi Buskila, Runchun Mark Wang, Tara J. Hamilton, André van Schaik
<span title="">2013</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.  ...  Keywords: pseudoinverse solution, spatio-temporal spike pattern recognition, spiking network synthesis, kernel method, spike-time encoded information www.frontiersin.org  ...  ACKNOWLEDGMENTS The authors thank James Wright for help with data preparation, and the organizers of the CapoCaccia and Telluride Cognitive Neuromorphic Engineering Workshops, where these ideas were formulated  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2013.00153">doi:10.3389/fnins.2013.00153</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24009550">pmid:24009550</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3757528/">pmcid:PMC3757528</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/soehqxzx7jambibxz4ki4pixh4">fatcat:soehqxzx7jambibxz4ki4pixh4</a> </span>
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Incremental learning algorithm for spatio-temporal spike pattern classification

Ammar Mohemmed, Nikola Kasabov
<span title="">2012</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qm5nunzmyva4tfjekdcm34uvhq" style="color: black;">The 2012 International Joint Conference on Neural Networks (IJCNN)</a> </i> &nbsp;
The training is performed in incremental fashion, i.e. the synaptic weights are adjusted after each presentation of a training pattern.  ...  The training is performed in incremental fashion, i.e. the synaptic weights are adjusted after each presentation of a training pattern.  ...  To apply 2 on a spiking neuron where the signals are spikes, the input, output and target spike patterns are convolved with the alpha kernel function, other functions also can be used.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ijcnn.2012.6252533">doi:10.1109/ijcnn.2012.6252533</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcnn/MohemmedK12.html">dblp:conf/ijcnn/MohemmedK12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jlf5fso7iza6znflsj5r6vbg7i">fatcat:jlf5fso7iza6znflsj5r6vbg7i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200215111339/https://www.zora.uzh.ch/id/eprint/75342/1/Mohemmed_Kasabov_Imcremental_Learning_algorithm.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/dc/37dcb23abc6524cb55233dca731da2ca276194ab.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ijcnn.2012.6252533"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning [article]

Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timothée Masquelier
<span title="2021-12-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and a form of temporal coding  ...  Similar strategies have been used to train (non-spiking) binarized neural networks.  ...  “Temporal coding in spiking neural networks 121, pp. 387–395, 2020. with alpha synaptic function,” arXiv, p. 1907.13223, 2019. [38] M.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.04039v2">arXiv:2007.04039v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jh5krgxh4ngrbhiieb4ghrxj2u">fatcat:jh5krgxh4ngrbhiieb4ghrxj2u</a> </span>
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A new learning method for inference accuracy, core occupation, and performance co-optimization on TrueNorth chip

Wei Wen, Chunpeng Wu, Yandan Wang, Kent Nixon, Qing Wu, Mark Barnell, Hai Li, Yiran Chen
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5vn6yyeefbbxtoo3uhwxwjwtme" style="color: black;">Proceedings of the 53rd Annual Design Automation Conference on - DAC &#39;16</a> </i> &nbsp;
However, in TrueNorth chip, low quantization resolution of the synaptic weights and spikes significantly limits the inference (e.g., classification) accuracy of the deployed neural network model.  ...  ., averaging the results over multiple copies instantiated in spatial and temporal domains, rapidly exhausts the hardware resources and slows down the computation.  ...  Besides the stochastic scheme, TrueNorth also supports many deterministic neural coding schemes (i.e., rate code, population code, time-to-spike code and rank code [9] ) in temporal domain to represent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2897937.2897968">doi:10.1145/2897937.2897968</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/dac/WenWWNWBLC16.html">dblp:conf/dac/WenWWNWBLC16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l4tf65pwhjahnnti2bgqccywc4">fatcat:l4tf65pwhjahnnti2bgqccywc4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200822195351/https://arxiv.org/vc/arxiv/papers/1604/1604.00697v2.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e5/9f/e59f8da0bd609a3b96b6c3d12d8cf06396b7bd9b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2897937.2897968"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Time and space in neuronal networks: The effects of spatial organization on network behavior

Stephen P. Womble, Netta Cohen
<span title="2010-08-27">2010</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y3fh56bfunh5fgneywwba6d4ke" style="color: black;">Complexity</a> </i> &nbsp;
We use simulations of spiking neural networks, operating close to a critical bifurcation between stationary behavior and population-wide oscillatory behavior, whereas in a nonspatial network, shorter time  ...  Biological neural networks tend to exhibit a variety of spatial structures.  ...  The synaptic time constant τ of the alpha-function was used as the bifurcation parameter.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/cplx.20343">doi:10.1002/cplx.20343</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gxmh3mu3o5ejbhkig7ly5aw5za">fatcat:gxmh3mu3o5ejbhkig7ly5aw5za</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808232441/http://www.comp.leeds.ac.uk/netta/CV/papers/WombleCohen10.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4a/a4/4aa4ae3c28e714b2b112e641b8acf92bec4eac3a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/cplx.20343"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> wiley.com </button> </a>

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks [article]

Wenrui Zhang, Peng Li
<span title="2019-11-03">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The proposed ST-RSBP directly computes the gradient of a rated-coded loss function defined at the output layer of the network w.r.t tunable parameters.  ...  As an important class of SNNs, recurrent spiking neural networks (RSNNs) possess great computational power. However, the practical application of RSNNs is severely limited by challenges in training.  ...  Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF, SRC, UC Santa Barbara, and their contractors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.06378v3">arXiv:1908.06378v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jngolviminhctfel6l35tvoksy">fatcat:jngolviminhctfel6l35tvoksy</a> </span>
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Spike time displacement based error backpropagation in convolutional spiking neural networks [article]

Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh, Timothée Masquelier
<span title="2021-08-31">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We recently proposed the STiDi-BP algorithm, which avoids backward recursive gradient computation, for training multi-layer spiking neural networks (SNNs) with single-spike-based temporal coding.  ...  The algorithm employs a linear approximation to compute the derivative of the spike latency with respect to the membrane potential and it uses spiking neurons with piecewise linear postsynaptic potential  ...  [6] employed a similar approach for SRM neuron models with alpha synaptic function. Zhou et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.13621v1">arXiv:2108.13621v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zdp6b2ok2zfqzaw2idrkzaqwm4">fatcat:zdp6b2ok2zfqzaw2idrkzaqwm4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210902112523/https://arxiv.org/pdf/2108.13621v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/04/18/041891e4cda67ac34923384dd5fb2a8fb73812fb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.13621v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

S4NN: temporal backpropagation for spiking neural networks with one spike per neuron [article]

Saeed Reza Kheradpisheh, Timothée Masquelier
<span title="2020-04-13">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding.  ...  With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information.  ...  Also, it can be used in convolutional spiking neural networks (CSNNs). Current CSNNs are mainly converted from traditional CNNs with rate [68, 69, 70, 71] and temporal coding [72] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.09495v3">arXiv:1910.09495v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qrr7vwrfqba6pjc667yefijedq">fatcat:qrr7vwrfqba6pjc667yefijedq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200415041736/https://arxiv.org/pdf/1910.09495v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.09495v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Dynamic Control of Synchronous Activity in Networks of Spiking Neurons

Axel Hutt, Andreas Mierau, Jérémie Lefebvre, Maurice J. Chacron
<span title="2016-09-26">2016</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Oscillatory brain activity is believed to play a central role in neural coding.  ...  We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics.  ...  What are implications of this dynamic behavior with respect to neural coding?  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0161488">doi:10.1371/journal.pone.0161488</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27669018">pmid:27669018</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5036852/">pmcid:PMC5036852</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lrf65rbrx5cg5ccrp75cohr32q">fatcat:lrf65rbrx5cg5ccrp75cohr32q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171012005636/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0161488&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/a6/eea6ad7e0cde629a3f4e9247e0cdd3e375fcf831.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0161488"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036852" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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