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Distinct Feedforward and Feedback Effects of Microstimulation in Visual Cortex Reveal Neural Mechanisms of Texture Segregation. 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Early recurrent feedback facilitates visual object recognition under challenging conditions. 2014.","DOI":"10.3389\/fpsyg.2014.00674"},{"issue":"25","key":"ref21","doi-asserted-by":"crossref","first-page":"8570","DOI":"10.1523\/JNEUROSCI.1375-14.2014","article-title":"The role of visual area V4 in the discrimination of partially occluded shapes","volume":"34","author":"Y Kosai","year":"2014","journal-title":"Journal of Neuroscience"},{"key":"ref22","unstructured":"Choi H, Pasupathy A, Shea-Brown E. Predictive coding in area V4: dynamic shape discrimination under partial occlusion. arXiv preprint arXiv:161205321. 2016."},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Spoerer C, McClure P, Kriegeskorte N. 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Perception; 2013."},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyg.2014.01193","article-title":"Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception","volume":"5","author":"AM Clarke","year":"2014","journal-title":"Frontiers in psychology"},{"issue":"25","key":"ref28","doi-asserted-by":"crossref","first-page":"3262","DOI":"10.1016\/j.visres.2005.06.007","article-title":"The recognition of partially visible natural objects in the presence and absence of their occluders","volume":"45","author":"JS Johnson","year":"2005","journal-title":"Vision research"},{"issue":"3","key":"ref29","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.neuron.2014.06.017","article-title":"Spatiotemporal dynamics underlying object completion in human ventral visual cortex","volume":"83","author":"H Tang","year":"2014","journal-title":"Neuron"},{"key":"ref30","unstructured":"Eberhardt S, Cader JG, Serre T, editors. How deep is the feature analysis underlying rapid visual categorization? Advances in neural information processing systems; 2016."},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. bioRxiv. 2018:240614.","DOI":"10.1101\/240614"},{"issue":"4","key":"ref32","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1111\/j.1467-9280.2006.01711.x","article-title":"Temporally unfolding neural representation of pictorial occlusion","volume":"17","author":"R Rauschenberger","year":"2006","journal-title":"Psychological Science"},{"issue":"5","key":"ref33","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1093\/cercor\/bhl031","article-title":"The sightless view: neural correlates of occluded objects","volume":"17","author":"OJ Hulme","year":"2007","journal-title":"Cerebral Cortex"},{"issue":"4","key":"ref34","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1167\/8.4.16","article-title":"Preferential responses to occluded objects in the human visual cortex","volume":"8","author":"J Hegd\u00e9","year":"2008","journal-title":"Journal of vision"},{"issue":"43","key":"ref35","doi-asserted-by":"crossref","first-page":"16992","DOI":"10.1523\/JNEUROSCI.1455-12.2013","article-title":"Topographic representation of an occluded object and the effects of spatiotemporal context in human early visual areas","volume":"33","author":"H Ban","year":"2013","journal-title":"Journal of Neuroscience"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1016\/j.neuroimage.2016.09.024","article-title":"Decoding information about dynamically occluded objects in visual cortex","volume":"146","author":"G Erlikhman","year":"2017","journal-title":"NeuroImage"},{"issue":"5502","key":"ref37","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1126\/science.1058249","article-title":"Seeking categories in the brain","volume":"291","author":"SJ Thorpe","year":"2001","journal-title":"Science"},{"issue":"2","key":"ref38","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.neuron.2009.02.025","article-title":"Timing, timing, timing: fast decoding of object information from intracranial field potentials in human visual cortex","volume":"62","author":"H Liu","year":"2009","journal-title":"Neuron"},{"issue":"12","key":"ref39","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1167\/15.12.1089","article-title":"The effects of recurrent dynamics on ventral-stream representational geometry","volume":"15","author":"S-M Khaligh-Razavi","year":"2015","journal-title":"Journal of vision"},{"issue":"12","key":"ref40","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1167\/15.12.1087","article-title":"Decoding the emerging representation of degraded visual objects in the human brain","volume":"15","author":"T Grootswagers","year":"2015","journal-title":"Journal of vision"},{"issue":"8","key":"ref41","doi-asserted-by":"crossref","first-page":"e0135697","DOI":"10.1371\/journal.pone.0135697","article-title":"A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification","volume":"10","author":"B Kaneshiro","year":"2015","journal-title":"PloS one"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"e36329","DOI":"10.7554\/eLife.36329","article-title":"Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway","volume":"7","author":"Y Mohsenzadeh","year":"2018","journal-title":"Elife"},{"issue":"10","key":"ref43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/13.10.1","article-title":"Representational dynamics of object vision: the first 1000 ms","volume":"13","author":"T Carlson","year":"2013","journal-title":"Journal of vision"},{"issue":"3","key":"ref44","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/nn.3635","article-title":"Resolving human object recognition in space and time","volume":"17","author":"RM Cichy","year":"2014","journal-title":"Nature neuroscience"},{"issue":"1","key":"ref45","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1152\/jn.00394.2013","article-title":"The dynamics of invariant object recognition in the human visual system","volume":"111","author":"L Isik","year":"2014","journal-title":"Journal of neurophysiology"},{"key":"ref46","doi-asserted-by":"crossref","unstructured":"Grootswagers T, Wardle SG, Carlson TA. Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time series neuroimaging data. Journal of cognitive neuroscience. 2017.","DOI":"10.1162\/jocn_a_01068"},{"key":"ref47","doi-asserted-by":"crossref","unstructured":"Contini EW, Wardle SG, Carlson TA. Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions. Neuropsychologia. 2017.","DOI":"10.1016\/j.neuropsychologia.2017.02.013"},{"key":"ref48","article-title":"Visual masking: Time slices through conscious and unconscious vision","author":"B Breitmeyer","year":"2006"},{"issue":"2","key":"ref49","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neuron.2009.04.012","article-title":"The speed of categorization in the human visual system","volume":"62","author":"SJ Thorpe","year":"2009","journal-title":"Neuron"},{"issue":"8","key":"ref50","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.1093\/cercor\/bhw135","article-title":"Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition","volume":"26","author":"RM Cichy","year":"2016","journal-title":"Cerebral Cortex"},{"issue":"4","key":"ref51","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.tics.2014.01.002","article-title":"Characterizing the dynamics of mental representations: the temporal generalization method","volume":"18","author":"J King","year":"2014","journal-title":"Trends in cognitive sciences"},{"issue":"5","key":"ref52","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.neuron.2016.10.051","article-title":"Brain mechanisms underlying the brief maintenance of seen and unseen sensory information","volume":"92","author":"J-R King","year":"2016","journal-title":"Neuron"},{"issue":"7","key":"ref53","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1162\/089892902320474490","article-title":"Masking interrupts figure-ground signals in V1","volume":"14","author":"VA Lamme","year":"2002","journal-title":"Journal of cognitive neuroscience"},{"issue":"11","key":"ref54","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1016\/j.visres.2005.01.004","article-title":"The time course of visual processing: Backward masking and natural scene categorisation","volume":"45","author":"N Bacon-Mac\u00e9","year":"2005","journal-title":"Vision research"},{"issue":"9","key":"ref55","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1162\/jocn.2007.19.9.1488","article-title":"Masking disrupts reentrant processing in human visual cortex","volume":"19","author":"JJ Fahrenfort","year":"2007","journal-title":"Journal of cognitive neuroscience"},{"issue":"15","key":"ref56","doi-asserted-by":"crossref","first-page":"6424","DOI":"10.1073\/pnas.0700622104","article-title":"A feedforward architecture accounts for rapid categorization","volume":"104","author":"T Serre","year":"2007","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"1\u20132","key":"ref57","doi-asserted-by":"crossref","first-page":"125","DOI":"10.2478\/v10053-008-0020-5","article-title":"The role of feedback in visual masking and visual processing","volume":"3","author":"SL Macknik","year":"2007","journal-title":"Advances in cognitive psychology"},{"key":"ref58","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J, editors. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref59","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J, editors. Identity mappings in deep residual networks. European Conference on Computer Vision; 2016: Springer.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref60","unstructured":"Liao Q, Poggio T. Bridging the gaps between residual learning, recurrent neural networks and visual cortex. arXiv preprint arXiv:160403640. 2016."},{"key":"ref61","unstructured":"Veit A, Wilber MJ, Belongie S, editors. Residual networks behave like ensembles of relatively shallow networks. Advances in Neural Information Processing Systems; 2016."},{"key":"ref62","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, editors. Imagenet: A large-scale hierarchical image database. Computer Vision and Pattern Recognition, 2009 CVPR 2009 IEEE Conference on; 2009: IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref63","unstructured":"Liang M, Hu X, editors. Recurrent convolutional neural network for object recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015."},{"key":"ref64","doi-asserted-by":"crossref","first-page":"27755","DOI":"10.1038\/srep27755","article-title":"Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence","volume":"6","author":"RM Cichy","year":"2016","journal-title":"Scientific reports"},{"issue":"8","key":"ref65","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1162\/jocn.2010.21544","article-title":"The evolution of meaning: spatio-temporal dynamics of visual object recognition","volume":"23","author":"A Clarke","year":"2011","journal-title":"Journal of cognitive neuroscience"},{"issue":"4","key":"ref66","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1080\/23273798.2014.970652","article-title":"Dynamic information processing states revealed through neurocognitive models of object semantics. Language","volume":"30","author":"A Clarke","year":"2015","journal-title":"cognition and neuroscience"},{"issue":"6754","key":"ref67","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1038\/44372","article-title":"Top-down signal from prefrontal cortex in executive control of memory retrieval","volume":"401","author":"H Tomita","year":"1999","journal-title":"Nature"},{"issue":"52","key":"ref68","doi-asserted-by":"crossref","first-page":"20961","DOI":"10.1073\/pnas.0706274105","article-title":"Evoked brain responses are generated by feedback loops","volume":"104","author":"MI Garrido","year":"2007","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.neuroimage.2016.01.006","article-title":"Representational dynamics of object recognition: Feedforward and feedback information flows","volume":"128","author":"E Goddard","year":"2016","journal-title":"NeuroImage"},{"key":"ref70","doi-asserted-by":"crossref","unstructured":"Devereux BJ, Clarke AD, Tyler LK. Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway. Scientific Reports. 2018.","DOI":"10.1101\/302406"},{"issue":"14","key":"ref71","doi-asserted-by":"crossref","first-page":"4766","DOI":"10.1523\/JNEUROSCI.2828-13.2014","article-title":"Object-specific semantic coding in human perirhinal cortex","volume":"34","author":"A Clarke","year":"2014","journal-title":"Journal of Neuroscience"},{"issue":"27","key":"ref72","doi-asserted-by":"crossref","first-page":"10005","DOI":"10.1523\/JNEUROSCI.5023-14.2015","article-title":"Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream","volume":"35","author":"U G\u00fc\u00e7l\u00fc","year":"2015","journal-title":"Journal of Neuroscience"},{"issue":"4","key":"ref73","doi-asserted-by":"crossref","first-page":"e1004896","DOI":"10.1371\/journal.pcbi.1004896","article-title":"Deep neural networks as a computational model for human shape sensitivity","volume":"12","author":"J Kubilius","year":"2016","journal-title":"PLoS computational biology"},{"key":"ref74","doi-asserted-by":"crossref","first-page":"32672","DOI":"10.1038\/srep32672","article-title":"Deep networks can resemble human feed-forward vision in invariant object recognition","volume":"6","author":"SR Kheradpisheh","year":"2016","journal-title":"Scientific reports"},{"key":"ref75","first-page":"10","article-title":"Humans and deep networks largely agree on which kinds of variation make object recognition harder","author":"SR Kheradpisheh","year":"2016","journal-title":"Frontiers in computational neuroscience"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.jmp.2016.10.007","article-title":"Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models","volume":"76","author":"S-M Khaligh-Razavi","year":"2017","journal-title":"Journal of Mathematical Psychology"},{"issue":"7","key":"ref77","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1016\/j.visres.2009.02.005","article-title":"Time course of amodal completion in face perception","volume":"49","author":"J Chen","year":"2009","journal-title":"Vision research"},{"key":"ref78","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014."},{"key":"ref79","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al., editors. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref80","doi-asserted-by":"crossref","unstructured":"Taigman Y, Yang M, Ranzato MA, Wolf L, editors. Deepface: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2014.","DOI":"10.1109\/CVPR.2014.220"},{"issue":"5","key":"ref81","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/7496.003.0016","article-title":"Scaling learning algorithms towards AI","volume":"34","author":"Y Bengio","year":"2007","journal-title":"Large-scale kernel machines"},{"issue":"6","key":"ref82","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/0047-2484(92)90081-J","article-title":"Neocortex size as a constraint on group size in primates","volume":"22","author":"RI Dunbar","year":"1992","journal-title":"Journal of human evolution"},{"issue":"1","key":"ref83","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1010028405318","article-title":"Why is brain size so important: Design problems and solutions as neocortex gets biggeror smaller","volume":"1","author":"JH Kaas","year":"2000","journal-title":"Brain and Mind"},{"issue":"10","key":"ref84","doi-asserted-by":"crossref","first-page":"3576","DOI":"10.1073\/pnas.0500692102","article-title":"Reciprocal evolution of the cerebellum and neocortex in fossil humans","volume":"102","author":"AH Weaver","year":"2005","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"issue":"4","key":"ref85","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.jhevol.2009.04.009","article-title":"The expensive brain: a framework for explaining evolutionary changes in brain size","volume":"57","author":"K Isler","year":"2009","journal-title":"Journal of Human Evolution"},{"key":"ref86","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2015.00303","article-title":"Functional constraints in the evolution of brain circuits","volume":"9","author":"CA Bosman","year":"2015","journal-title":"Frontiers in neuroscience"},{"issue":"1","key":"ref87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1037\/0033-295X.83.1.1","article-title":"Implications of sustained and transient channels for theories of visual pattern masking, saccadic suppression, and information processing","volume":"83","author":"BG Breitmeyer","year":"1976","journal-title":"Psychological review"},{"issue":"1","key":"ref88","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1023\/A:1026553619983","article-title":"A parametric texture model based on joint statistics of complex wavelet coefficients","volume":"40","author":"J Portilla","year":"2000","journal-title":"International journal of computer vision"},{"issue":"7","key":"ref89","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1088\/0031-9155\/51\/7\/008","article-title":"Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements","volume":"51","author":"S Taulu","year":"2006","journal-title":"Physics in Medicine & Biology"},{"key":"ref90","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1155\/2011\/879716","article-title":"Brainstorm: a user-friendly application for MEG\/EEG analysis","volume":"2011","author":"F Tadel","year":"2011","journal-title":"Computational intelligence and neuroscience"},{"issue":"4","key":"ref91","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1163\/156856897X00366","article-title":"The VideoToolbox software for visual psychophysics: Transforming numbers into movies","volume":"10","author":"DG Pelli","year":"1997","journal-title":"Spatial vision"},{"issue":"4","key":"ref92","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1162\/jocn_a_01070","article-title":"Representational dynamics of facial viewpoint encoding","volume":"29","author":"TC Kietzmann","year":"2017","journal-title":"Journal of cognitive neuroscience"},{"issue":"3","key":"ref93","first-page":"27","article-title":"LIBSVM: a library for support vector machines","volume":"2","author":"C-C Chang","year":"2011","journal-title":"ACM transactions on intelligent systems and technology (TIST)"},{"key":"ref94","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3389\/neuro.01.035.2009","article-title":"Relating population-code representations between man, monkey, and computational models","volume":"3","author":"N Kriegeskorte","year":"2009","journal-title":"Frontiers in Neuroscience"},{"issue":"8","key":"ref95","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.tics.2013.06.007","article-title":"Representational geometry: integrating cognition, computation, and the brain","volume":"17","author":"N Kriegeskorte","year":"2013","journal-title":"Trends in cognitive sciences"},{"key":"ref96","doi-asserted-by":"crossref","unstructured":"Khaligh-Razavi S-M, Bainbridge WA, Pantazis D, Oliva A. From what we perceive to what we remember: Characterizing representational dynamics of visual memorability. bioRxiv. 2016:049700.","DOI":"10.1101\/049700"},{"key":"ref97","article-title":"Multiple regression in behavioral research: Explanation and prediction","author":"E Pedzahur","year":"1997"},{"key":"ref98","first-page":"977","article-title":"International encyclopedia of statistical science","author":"JD Gibbons","year":"2011"},{"key":"ref99","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","author":"Y Benjamini","year":"1995","journal-title":"Journal of the royal statistical society Series B (Methodological)"},{"issue":"4","key":"ref100","doi-asserted-by":"crossref","first-page":"e1003553","DOI":"10.1371\/journal.pcbi.1003553","article-title":"A toolbox for representational similarity analysis","volume":"10","author":"H Nili","year":"2014","journal-title":"PLoS Comput Biol"},{"key":"ref101","unstructured":"Krizhevsky A, Sutskever I, Hinton GE, editors. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems; 2012."},{"issue":"3","key":"ref102","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"O Russakovsky","year":"2015","journal-title":"International Journal of Computer Vision"},{"key":"ref103","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J, editors. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision; 2015.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref104","unstructured":"Glorot X, Bengio Y, editors. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics; 2010."},{"issue":"36","key":"ref105","doi-asserted-by":"crossref","first-page":"8865","DOI":"10.1523\/JNEUROSCI.1640-08.2008","article-title":"Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe","volume":"28","author":"F Mormann","year":"2008","journal-title":"Journal of Neuroscience"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1007001","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T00:00:00Z","timestamp":1559001600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pcbi.1007001","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T12:15:50Z","timestamp":1694866550000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1007001"}},"subtitle":[],"editor":[{"given":"Leyla","family":"Isik","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2019,5,15]]},"references-count":105,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5,15]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1007001","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/302034","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,15]]}}}