[{"date_updated":"2025-09-29T11:09:18Z","external_id":{"isi":["000330244500130"]},"department":[{"_id":"GaTk"}],"_id":"3263","file":[{"file_name":"IST-2016-432-v1+1_journal.pone.0085841.pdf","file_size":1568524,"file_id":"5011","creator":"system","access_level":"open_access","date_updated":"2020-07-14T12:46:06Z","checksum":"1d5816b343abe5eadc3eb419bcece971","date_created":"2018-12-12T10:13:28Z","content_type":"application/pdf","relation":"main_file"}],"article_number":"e85841","file_date_updated":"2020-07-14T12:46:06Z","oa_version":"Published Version","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"doi":"10.1371/journal.pone.0085841","type":"journal_article","publisher":"Public Library of Science","year":"2014","ddc":["570"],"issue":"1","status":"public","pubrep_id":"432","date_published":"2014-01-21T00:00:00Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2018-12-11T12:02:20Z","quality_controlled":"1","day":"21","has_accepted_license":"1","corr_author":"1","publication_status":"published","title":"Adaptation to changes in higher-order stimulus statistics in the salamander retina","publication":"PLoS One","abstract":[{"lang":"eng","text":"Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution."}],"intvolume":"         9","volume":9,"scopus_import":"1","month":"01","isi":1,"citation":{"ieee":"G. Tkačik, A. Ghosh, E. Schneidman, and R. Segev, “Adaptation to changes in higher-order stimulus statistics in the salamander retina,” <i>PLoS One</i>, vol. 9, no. 1. Public Library of Science, 2014.","mla":"Tkačik, Gašper, et al. “Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina.” <i>PLoS One</i>, vol. 9, no. 1, e85841, Public Library of Science, 2014, doi:<a href=\"https://doi.org/10.1371/journal.pone.0085841\">10.1371/journal.pone.0085841</a>.","short":"G. Tkačik, A. Ghosh, E. Schneidman, R. Segev, PLoS One 9 (2014).","ista":"Tkačik G, Ghosh A, Schneidman E, Segev R. 2014. Adaptation to changes in higher-order stimulus statistics in the salamander retina. PLoS One. 9(1), e85841.","chicago":"Tkačik, Gašper, Anandamohan Ghosh, Elad Schneidman, and Ronen Segev. “Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina.” <i>PLoS One</i>. Public Library of Science, 2014. <a href=\"https://doi.org/10.1371/journal.pone.0085841\">https://doi.org/10.1371/journal.pone.0085841</a>.","apa":"Tkačik, G., Ghosh, A., Schneidman, E., &#38; Segev, R. (2014). Adaptation to changes in higher-order stimulus statistics in the salamander retina. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0085841\">https://doi.org/10.1371/journal.pone.0085841</a>","ama":"Tkačik G, Ghosh A, Schneidman E, Segev R. Adaptation to changes in higher-order stimulus statistics in the salamander retina. <i>PLoS One</i>. 2014;9(1). doi:<a href=\"https://doi.org/10.1371/journal.pone.0085841\">10.1371/journal.pone.0085841</a>"},"article_processing_charge":"No","author":[{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"},{"full_name":"Ghosh, Anandamohan","first_name":"Anandamohan","last_name":"Ghosh"},{"full_name":"Schneidman, Elad","first_name":"Elad","last_name":"Schneidman"},{"full_name":"Segev, Ronen","last_name":"Segev","first_name":"Ronen"}],"language":[{"iso":"eng"}],"acknowledgement":"This work was supported by The Israel Science Foundation and The Human Frontiers Science Program.\r\nWe thank the referees for helping significantly improve this paper. We also thank Vijay Balasubramanian, Kristina Simmons, and Jason Prentice for stimulating discussions. GT wishes to thank the faculty and students of the “Methods in Computational Neuroscience” course at Marine Biological Laboratory, Woods Hole.\r\n","oa":1,"publist_id":"3385"},{"ddc":["570"],"year":"2014","publisher":"Academic Press","type":"journal_article","date_created":"2018-12-11T11:55:18Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2014-10-12T00:00:00Z","pubrep_id":"444","status":"public","department":[{"_id":"GaTk"}],"external_id":{"isi":["000347267200005"]},"related_material":{"link":[{"url":"https://doi.org/10.1016/j.jtbi.2015.03.013","relation":"erratum"}]},"date_updated":"2026-07-07T13:13:09Z","doi":"10.1016/j.jtbi.2014.09.041","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"oa_version":"Published Version","file_date_updated":"2020-07-14T12:45:25Z","file":[{"access_level":"open_access","date_updated":"2020-07-14T12:45:25Z","checksum":"a9dbae18d3233b3dab6944fd3f2cd49e","file_size":2679222,"file_name":"IST-2016-444-v1+1_1-s2.0-S0022519314005888-main.pdf","file_id":"5316","creator":"system","relation":"main_file","date_created":"2018-12-12T10:17:58Z","content_type":"application/pdf"}],"_id":"2028","das_tickbox":"1","month":"10","scopus_import":"1","volume":365,"publist_id":"5043","oa":1,"acknowledgement":"This work is supported by AFOSR grant FA 9550-11-1-0165, program grant RPG 24/2012 from the Human Frontiers of Science (DBF) and travel support from the European Commission Marie Curie International Reintegration Grant PIRG04-GA-2008-239429 (KB). DP was supported by NIHR01 GM104987 and the Wyss Institute of Biologically Inspired Engineering. ","language":[{"iso":"eng"}],"author":[{"orcid":"0000-0002-7214-0171","full_name":"Bodova, Katarina","last_name":"Bodova","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","first_name":"Katarina"},{"full_name":"Paydarfar, David","first_name":"David","last_name":"Paydarfar"},{"full_name":"Forger, Daniel","last_name":"Forger","first_name":"Daniel"}],"citation":{"ista":"Bodova K, Paydarfar D, Forger D. 2014. Characterizing spiking in noisy type II neurons. Journal of Theoretical Biology. 365, 40–54.","chicago":"Bodova, Katarina, David Paydarfar, and Daniel Forger. “Characterizing Spiking in Noisy Type II Neurons.” <i>Journal of Theoretical Biology</i>. Academic Press, 2014. <a href=\"https://doi.org/10.1016/j.jtbi.2014.09.041\">https://doi.org/10.1016/j.jtbi.2014.09.041</a>.","apa":"Bodova, K., Paydarfar, D., &#38; Forger, D. (2014). Characterizing spiking in noisy type II neurons. <i>Journal of Theoretical Biology</i>. Academic Press. <a href=\"https://doi.org/10.1016/j.jtbi.2014.09.041\">https://doi.org/10.1016/j.jtbi.2014.09.041</a>","ama":"Bodova K, Paydarfar D, Forger D. Characterizing spiking in noisy type II neurons. <i>Journal of Theoretical Biology</i>. 2014;365:40-54. doi:<a href=\"https://doi.org/10.1016/j.jtbi.2014.09.041\">10.1016/j.jtbi.2014.09.041</a>","short":"K. Bodova, D. Paydarfar, D. Forger, Journal of Theoretical Biology 365 (2014) 40–54.","mla":"Bodova, Katarina, et al. “Characterizing Spiking in Noisy Type II Neurons.” <i>Journal of Theoretical Biology</i>, vol. 365, Academic Press, 2014, pp. 40–54, doi:<a href=\"https://doi.org/10.1016/j.jtbi.2014.09.041\">10.1016/j.jtbi.2014.09.041</a>.","ieee":"K. Bodova, D. Paydarfar, and D. Forger, “Characterizing spiking in noisy type II neurons,” <i>Journal of Theoretical Biology</i>, vol. 365. Academic Press, pp. 40–54, 2014."},"article_processing_charge":"No","isi":1,"page":"40 - 54","day":"12","quality_controlled":"1","abstract":[{"text":"Understanding the dynamics of noisy neurons remains an important challenge in neuroscience. Here, we describe a simple probabilistic model that accurately describes the firing behavior in a large class (type II) of neurons. To demonstrate the usefulness of this model, we show how it accurately predicts the interspike interval (ISI) distributions, bursting patterns and mean firing rates found by: (1) simulations of the classic Hodgkin-Huxley model with channel noise, (2) experimental data from squid giant axon with a noisy input current and (3) experimental data on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple model has 6 parameters, however, in some cases, two of these parameters are coupled and only 5 parameters account for much of the known behavior. From these parameters, many properties of spiking can be found through simple calculation. Thus, we show how the complex effects of noise can be understood through a simple and general probabilistic model.","lang":"eng"}],"intvolume":"       365","publication":"Journal of Theoretical Biology","title":"Characterizing spiking in noisy type II neurons","corr_author":"1","publication_status":"published","has_accepted_license":"1"},{"oa":1,"publist_id":"4667","author":[{"full_name":"Simmons, Kristina","last_name":"Simmons","first_name":"Kristina"},{"first_name":"Jason","last_name":"Prentice","full_name":"Prentice, Jason"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"full_name":"Homann, Jan","last_name":"Homann","first_name":"Jan"},{"last_name":"Yee","first_name":"Heather","full_name":"Yee, Heather"},{"last_name":"Palmer","first_name":"Stephanie","full_name":"Palmer, Stephanie"},{"first_name":"Philip","last_name":"Nelson","full_name":"Nelson, Philip"},{"full_name":"Balasubramanian, Vijay","first_name":"Vijay","last_name":"Balasubramanian"}],"language":[{"iso":"eng"}],"article_processing_charge":"No","citation":{"mla":"Simmons, Kristina, et al. “Transformation of Stimulus Correlations by the Retina.” <i>PLoS Computational Biology</i>, vol. 9, no. 12, e1003344, Public Library of Science, 2013, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003344\">10.1371/journal.pcbi.1003344</a>.","ieee":"K. Simmons <i>et al.</i>, “Transformation of stimulus correlations by the retina,” <i>PLoS Computational Biology</i>, vol. 9, no. 12. Public Library of Science, 2013.","short":"K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson, V. Balasubramanian, PLoS Computational Biology 9 (2013).","apa":"Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., … Balasubramanian, V. (2013). Transformation of stimulus correlations by the retina. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1003344\">https://doi.org/10.1371/journal.pcbi.1003344</a>","ama":"Simmons K, Prentice J, Tkačik G, et al. Transformation of stimulus correlations by the retina. <i>PLoS Computational Biology</i>. 2013;9(12). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1003344\">10.1371/journal.pcbi.1003344</a>","ista":"Simmons K, Prentice J, Tkačik G, Homann J, Yee H, Palmer S, Nelson P, Balasubramanian V. 2013. Transformation of stimulus correlations by the retina. PLoS Computational Biology. 9(12), e1003344.","chicago":"Simmons, Kristina, Jason Prentice, Gašper Tkačik, Jan Homann, Heather Yee, Stephanie Palmer, Philip Nelson, and Vijay Balasubramanian. “Transformation of Stimulus Correlations by the Retina.” <i>PLoS Computational Biology</i>. Public Library of Science, 2013. <a href=\"https://doi.org/10.1371/journal.pcbi.1003344\">https://doi.org/10.1371/journal.pcbi.1003344</a>."},"isi":1,"volume":9,"month":"12","scopus_import":"1","publication":"PLoS Computational Biology","intvolume":"         9","abstract":[{"lang":"eng","text":"Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible."}],"has_accepted_license":"1","title":"Transformation of stimulus correlations by the retina","publication_status":"published","day":"05","quality_controlled":"1","date_created":"2018-12-11T11:56:43Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","status":"public","date_published":"2013-12-05T00:00:00Z","pubrep_id":"410","ddc":["570"],"issue":"12","year":"2013","type":"journal_article","publisher":"Public Library of Science","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"doi":"10.1371/journal.pcbi.1003344","file_date_updated":"2020-07-14T12:45:36Z","oa_version":"Published Version","_id":"2277","article_number":"e1003344","file":[{"relation":"main_file","content_type":"application/pdf","date_created":"2018-12-12T10:14:36Z","checksum":"46722afc4f7eabb0831165d9c1b171ad","date_updated":"2020-07-14T12:45:36Z","access_level":"open_access","creator":"system","file_id":"5089","file_size":3115568,"file_name":"IST-2016-410-v1+1_journal.pcbi.1003344.pdf"}],"department":[{"_id":"GaTk"}],"external_id":{"isi":["000329364800028"]},"date_updated":"2025-09-29T14:27:23Z","related_material":{"record":[{"status":"public","id":"9752","relation":"research_data"}]}},{"day":"01","quality_controlled":"1","date_updated":"2025-06-03T11:46:23Z","department":[{"_id":"GaTk"}],"abstract":[{"text":"Progress in understanding the global brain dynamics has remained slow to date in large part because of the highly multiscale nature of brain activity. Indeed, normal brain dynamics is characterized by complex interactions between multiple levels: from the microscopic scale of single neurons to the mesoscopic level of local groups of neurons, and finally to the macroscopic level of the whole brain. Among the most difficult tasks are those of identifying which scales are significant for a given particular function and describing how the scales affect each other. It is important to realize that the scales of time and space are linked together, or even intertwined, and that causal inference is far more ambiguous between than within levels. We approach this problem from the perspective of our recent work on simultaneous recording from micro- and macroelectrodes in the human brain. We propose a physiological description of these multilevel interactions, based on phase–amplitude coupling of neuronal oscillations that operate at multiple frequencies and on different spatial scales. Specifically, the amplitude of the oscillations on a particular spatial scale is modulated by phasic variations in neuronal excitability induced by lower frequency oscillations that emerge on a larger spatial scale. Following this general principle, it is possible to scale up or scale down the multiscale brain dynamics. It is expected that large-scale network oscillations in the low-frequency range, mediating downward effects, may play an important role in attention and consciousness.","lang":"eng"}],"oa_version":"None","publication":"Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain","_id":"2413","title":"Neuronal oscillations scale up and scale down the brain dynamics ","publication_status":"published","alternative_title":["Reviews of Nonlinear Dynamics and Complexity"],"doi":"10.1002/9783527671632.ch08","publisher":"Wiley","type":"book_chapter","publication_identifier":{"eisbn":["9783527671632"],"isbn":["9783527411986 "]},"month":"08","scopus_import":"1","year":"2013","language":[{"iso":"eng"}],"editor":[{"full_name":"Meyer, Misha","first_name":"Misha","last_name":"Meyer"},{"first_name":"Z.","last_name":"Pesenson","full_name":"Pesenson, Z."}],"author":[{"last_name":"Valderrama","first_name":"Mario","full_name":"Valderrama, Mario"},{"last_name":"Botella Soler","id":"421234E8-F248-11E8-B48F-1D18A9856A87","first_name":"Vicente","orcid":"0000-0002-8790-1914","full_name":"Botella Soler, Vicente"},{"last_name":"Le Van Quyen","first_name":"Michel","full_name":"Le Van Quyen, Michel"}],"citation":{"short":"M. Valderrama, V. Botella Soler, M. Le Van Quyen, in:, M. Meyer, Z. Pesenson (Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, Wiley, 2013.","ista":"Valderrama M, Botella Soler V, Le Van Quyen M. 2013.Neuronal oscillations scale up and scale down the brain dynamics . In: Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Reviews of Nonlinear Dynamics and Complexity, .","chicago":"Valderrama, Mario, Vicente Botella Soler, and Michel Le Van Quyen. “Neuronal Oscillations Scale up and Scale down the Brain Dynamics .” In <i>Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain</i>, edited by Misha Meyer and Z. Pesenson. Wiley, 2013. <a href=\"https://doi.org/10.1002/9783527671632.ch08\">https://doi.org/10.1002/9783527671632.ch08</a>.","apa":"Valderrama, M., Botella Soler, V., &#38; Le Van Quyen, M. (2013). Neuronal oscillations scale up and scale down the brain dynamics . In M. Meyer &#38; Z. Pesenson (Eds.), <i>Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain</i>. Wiley. <a href=\"https://doi.org/10.1002/9783527671632.ch08\">https://doi.org/10.1002/9783527671632.ch08</a>","ama":"Valderrama M, Botella Soler V, Le Van Quyen M. Neuronal oscillations scale up and scale down the brain dynamics . In: Meyer M, Pesenson Z, eds. <i>Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain</i>. Wiley; 2013. doi:<a href=\"https://doi.org/10.1002/9783527671632.ch08\">10.1002/9783527671632.ch08</a>","ieee":"M. Valderrama, V. Botella Soler, and M. Le Van Quyen, “Neuronal oscillations scale up and scale down the brain dynamics ,” in <i>Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain</i>, M. Meyer and Z. Pesenson, Eds. Wiley, 2013.","mla":"Valderrama, Mario, et al. “Neuronal Oscillations Scale up and Scale down the Brain Dynamics .” <i>Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain</i>, edited by Misha Meyer and Z. Pesenson, Wiley, 2013, doi:<a href=\"https://doi.org/10.1002/9783527671632.ch08\">10.1002/9783527671632.ch08</a>."},"article_processing_charge":"No","date_published":"2013-08-01T00:00:00Z","status":"public","publist_id":"4513","date_created":"2018-12-11T11:57:31Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"article_number":"P03011","_id":"2850","oa_version":"Preprint","doi":"10.1088/1742-5468/2013/03/P03011","date_updated":"2025-09-29T13:42:18Z","external_id":{"isi":["000316056900011"],"arxiv":["1207.6319"]},"department":[{"_id":"GaTk"}],"status":"public","date_published":"2013-03-12T00:00:00Z","date_created":"2018-12-11T11:59:55Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","type":"journal_article","article_type":"original","publisher":"IOP Publishing","year":"2013","issue":"3","publication_status":"published","title":"The simplest maximum entropy model for collective behavior in a neural network","publication":"Journal of Statistical Mechanics Theory and Experiment","abstract":[{"text":"Recent work emphasizes that the maximum entropy principle provides a bridge between statistical mechanics models for collective behavior in neural networks and experiments on networks of real neurons. Most of this work has focused on capturing the measured correlations among pairs of neurons. Here we suggest an alternative, constructing models that are consistent with the distribution of global network activity, i.e. the probability that K out of N cells in the network generate action potentials in the same small time bin. The inverse problem that we need to solve in constructing the model is analytically tractable, and provides a natural 'thermodynamics' for the network in the limit of large N. We analyze the responses of neurons in a small patch of the retina to naturalistic stimuli, and find that the implied thermodynamics is very close to an unusual critical point, in which the entropy (in proper units) is exactly equal to the energy. © 2013 IOP Publishing Ltd and SISSA Medialab srl.\r\n","lang":"eng"}],"intvolume":"      2013","quality_controlled":"1","day":"12","main_file_link":[{"url":"http://arxiv.org/abs/1207.6319","open_access":"1"}],"citation":{"apa":"Tkačik, G., Marre, O., Mora, T., Amodei, D., Berry, M., &#38; Bialek, W. (2013). The simplest maximum entropy model for collective behavior in a neural network. <i>Journal of Statistical Mechanics Theory and Experiment</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03011\">https://doi.org/10.1088/1742-5468/2013/03/P03011</a>","ama":"Tkačik G, Marre O, Mora T, Amodei D, Berry M, Bialek W. The simplest maximum entropy model for collective behavior in a neural network. <i>Journal of Statistical Mechanics Theory and Experiment</i>. 2013;2013(3). doi:<a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03011\">10.1088/1742-5468/2013/03/P03011</a>","chicago":"Tkačik, Gašper, Olivier Marre, Thierry Mora, Dario Amodei, Michael Berry, and William Bialek. “The Simplest Maximum Entropy Model for Collective Behavior in a Neural Network.” <i>Journal of Statistical Mechanics Theory and Experiment</i>. IOP Publishing, 2013. <a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03011\">https://doi.org/10.1088/1742-5468/2013/03/P03011</a>.","ista":"Tkačik G, Marre O, Mora T, Amodei D, Berry M, Bialek W. 2013. The simplest maximum entropy model for collective behavior in a neural network. Journal of Statistical Mechanics Theory and Experiment. 2013(3), P03011.","short":"G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, W. Bialek, Journal of Statistical Mechanics Theory and Experiment 2013 (2013).","ieee":"G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, and W. Bialek, “The simplest maximum entropy model for collective behavior in a neural network,” <i>Journal of Statistical Mechanics Theory and Experiment</i>, vol. 2013, no. 3. IOP Publishing, 2013.","mla":"Tkačik, Gašper, et al. “The Simplest Maximum Entropy Model for Collective Behavior in a Neural Network.” <i>Journal of Statistical Mechanics Theory and Experiment</i>, vol. 2013, no. 3, P03011, IOP Publishing, 2013, doi:<a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03011\">10.1088/1742-5468/2013/03/P03011</a>."},"article_processing_charge":"No","isi":1,"author":[{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"},{"last_name":"Mora","first_name":"Thierry","full_name":"Mora, Thierry"},{"full_name":"Amodei, Dario","first_name":"Dario","last_name":"Amodei"},{"full_name":"Berry, Michael","first_name":"Michael","last_name":"Berry"},{"full_name":"Bialek, William","first_name":"William","last_name":"Bialek"}],"language":[{"iso":"eng"}],"arxiv":1,"acknowledgement":"his work was supported in part by NSF Grants IIS-0613435 and PHY-0957573, by NIH Grants R01 EY14196 and P50 GM071508, by the Fannie and John Hertz Foundation, by the Human Frontiers Science Program, by the Swartz Foundation, and by the WM Keck Foundation.\r\n","oa":1,"publist_id":"3942","volume":2013,"scopus_import":"1","month":"03"},{"quality_controlled":"1","day":"12","publication_status":"published","title":"A simple method for estimating the entropy of neural activity","abstract":[{"text":"The number of possible activity patterns in a population of neurons grows exponentially with the size of the population. Typical experiments explore only a tiny fraction of the large space of possible activity patterns in the case of populations with more than 10 or 20 neurons. It is thus impossible, in this undersampled regime, to estimate the probabilities with which most of the activity patterns occur. As a result, the corresponding entropy - which is a measure of the computational power of the neural population - cannot be estimated directly. We propose a simple scheme for estimating the entropy in the undersampled regime, which bounds its value from both below and above. The lower bound is the usual 'naive' entropy of the experimental frequencies. The upper bound results from a hybrid approximation of the entropy which makes use of the naive estimate, a maximum entropy fit, and a coverage adjustment. We apply our simple scheme to artificial data, in order to check their accuracy; we also compare its performance to those of several previously defined entropy estimators. We then apply it to actual measurements of neural activity in populations with up to 100 cells. Finally, we discuss the similarities and differences between the proposed simple estimation scheme and various earlier methods. © 2013 IOP Publishing Ltd and SISSA Medialab srl.","lang":"eng"}],"intvolume":"      2013","publication":"Journal of Statistical Mechanics Theory and Experiment","scopus_import":"1","month":"03","volume":2013,"isi":1,"citation":{"mla":"Berry, Michael, et al. “A Simple Method for Estimating the Entropy of Neural Activity.” <i>Journal of Statistical Mechanics Theory and Experiment</i>, vol. 2013, no. 3, P03015, IOP Publishing, 2013, doi:<a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03015\">10.1088/1742-5468/2013/03/P03015</a>.","ieee":"M. Berry, G. Tkačik, J. Dubuis, O. Marre, and R. Da Silveira, “A simple method for estimating the entropy of neural activity,” <i>Journal of Statistical Mechanics Theory and Experiment</i>, vol. 2013, no. 3. IOP Publishing, 2013.","short":"M. Berry, G. Tkačik, J. Dubuis, O. Marre, R. Da Silveira, Journal of Statistical Mechanics Theory and Experiment 2013 (2013).","chicago":"Berry, Michael, Gašper Tkačik, Julien Dubuis, Olivier Marre, and Ravá Da Silveira. “A Simple Method for Estimating the Entropy of Neural Activity.” <i>Journal of Statistical Mechanics Theory and Experiment</i>. IOP Publishing, 2013. <a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03015\">https://doi.org/10.1088/1742-5468/2013/03/P03015</a>.","ista":"Berry M, Tkačik G, Dubuis J, Marre O, Da Silveira R. 2013. A simple method for estimating the entropy of neural activity. Journal of Statistical Mechanics Theory and Experiment. 2013(3), P03015.","apa":"Berry, M., Tkačik, G., Dubuis, J., Marre, O., &#38; Da Silveira, R. (2013). A simple method for estimating the entropy of neural activity. <i>Journal of Statistical Mechanics Theory and Experiment</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03015\">https://doi.org/10.1088/1742-5468/2013/03/P03015</a>","ama":"Berry M, Tkačik G, Dubuis J, Marre O, Da Silveira R. A simple method for estimating the entropy of neural activity. <i>Journal of Statistical Mechanics Theory and Experiment</i>. 2013;2013(3). doi:<a href=\"https://doi.org/10.1088/1742-5468/2013/03/P03015\">10.1088/1742-5468/2013/03/P03015</a>"},"article_processing_charge":"No","language":[{"iso":"eng"}],"author":[{"first_name":"Michael","last_name":"Berry","full_name":"Berry, Michael"},{"last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"},{"full_name":"Dubuis, Julien","last_name":"Dubuis","first_name":"Julien"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"full_name":"Da Silveira, Ravá","last_name":"Da Silveira","first_name":"Ravá"}],"publist_id":"3941","date_updated":"2025-09-29T13:41:46Z","external_id":{"isi":["000316056900015"]},"department":[{"_id":"GaTk"}],"_id":"2851","article_number":"P03015","oa_version":"None","doi":"10.1088/1742-5468/2013/03/P03015","publisher":"IOP Publishing","type":"journal_article","year":"2013","issue":"3","date_published":"2013-03-12T00:00:00Z","status":"public","date_created":"2018-12-11T11:59:56Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345"},{"doi":"10.1088/1751-8113/46/12/125101","oa_version":"None","article_number":"125101","_id":"2861","department":[{"_id":"GaTk"}],"external_id":{"isi":["000316058200010"]},"date_updated":"2025-09-29T13:36:56Z","date_created":"2018-12-11T11:59:59Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","status":"public","date_published":"2013-03-29T00:00:00Z","issue":"12","year":"2013","type":"journal_article","publisher":"IOP Publishing","publication":"Journal of Physics A: Mathematical and Theoretical","intvolume":"        46","abstract":[{"lang":"eng","text":"We consider a two-parameter family of piecewise linear maps in which the moduli of the two slopes take different values. We provide numerical evidence of the existence of some parameter regions in which the Lyapunov exponent and the topological entropy remain constant. Analytical proof of this phenomenon is also given for certain cases. Surprisingly however, the systems with that property are not conjugate as we prove by using kneading theory."}],"corr_author":"1","publication_status":"published","title":"Lyapunov exponent and topological entropy plateaus in piecewise linear maps","day":"29","quality_controlled":"1","publist_id":"3928","author":[{"full_name":"Botella Soler, Vicente","orcid":"0000-0002-8790-1914","id":"421234E8-F248-11E8-B48F-1D18A9856A87","first_name":"Vicente","last_name":"Botella Soler"},{"full_name":"Oteo, José","first_name":"José","last_name":"Oteo"},{"first_name":"Javier","last_name":"Ros","full_name":"Ros, Javier"},{"first_name":"Paul","last_name":"Glendinning","full_name":"Glendinning, Paul"}],"language":[{"iso":"eng"}],"article_processing_charge":"No","citation":{"ieee":"V. Botella Soler, J. Oteo, J. Ros, and P. Glendinning, “Lyapunov exponent and topological entropy plateaus in piecewise linear maps,” <i>Journal of Physics A: Mathematical and Theoretical</i>, vol. 46, no. 12. IOP Publishing, 2013.","mla":"Botella Soler, Vicente, et al. “Lyapunov Exponent and Topological Entropy Plateaus in Piecewise Linear Maps.” <i>Journal of Physics A: Mathematical and Theoretical</i>, vol. 46, no. 12, 125101, IOP Publishing, 2013, doi:<a href=\"https://doi.org/10.1088/1751-8113/46/12/125101\">10.1088/1751-8113/46/12/125101</a>.","short":"V. Botella Soler, J. Oteo, J. Ros, P. Glendinning, Journal of Physics A: Mathematical and Theoretical 46 (2013).","ista":"Botella Soler V, Oteo J, Ros J, Glendinning P. 2013. Lyapunov exponent and topological entropy plateaus in piecewise linear maps. Journal of Physics A: Mathematical and Theoretical. 46(12), 125101.","chicago":"Botella Soler, Vicente, José Oteo, Javier Ros, and Paul Glendinning. “Lyapunov Exponent and Topological Entropy Plateaus in Piecewise Linear Maps.” <i>Journal of Physics A: Mathematical and Theoretical</i>. IOP Publishing, 2013. <a href=\"https://doi.org/10.1088/1751-8113/46/12/125101\">https://doi.org/10.1088/1751-8113/46/12/125101</a>.","apa":"Botella Soler, V., Oteo, J., Ros, J., &#38; Glendinning, P. (2013). Lyapunov exponent and topological entropy plateaus in piecewise linear maps. <i>Journal of Physics A: Mathematical and Theoretical</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1751-8113/46/12/125101\">https://doi.org/10.1088/1751-8113/46/12/125101</a>","ama":"Botella Soler V, Oteo J, Ros J, Glendinning P. Lyapunov exponent and topological entropy plateaus in piecewise linear maps. <i>Journal of Physics A: Mathematical and Theoretical</i>. 2013;46(12). doi:<a href=\"https://doi.org/10.1088/1751-8113/46/12/125101\">10.1088/1751-8113/46/12/125101</a>"},"isi":1,"volume":46,"scopus_import":"1","month":"03"},{"quality_controlled":"1","day":"01","has_accepted_license":"1","publication_status":"published","title":"Stimulus-dependent maximum entropy models of neural population codes","corr_author":"1","publication":"PLoS Computational Biology","intvolume":"         9","abstract":[{"text":"Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.","lang":"eng"}],"volume":9,"scopus_import":"1","month":"03","oa":1,"publist_id":"3926","citation":{"mla":"Granot Atedgi, Einat, et al. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” <i>PLoS Computational Biology</i>, vol. 9, no. 3, e1002922, Public Library of Science, 2013, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">10.1371/journal.pcbi.1002922</a>.","ieee":"E. Granot Atedgi, G. Tkačik, R. Segev, and E. Schneidman, “Stimulus-dependent maximum entropy models of neural population codes,” <i>PLoS Computational Biology</i>, vol. 9, no. 3. Public Library of Science, 2013.","short":"E. Granot Atedgi, G. Tkačik, R. Segev, E. Schneidman, PLoS Computational Biology 9 (2013).","ama":"Granot Atedgi E, Tkačik G, Segev R, Schneidman E. Stimulus-dependent maximum entropy models of neural population codes. <i>PLoS Computational Biology</i>. 2013;9(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">10.1371/journal.pcbi.1002922</a>","apa":"Granot Atedgi, E., Tkačik, G., Segev, R., &#38; Schneidman, E. (2013). Stimulus-dependent maximum entropy models of neural population codes. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">https://doi.org/10.1371/journal.pcbi.1002922</a>","ista":"Granot Atedgi E, Tkačik G, Segev R, Schneidman E. 2013. Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. 9(3), e1002922.","chicago":"Granot Atedgi, Einat, Gašper Tkačik, Ronen Segev, and Elad Schneidman. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” <i>PLoS Computational Biology</i>. Public Library of Science, 2013. <a href=\"https://doi.org/10.1371/journal.pcbi.1002922\">https://doi.org/10.1371/journal.pcbi.1002922</a>."},"isi":1,"article_processing_charge":"No","author":[{"full_name":"Granot Atedgi, Einat","last_name":"Granot Atedgi","first_name":"Einat"},{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"},{"first_name":"Ronen","last_name":"Segev","full_name":"Segev, Ronen"},{"last_name":"Schneidman","first_name":"Elad","full_name":"Schneidman, Elad"}],"language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}],"date_updated":"2025-09-29T13:35:44Z","external_id":{"isi":["000316864200003"]},"tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"doi":"10.1371/journal.pcbi.1002922","_id":"2863","file":[{"date_created":"2018-12-12T10:14:45Z","content_type":"application/pdf","relation":"main_file","creator":"system","file_name":"IST-2013-120-v1+1_journal.pcbi.1002922.pdf","file_size":1548120,"file_id":"5099","checksum":"5a30876c193209fa05b26db71845dd16","access_level":"open_access","date_updated":"2020-07-14T12:45:52Z"}],"article_number":"e1002922","file_date_updated":"2020-07-14T12:45:52Z","oa_version":"Published Version","year":"2013","ddc":["570"],"issue":"3","type":"journal_article","publisher":"Public Library of Science","date_created":"2018-12-11T12:00:00Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","status":"public","date_published":"2013-03-01T00:00:00Z","pubrep_id":"120"},{"department":[{"_id":"GaTk"}],"date_updated":"2025-09-29T13:29:59Z","external_id":{"arxiv":["1205.6598"],"isi":["000314090300012"]},"doi":"10.1103/PhysRevLett.110.058104","article_number":"058104","_id":"2913","oa_version":"Preprint","year":"2013","issue":"5","type":"journal_article","publisher":"American Physical Society","date_created":"2018-12-11T12:00:18Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","status":"public","date_published":"2013-01-28T00:00:00Z","main_file_link":[{"url":"http://arxiv.org/abs/1205.6598","open_access":"1"}],"quality_controlled":"1","day":"28","corr_author":"1","title":"Retinal metric: a stimulus distance measure derived from population neural responses","publication_status":"published","publication":"Physical Review Letters","abstract":[{"text":"The ability of an organism to distinguish between various stimuli is limited by the structure and noise in the population code of its sensory neurons. Here we infer a distance measure on the stimulus space directly from the recorded activity of 100 neurons in the salamander retina. In contrast to previously used measures of stimulus similarity, this &quot;neural metric&quot; tells us how distinguishable a pair of stimulus clips is to the retina, based on the similarity between the induced distributions of population responses. We show that the retinal distance strongly deviates from Euclidean, or any static metric, yet has a simple structure: we identify the stimulus features that the neural population is jointly sensitive to, and show the support-vector-machine- like kernel function relating the stimulus and neural response spaces. We show that the non-Euclidean nature of the retinal distance has important consequences for neural decoding.","lang":"eng"}],"intvolume":"       110","volume":110,"scopus_import":"1","month":"01","arxiv":1,"oa":1,"publist_id":"3830","article_processing_charge":"No","isi":1,"citation":{"mla":"Tkačik, Gašper, et al. “Retinal Metric: A Stimulus Distance Measure Derived from Population Neural Responses.” <i>Physical Review Letters</i>, vol. 110, no. 5, 058104, American Physical Society, 2013, doi:<a href=\"https://doi.org/10.1103/PhysRevLett.110.058104\">10.1103/PhysRevLett.110.058104</a>.","ieee":"G. Tkačik, E. Granot Atedgi, R. Segev, and E. Schneidman, “Retinal metric: a stimulus distance measure derived from population neural responses,” <i>Physical Review Letters</i>, vol. 110, no. 5. American Physical Society, 2013.","short":"G. Tkačik, E. Granot Atedgi, R. Segev, E. Schneidman, Physical Review Letters 110 (2013).","apa":"Tkačik, G., Granot Atedgi, E., Segev, R., &#38; Schneidman, E. (2013). Retinal metric: a stimulus distance measure derived from population neural responses. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevLett.110.058104\">https://doi.org/10.1103/PhysRevLett.110.058104</a>","ama":"Tkačik G, Granot Atedgi E, Segev R, Schneidman E. Retinal metric: a stimulus distance measure derived from population neural responses. <i>Physical Review Letters</i>. 2013;110(5). doi:<a href=\"https://doi.org/10.1103/PhysRevLett.110.058104\">10.1103/PhysRevLett.110.058104</a>","chicago":"Tkačik, Gašper, Einat Granot Atedgi, Ronen Segev, and Elad Schneidman. “Retinal Metric: A Stimulus Distance Measure Derived from Population Neural Responses.” <i>Physical Review Letters</i>. American Physical Society, 2013. <a href=\"https://doi.org/10.1103/PhysRevLett.110.058104\">https://doi.org/10.1103/PhysRevLett.110.058104</a>.","ista":"Tkačik G, Granot Atedgi E, Segev R, Schneidman E. 2013. Retinal metric: a stimulus distance measure derived from population neural responses. Physical Review Letters. 110(5), 058104."},"author":[{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"},{"full_name":"Granot Atedgi, Einat","last_name":"Granot Atedgi","first_name":"Einat"},{"first_name":"Ronen","last_name":"Segev","full_name":"Segev, Ronen"},{"full_name":"Schneidman, Elad","first_name":"Elad","last_name":"Schneidman"}],"language":[{"iso":"eng"}]},{"department":[{"_id":"GaTk"}],"external_id":{"arxiv":["0806.2694"],"isi":["000313006100058"]},"date_updated":"2025-09-29T13:29:28Z","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"doi":"10.1103/PhysRevLett.110.018701","oa_version":"Published Version","file_date_updated":"2020-07-14T12:45:53Z","_id":"2914","article_number":"018701","file":[{"checksum":"72bfbc2094c4680e8a8a6bed668cd06d","access_level":"open_access","date_updated":"2020-07-14T12:45:53Z","creator":"system","file_size":416965,"file_name":"IST-2016-401-v1+1_1281.full.pdf","file_id":"5366","relation":"main_file","date_created":"2018-12-12T10:18:44Z","content_type":"application/pdf"}],"issue":"1","ddc":["530"],"year":"2013","publisher":"American Physical Society","type":"journal_article","article_type":"original","date_created":"2018-12-11T12:00:19Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_published":"2013-01-02T00:00:00Z","pubrep_id":"401","status":"public","day":"02","quality_controlled":"1","intvolume":"       110","abstract":[{"lang":"eng","text":"The scale invariance of natural images suggests an analogy to the statistical mechanics of physical systems at a critical point. Here we examine the distribution of pixels in small image patches and show how to construct the corresponding thermodynamics. We find evidence for criticality in a diverging specific heat, which corresponds to large fluctuations in how &quot;surprising&quot; we find individual images, and in the quantitative form of the entropy vs energy. We identify special image configurations as local energy minima and show that average patches within each basin are interpretable as lines and edges in all orientations."}],"publication":"Physical Review Letters","title":"Statistical thermodynamics of natural images","publication_status":"published","has_accepted_license":"1","month":"01","volume":110,"publist_id":"3829","oa":1,"acknowledgement":"This work was supported in part by NSF Grants No. IIS-0613435, No. IBN-0344678, and No. PHY-0957573, by NIH Grant No. T32 MH065214, by the Human Frontier Science Program, and by the Swartz Foundation.\r\nCC BY 3.0\r\n","arxiv":1,"language":[{"iso":"eng"}],"author":[{"last_name":"Stephens","first_name":"Greg","full_name":"Stephens, Greg"},{"first_name":"Thierry","last_name":"Mora","full_name":"Mora, Thierry"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik"},{"full_name":"Bialek, William","first_name":"William","last_name":"Bialek"}],"article_processing_charge":"No","citation":{"short":"G. Stephens, T. Mora, G. Tkačik, W. Bialek, Physical Review Letters 110 (2013).","ista":"Stephens G, Mora T, Tkačik G, Bialek W. 2013. Statistical thermodynamics of natural images. Physical Review Letters. 110(1), 018701.","chicago":"Stephens, Greg, Thierry Mora, Gašper Tkačik, and William Bialek. “Statistical Thermodynamics of Natural Images.” <i>Physical Review Letters</i>. American Physical Society, 2013. <a href=\"https://doi.org/10.1103/PhysRevLett.110.018701\">https://doi.org/10.1103/PhysRevLett.110.018701</a>.","apa":"Stephens, G., Mora, T., Tkačik, G., &#38; Bialek, W. (2013). Statistical thermodynamics of natural images. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevLett.110.018701\">https://doi.org/10.1103/PhysRevLett.110.018701</a>","ama":"Stephens G, Mora T, Tkačik G, Bialek W. Statistical thermodynamics of natural images. <i>Physical Review Letters</i>. 2013;110(1). doi:<a href=\"https://doi.org/10.1103/PhysRevLett.110.018701\">10.1103/PhysRevLett.110.018701</a>","mla":"Stephens, Greg, et al. “Statistical Thermodynamics of Natural Images.” <i>Physical Review Letters</i>, vol. 110, no. 1, 018701, American Physical Society, 2013, doi:<a href=\"https://doi.org/10.1103/PhysRevLett.110.018701\">10.1103/PhysRevLett.110.018701</a>.","ieee":"G. Stephens, T. Mora, G. Tkačik, and W. Bialek, “Statistical thermodynamics of natural images,” <i>Physical Review Letters</i>, vol. 110, no. 1. American Physical Society, 2013."},"isi":1},{"publication_status":"published","title":"Dynamic persistence of antibiotic-stressed mycobacteria","intvolume":"       339","abstract":[{"text":"Exposure of an isogenic bacterial population to a cidal antibiotic typically fails to eliminate a small fraction of refractory cells. Historically, fractional killing has been attributed to infrequently dividing or nondividing &quot;persisters.&quot; Using microfluidic cultures and time-lapse microscopy, we found that Mycobacterium smegmatis persists by dividing in the presence of the drug isoniazid (INH). Although persistence in these studies was characterized by stable numbers of cells, this apparent stability was actually a dynamic state of balanced division and death. Single cells expressed catalase-peroxidase (KatG), which activates INH, in stochastic pulses that were negatively correlated with cell survival. These behaviors may reflect epigenetic effects, because KatG pulsing and death were correlated between sibling cells. Selection of lineages characterized by infrequent KatG pulsing could allow nonresponsive adaptation during prolonged drug exposure.","lang":"eng"}],"publication":"Science","quality_controlled":"1","day":"04","publist_id":"7321","article_processing_charge":"No","citation":{"short":"Y. Wakamoto, N. Dhar, R.P. Chait, K. Schneider, F. Signorino Gelo, S. Leibler, J. Mckinney, Science 339 (2013) 91–95.","apa":"Wakamoto, Y., Dhar, N., Chait, R. P., Schneider, K., Signorino Gelo, F., Leibler, S., &#38; Mckinney, J. (2013). Dynamic persistence of antibiotic-stressed mycobacteria. <i>Science</i>. American Association for the Advancement of Science. <a href=\"https://doi.org/10.1126/science.1229858\">https://doi.org/10.1126/science.1229858</a>","ama":"Wakamoto Y, Dhar N, Chait RP, et al. Dynamic persistence of antibiotic-stressed mycobacteria. <i>Science</i>. 2013;339(6115):91-95. doi:<a href=\"https://doi.org/10.1126/science.1229858\">10.1126/science.1229858</a>","ista":"Wakamoto Y, Dhar N, Chait RP, Schneider K, Signorino Gelo F, Leibler S, Mckinney J. 2013. Dynamic persistence of antibiotic-stressed mycobacteria. Science. 339(6115), 91–95.","chicago":"Wakamoto, Yurichi, Neraaj Dhar, Remy P Chait, Katrin Schneider, François Signorino Gelo, Stanislas Leibler, and John Mckinney. “Dynamic Persistence of Antibiotic-Stressed Mycobacteria.” <i>Science</i>. American Association for the Advancement of Science, 2013. <a href=\"https://doi.org/10.1126/science.1229858\">https://doi.org/10.1126/science.1229858</a>.","ieee":"Y. Wakamoto <i>et al.</i>, “Dynamic persistence of antibiotic-stressed mycobacteria,” <i>Science</i>, vol. 339, no. 6115. American Association for the Advancement of Science, pp. 91–95, 2013.","mla":"Wakamoto, Yurichi, et al. “Dynamic Persistence of Antibiotic-Stressed Mycobacteria.” <i>Science</i>, vol. 339, no. 6115, American Association for the Advancement of Science, 2013, pp. 91–95, doi:<a href=\"https://doi.org/10.1126/science.1229858\">10.1126/science.1229858</a>."},"isi":1,"page":"91 - 95","language":[{"iso":"eng"}],"author":[{"last_name":"Wakamoto","first_name":"Yurichi","full_name":"Wakamoto, Yurichi"},{"full_name":"Dhar, Neraaj","last_name":"Dhar","first_name":"Neraaj"},{"last_name":"Chait","first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P"},{"last_name":"Schneider","first_name":"Katrin","full_name":"Schneider, Katrin"},{"last_name":"Signorino Gelo","first_name":"François","full_name":"Signorino Gelo, François"},{"full_name":"Leibler, Stanislas","last_name":"Leibler","first_name":"Stanislas"},{"last_name":"Mckinney","first_name":"John","full_name":"Mckinney, John"}],"month":"01","scopus_import":"1","volume":339,"doi":"10.1126/science.1229858","_id":"499","oa_version":"None","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_updated":"2025-09-30T07:29:30Z","external_id":{"isi":["000312985800059"]},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2018-12-11T11:46:48Z","date_published":"2013-01-04T00:00:00Z","status":"public","year":"2013","issue":"6115","publisher":"American Association for the Advancement of Science","type":"journal_article"},{"year":"2013","ddc":["570"],"issue":"41","pmid":1,"type":"journal_article","publisher":"National Academy of Sciences","date_created":"2018-12-11T12:02:19Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","status":"public","date_published":"2013-10-08T00:00:00Z","department":[{"_id":"GaTk"}],"date_updated":"2025-09-29T13:24:21Z","external_id":{"isi":["000325395600021"],"pmid":["24089448"]},"doi":"10.1073/pnas.1315642110","file":[{"checksum":"ecd859fe52a562193027d428b5524a8d","date_updated":"2020-07-14T12:46:06Z","access_level":"open_access","creator":"dernst","file_id":"5873","file_size":1670548,"file_name":"2013_PNAS_Dubuis.pdf","relation":"main_file","content_type":"application/pdf","date_created":"2019-01-22T13:53:23Z"}],"_id":"3261","file_date_updated":"2020-07-14T12:46:06Z","oa_version":"Published Version","volume":110,"scopus_import":"1","month":"10","oa":1,"publist_id":"3387","page":"16301 - 16308","article_processing_charge":"No","citation":{"ista":"Dubuis J, Tkačik G, Wieschaus E, Gregor T, Bialek W. 2013. Positional information, in bits. PNAS. 110(41), 16301–16308.","chicago":"Dubuis, Julien, Gašper Tkačik, Eric Wieschaus, Thomas Gregor, and William Bialek. “Positional Information, in Bits.” <i>PNAS</i>. National Academy of Sciences, 2013. <a href=\"https://doi.org/10.1073/pnas.1315642110\">https://doi.org/10.1073/pnas.1315642110</a>.","ama":"Dubuis J, Tkačik G, Wieschaus E, Gregor T, Bialek W. Positional information, in bits. <i>PNAS</i>. 2013;110(41):16301-16308. doi:<a href=\"https://doi.org/10.1073/pnas.1315642110\">10.1073/pnas.1315642110</a>","apa":"Dubuis, J., Tkačik, G., Wieschaus, E., Gregor, T., &#38; Bialek, W. (2013). Positional information, in bits. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1315642110\">https://doi.org/10.1073/pnas.1315642110</a>","short":"J. Dubuis, G. Tkačik, E. Wieschaus, T. Gregor, W. Bialek, PNAS 110 (2013) 16301–16308.","ieee":"J. Dubuis, G. Tkačik, E. Wieschaus, T. Gregor, and W. Bialek, “Positional information, in bits,” <i>PNAS</i>, vol. 110, no. 41. National Academy of Sciences, pp. 16301–16308, 2013.","mla":"Dubuis, Julien, et al. “Positional Information, in Bits.” <i>PNAS</i>, vol. 110, no. 41, National Academy of Sciences, 2013, pp. 16301–08, doi:<a href=\"https://doi.org/10.1073/pnas.1315642110\">10.1073/pnas.1315642110</a>."},"isi":1,"author":[{"last_name":"Dubuis","first_name":"Julien","full_name":"Dubuis, Julien"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"},{"first_name":"Eric","last_name":"Wieschaus","full_name":"Wieschaus, Eric"},{"last_name":"Gregor","first_name":"Thomas","full_name":"Gregor, Thomas"},{"first_name":"William","last_name":"Bialek","full_name":"Bialek, William"}],"language":[{"iso":"eng"}],"quality_controlled":"1","day":"08","has_accepted_license":"1","title":"Positional information, in bits","publication_status":"published","publication":"PNAS","intvolume":"       110","abstract":[{"text":"Cells in a developing embryo have no direct way of &quot;measuring&quot; their physical position. Through a variety of processes, however, the expression levels of multiple genes come to be correlated with position, and these expression levels thus form a code for &quot;positional information.&quot; We show how to measure this information, in bits, using the gap genes in the Drosophila embryo as an example. Individual genes carry nearly two bits of information, twice as much as expected if the expression patterns consisted only of on/off domains separated by sharp boundaries. Taken together, four gap genes carry enough information to define a cell's location with an error bar of ~1% along the anterior-posterior axis of the embryo. This precision is nearly enough for each cell to have a unique identity, which is the maximum information the system can use, and is nearly constant along the length of the embryo. We argue that this constancy is a signature of optimality in the transmission of information from primary morphogen inputs to the output of the gap gene network.","lang":"eng"}]},{"oa_version":"Preprint","_id":"2818","das_tickbox":"1","doi":"10.1162/NECO_a_00463","external_id":{"arxiv":["1209.0121"],"isi":["000319903700001"]},"date_updated":"2026-07-06T13:45:52Z","department":[{"_id":"GaTk"}],"date_published":"2013-07-01T00:00:00Z","status":"public","date_created":"2018-12-11T11:59:45Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"MIT Press","type":"journal_article","issue":"7","year":"2013","abstract":[{"text":"Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are selective for only a small number of linear projections of a potentially high-dimensional input. In this review, we explore recent modeling approaches where the neural response depends on the quadratic form of the input rather than on its linear projection, that is, the neuron is sensitive to the local covariance structure of the signal preceding the spike. To infer this quadratic dependence in the presence of arbitrary (e.g., naturalistic) stimulus distribution, we review several inference methods, focusing in particular on two information theory–based approaches (maximization of stimulus energy and of noise entropy) and two likelihood-based approaches (Bayesian spike-triggered covariance and extensions of generalized linear models). We analyze the formal relationship between the likelihood-based and information-based approaches to demonstrate how they lead to consistent inference. We demonstrate the practical feasibility of these procedures by using model neurons responding to a flickering variance stimulus.","lang":"eng"}],"intvolume":"        25","publication":"Neural Computation","publication_status":"published","title":"Learning quadratic receptive fields from neural responses to natural stimuli","day":"01","quality_controlled":"1","main_file_link":[{"url":"http://arxiv.org/abs/1209.0121","open_access":"1"}],"language":[{"iso":"eng"}],"author":[{"full_name":"Rajan, Kanaka","last_name":"Rajan","first_name":"Kanaka"},{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik"}],"citation":{"ama":"Rajan K, Marre O, Tkačik G. Learning quadratic receptive fields from neural responses to natural stimuli. <i>Neural Computation</i>. 2013;25(7):1661-1692. doi:<a href=\"https://doi.org/10.1162/NECO_a_00463\">10.1162/NECO_a_00463</a>","apa":"Rajan, K., Marre, O., &#38; Tkačik, G. (2013). Learning quadratic receptive fields from neural responses to natural stimuli. <i>Neural Computation</i>. MIT Press. <a href=\"https://doi.org/10.1162/NECO_a_00463\">https://doi.org/10.1162/NECO_a_00463</a>","chicago":"Rajan, Kanaka, Olivier Marre, and Gašper Tkačik. “Learning Quadratic Receptive Fields from Neural Responses to Natural Stimuli.” <i>Neural Computation</i>. MIT Press, 2013. <a href=\"https://doi.org/10.1162/NECO_a_00463\">https://doi.org/10.1162/NECO_a_00463</a>.","ista":"Rajan K, Marre O, Tkačik G. 2013. Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation. 25(7), 1661–1692.","short":"K. Rajan, O. Marre, G. Tkačik, Neural Computation 25 (2013) 1661–1692.","mla":"Rajan, Kanaka, et al. “Learning Quadratic Receptive Fields from Neural Responses to Natural Stimuli.” <i>Neural Computation</i>, vol. 25, no. 7, MIT Press, 2013, pp. 1661–92, doi:<a href=\"https://doi.org/10.1162/NECO_a_00463\">10.1162/NECO_a_00463</a>.","ieee":"K. Rajan, O. Marre, and G. Tkačik, “Learning quadratic receptive fields from neural responses to natural stimuli,” <i>Neural Computation</i>, vol. 25, no. 7. MIT Press, pp. 1661–1692, 2013."},"isi":1,"article_processing_charge":"No","page":"1661 - 1692","publist_id":"3983","oa":1,"arxiv":1,"month":"07","scopus_import":"1","volume":25},{"year":"2012","issue":"6","publisher":"Elsevier","type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T12:02:24Z","date_published":"2012-06-01T00:00:00Z","status":"public","department":[{"_id":"GaTk"}],"date_updated":"2026-07-07T13:10:10Z","external_id":{"isi":["000301691600005"]},"doi":"10.1016/j.enganabound.2011.12.014","_id":"3274","das_tickbox":"1","oa_version":"None","scopus_import":"1","month":"06","volume":36,"acknowledgement":"This work was supported by the Austrian Federal Ministry of Transport, Innovation and Technology under the Grant Bmvit-isb2 and the FFG under the project Pr. Nr. 809089.","publist_id":"3372","article_processing_charge":"No","citation":{"mla":"Rieckh, Georg, et al. “A 2.5D-Fourier-BEM Model for Vibrations in a Tunnel Running through Layered Anisotropic Soil.” <i>Engineering Analysis with Boundary Elements</i>, vol. 36, no. 6, Elsevier, 2012, pp. 960–67, doi:<a href=\"https://doi.org/10.1016/j.enganabound.2011.12.014\">10.1016/j.enganabound.2011.12.014</a>.","ieee":"G. Rieckh, W. Kreuzer, H. Waubke, and P. Balazs, “A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil,” <i>Engineering Analysis with Boundary Elements</i>, vol. 36, no. 6. Elsevier, pp. 960–967, 2012.","apa":"Rieckh, G., Kreuzer, W., Waubke, H., &#38; Balazs, P. (2012). A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil. <i>Engineering Analysis with Boundary Elements</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.enganabound.2011.12.014\">https://doi.org/10.1016/j.enganabound.2011.12.014</a>","ama":"Rieckh G, Kreuzer W, Waubke H, Balazs P. A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil. <i>Engineering Analysis with Boundary Elements</i>. 2012;36(6):960-967. doi:<a href=\"https://doi.org/10.1016/j.enganabound.2011.12.014\">10.1016/j.enganabound.2011.12.014</a>","ista":"Rieckh G, Kreuzer W, Waubke H, Balazs P. 2012. A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil. Engineering Analysis with Boundary Elements. 36(6), 960–967.","chicago":"Rieckh, Georg, Wolfgang Kreuzer, Holger Waubke, and Peter Balazs. “A 2.5D-Fourier-BEM Model for Vibrations in a Tunnel Running through Layered Anisotropic Soil.” <i>Engineering Analysis with Boundary Elements</i>. Elsevier, 2012. <a href=\"https://doi.org/10.1016/j.enganabound.2011.12.014\">https://doi.org/10.1016/j.enganabound.2011.12.014</a>.","short":"G. Rieckh, W. Kreuzer, H. Waubke, P. Balazs, Engineering Analysis with Boundary Elements 36 (2012) 960–967."},"isi":1,"page":"960 - 967","language":[{"iso":"eng"}],"author":[{"last_name":"Rieckh","first_name":"Georg","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg"},{"full_name":"Kreuzer, Wolfgang","first_name":"Wolfgang","last_name":"Kreuzer"},{"last_name":"Waubke","first_name":"Holger","full_name":"Waubke, Holger"},{"first_name":"Peter","last_name":"Balazs","full_name":"Balazs, Peter"}],"quality_controlled":"1","day":"01","corr_author":"1","publication_status":"published","title":"A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil","intvolume":"        36","abstract":[{"text":"A boundary element model of a tunnel running through horizontally layered soil with anisotropic material properties is presented. Since there is no analytical fundamental solution for wave propagation inside a layered orthotropic medium in 3D, the fundamental displacements and stresses have to be calculated numerically. In our model this is done in the Fourier domain with respect to space and time. The assumption of a straight tunnel with infinite extension in the x direction makes it possible to decouple the system for every wave number kx, leading to a 2.5D-problem, which is suited for parallel computation. The special form of the fundamental solution, resulting from our Fourier ansatz, and the fact, that the calculation of the boundary integral equation is performed in the Fourier domain, enhances the stability and efficiency of the numerical calculations.","lang":"eng"}],"publication":"Engineering Analysis with Boundary Elements"},{"scopus_import":"1","month":"04","volume":85,"language":[{"iso":"eng"}],"author":[{"last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"},{"full_name":"Walczak, Aleksandra","last_name":"Walczak","first_name":"Aleksandra"},{"last_name":"Bialek","first_name":"William","full_name":"Bialek, William"}],"citation":{"mla":"Tkačik, Gašper, et al. “Optimizing Information Flow in Small Genetic Networks. III. A Self-Interacting Gene.” <i>Physical Review E</i>, vol. 85, no. 4, 041903, American Institute of Physics, 2012, doi:<a href=\"https://doi.org/10.1103/PhysRevE.85.041903\">10.1103/PhysRevE.85.041903</a>.","ieee":"G. Tkačik, A. Walczak, and W. Bialek, “Optimizing information flow in small genetic networks. III. A self-interacting gene,” <i>Physical Review E</i>, vol. 85, no. 4. American Institute of Physics, 2012.","ama":"Tkačik G, Walczak A, Bialek W. Optimizing information flow in small genetic networks. III. A self-interacting gene. <i>Physical Review E</i>. 2012;85(4). doi:<a href=\"https://doi.org/10.1103/PhysRevE.85.041903\">10.1103/PhysRevE.85.041903</a>","apa":"Tkačik, G., Walczak, A., &#38; Bialek, W. (2012). Optimizing information flow in small genetic networks. III. A self-interacting gene. <i>Physical Review E</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.85.041903\">https://doi.org/10.1103/PhysRevE.85.041903</a>","ista":"Tkačik G, Walczak A, Bialek W. 2012. Optimizing information flow in small genetic networks. III. A self-interacting gene. Physical Review E. 85(4), 041903.","chicago":"Tkačik, Gašper, Aleksandra Walczak, and William Bialek. “Optimizing Information Flow in Small Genetic Networks. III. A Self-Interacting Gene.” <i>Physical Review E</i>. American Institute of Physics, 2012. <a href=\"https://doi.org/10.1103/PhysRevE.85.041903\">https://doi.org/10.1103/PhysRevE.85.041903</a>.","short":"G. Tkačik, A. Walczak, W. Bialek, Physical Review E 85 (2012)."},"isi":1,"article_processing_charge":"No","publist_id":"3386","oa":1,"acknowledgement":"We thank T. Gregor, E. F. Wieschaus, and, especially, C. G. Callan for helpful discussions.\r\nWork at Princeton was supported in part by NSF Grants No. PHY–0957573 and No. CCF–0939370, by NIH Grant No. R01 GM077599, and by the W. M. Keck Foundation. For part of this work, G.T. was supported in part by NSF Grant No. EF–0928048 and by the Vice Provost for Research at the University of Pennsylvania.","arxiv":1,"day":"01","quality_controlled":"1","main_file_link":[{"url":"http://arxiv.org/abs/1112.5026","open_access":"1"}],"intvolume":"        85","abstract":[{"text":"Living cells must control the reading out or &quot;expression&quot; of information encoded in their genomes, and this regulation often is mediated by transcription factors--proteins that bind to DNA and either enhance or repress the expression of nearby genes. But the expression of transcription factor proteins is itself regulated, and many transcription factors regulate their own expression in addition to responding to other input signals. Here we analyze the simplest of such self-regulatory circuits, asking how parameters can be chosen to optimize information transmission from inputs to outputs in the steady state. Some nonzero level of self-regulation is almost always optimal, with self-activation dominant when transcription factor concentrations are low and self-repression dominant when concentrations are high. In steady state the optimal self-activation is never strong enough to induce bistability, although there is a limit in which the optimal parameters are very close to the critical point.","lang":"eng"}],"publication":"Physical Review E","publication_status":"published","corr_author":"1","title":"Optimizing information flow in small genetic networks. III. A self-interacting gene","publisher":"American Institute of Physics","type":"journal_article","issue":"4","year":"2012","date_published":"2012-04-01T00:00:00Z","status":"public","date_created":"2018-12-11T12:02:20Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"isi":["000302410200006"],"arxiv":["1112.5026"]},"date_updated":"2026-07-07T13:21:57Z","department":[{"_id":"GaTk"}],"oa_version":"Preprint","article_number":"041903","_id":"3262","das_tickbox":"1","doi":"10.1103/PhysRevE.85.041903"},{"date_published":"2011-04-01T00:00:00Z","status":"public","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2018-12-11T12:02:58Z","publisher":"IOP Publishing","type":"journal_article","year":"2011","issue":"15","article_number":"153102","_id":"3374","oa_version":"Submitted Version","doi":"10.1088/0953-8984/23/15/153102","date_updated":"2025-09-30T08:56:23Z","external_id":{"arxiv":["1101.4240"],"isi":["000289199800002"]},"department":[{"_id":"GaTk"}],"citation":{"ieee":"G. Tkačik and A. Walczak, “Information transmission in genetic regulatory networks a review,” <i>Journal of Physics: Condensed Matter</i>, vol. 23, no. 15. IOP Publishing, 2011.","mla":"Tkačik, Gašper, and Aleksandra Walczak. “Information Transmission in Genetic Regulatory Networks a Review.” <i>Journal of Physics: Condensed Matter</i>, vol. 23, no. 15, 153102, IOP Publishing, 2011, doi:<a href=\"https://doi.org/10.1088/0953-8984/23/15/153102\">10.1088/0953-8984/23/15/153102</a>.","ama":"Tkačik G, Walczak A. Information transmission in genetic regulatory networks a review. <i>Journal of Physics: Condensed Matter</i>. 2011;23(15). doi:<a href=\"https://doi.org/10.1088/0953-8984/23/15/153102\">10.1088/0953-8984/23/15/153102</a>","apa":"Tkačik, G., &#38; Walczak, A. (2011). Information transmission in genetic regulatory networks a review. <i>Journal of Physics: Condensed Matter</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/0953-8984/23/15/153102\">https://doi.org/10.1088/0953-8984/23/15/153102</a>","ista":"Tkačik G, Walczak A. 2011. Information transmission in genetic regulatory networks a review. Journal of Physics: Condensed Matter. 23(15), 153102.","chicago":"Tkačik, Gašper, and Aleksandra Walczak. “Information Transmission in Genetic Regulatory Networks a Review.” <i>Journal of Physics: Condensed Matter</i>. IOP Publishing, 2011. <a href=\"https://doi.org/10.1088/0953-8984/23/15/153102\">https://doi.org/10.1088/0953-8984/23/15/153102</a>.","short":"G. Tkačik, A. Walczak, Journal of Physics: Condensed Matter 23 (2011)."},"isi":1,"article_processing_charge":"No","language":[{"iso":"eng"}],"author":[{"last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"},{"full_name":"Walczak, Aleksandra","last_name":"Walczak","first_name":"Aleksandra"}],"arxiv":1,"publist_id":"3233","oa":1,"month":"04","scopus_import":"1","volume":23,"publication_status":"published","corr_author":"1","title":"Information transmission in genetic regulatory networks a review","abstract":[{"text":"Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'.","lang":"eng"}],"intvolume":"        23","publication":"Journal of Physics: Condensed Matter","quality_controlled":"1","day":"01","main_file_link":[{"url":"http://arxiv.org/abs/1101.4240","open_access":"1"}]},{"publication_status":"published","corr_author":"1","title":"Natural images from the birthplace of the human eye","has_accepted_license":"1","abstract":[{"text":"Here we introduce a database of calibrated natural images publicly available through an easy-to-use web interface. Using a Nikon D70 digital SLR camera, we acquired about  six-megapixel images of Okavango Delta of Botswana, a tropical savanna habitat similar to where the human eye is thought to have evolved. Some sequences of images were captured unsystematically while following a baboon troop, while others were designed to vary a single parameter such as aperture, object distance, time of day or position on the horizon. Images are available in the raw RGB format and in grayscale. Images are also available in units relevant to the physiology of human cone photoreceptors, where pixel values represent the expected number of photoisomerizations per second for cones sensitive to long (L), medium (M) and short (S) wavelengths. This database is distributed under a Creative Commons Attribution-Noncommercial Unported license to facilitate research in computer vision, psychophysics of perception, and visual neuroscience.","lang":"eng"}],"intvolume":"         6","publication":"PLoS One","quality_controlled":"1","day":"16","publist_id":"3223","oa":1,"citation":{"ista":"Tkačik G, Garrigan P, Ratliff C, Milcinski G, Klein J, Seyfarth L, Sterling P, Brainard D, Balasubramanian V. 2011. Natural images from the birthplace of the human eye. PLoS One. 6(6), e20409.","chicago":"Tkačik, Gašper, Patrick Garrigan, Charles Ratliff, Grega Milcinski, Jennifer Klein, Lucia Seyfarth, Peter Sterling, David Brainard, and Vijay Balasubramanian. “Natural Images from the Birthplace of the Human Eye.” <i>PLoS One</i>. Public Library of Science, 2011. <a href=\"https://doi.org/10.1371/journal.pone.0020409\">https://doi.org/10.1371/journal.pone.0020409</a>.","apa":"Tkačik, G., Garrigan, P., Ratliff, C., Milcinski, G., Klein, J., Seyfarth, L., … Balasubramanian, V. (2011). Natural images from the birthplace of the human eye. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0020409\">https://doi.org/10.1371/journal.pone.0020409</a>","ama":"Tkačik G, Garrigan P, Ratliff C, et al. Natural images from the birthplace of the human eye. <i>PLoS One</i>. 2011;6(6). doi:<a href=\"https://doi.org/10.1371/journal.pone.0020409\">10.1371/journal.pone.0020409</a>","short":"G. Tkačik, P. Garrigan, C. Ratliff, G. Milcinski, J. Klein, L. Seyfarth, P. Sterling, D. Brainard, V. Balasubramanian, PLoS One 6 (2011).","mla":"Tkačik, Gašper, et al. “Natural Images from the Birthplace of the Human Eye.” <i>PLoS One</i>, vol. 6, no. 6, e20409, Public Library of Science, 2011, doi:<a href=\"https://doi.org/10.1371/journal.pone.0020409\">10.1371/journal.pone.0020409</a>.","ieee":"G. Tkačik <i>et al.</i>, “Natural images from the birthplace of the human eye,” <i>PLoS One</i>, vol. 6, no. 6. 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