--- _id: '9752' 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. article_processing_charge: No author: - first_name: Kristina full_name: Simmons, Kristina last_name: Simmons - first_name: Jason full_name: Prentice, Jason last_name: Prentice - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Jan full_name: Homann, Jan last_name: Homann - first_name: Heather full_name: Yee, Heather last_name: Yee - first_name: Stephanie full_name: Palmer, Stephanie last_name: Palmer - first_name: Philip full_name: Nelson, Philip last_name: Nelson - first_name: Vijay full_name: Balasubramanian, Vijay last_name: Balasubramanian citation: ama: 'Simmons K, Prentice J, Tkačik G, et al. Data from: Transformation of stimulus correlations by the retina. 2014. doi:10.5061/dryad.246qg' apa: 'Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., … Balasubramanian, V. (2014). Data from: Transformation of stimulus correlations by the retina. Dryad. https://doi.org/10.5061/dryad.246qg' chicago: 'Simmons, Kristina, Jason Prentice, Gašper Tkačik, Jan Homann, Heather Yee, Stephanie Palmer, Philip Nelson, and Vijay Balasubramanian. “Data from: Transformation of Stimulus Correlations by the Retina.” Dryad, 2014. https://doi.org/10.5061/dryad.246qg.' ieee: 'K. Simmons et al., “Data from: Transformation of stimulus correlations by the retina.” Dryad, 2014.' ista: 'Simmons K, Prentice J, Tkačik G, Homann J, Yee H, Palmer S, Nelson P, Balasubramanian V. 2014. Data from: Transformation of stimulus correlations by the retina, Dryad, 10.5061/dryad.246qg.' mla: 'Simmons, Kristina, et al. Data from: Transformation of Stimulus Correlations by the Retina. Dryad, 2014, doi:10.5061/dryad.246qg.' short: K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson, V. Balasubramanian, (2014). date_created: 2021-07-30T08:13:52Z date_published: 2014-11-07T00:00:00Z date_updated: 2023-02-23T10:35:57Z day: '07' department: - _id: GaTk doi: 10.5061/dryad.246qg main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.246qg month: '11' oa: 1 oa_version: Published Version publisher: Dryad related_material: record: - id: '2277' relation: used_in_publication status: public status: public title: 'Data from: Transformation of stimulus correlations by the retina' type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2014' ... --- _id: '2257' abstract: - lang: eng text: 'Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population''s capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.' acknowledgement: "\r\n\r\n\r\n\r\nThis work was funded by NSF grant IIS-0613435, NSF grant PHY-0957573, NSF grant CCF-0939370, NIH grant R01 EY14196, NIH grant P50 GM071508, the Fannie and John Hertz Foundation, the Swartz Foundation, the WM Keck Foundation, ANR Optima and the French State program “Investissements d'Avenir” [LIFESENSES: ANR-10-LABX-65], and the Austrian Research Foundation FWF P25651." article_number: e1003408 author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Dario full_name: Amodei, Dario last_name: Amodei - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman - first_name: William full_name: Bialek, William last_name: Bialek - first_name: Michael full_name: Berry, Michael last_name: Berry citation: ama: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 2014;10(1). doi:10.1371/journal.pcbi.1003408 apa: Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., & Berry, M. (2014). Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1003408 chicago: Tkačik, Gašper, Olivier Marre, Dario Amodei, Elad Schneidman, William Bialek, and Michael Berry. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” PLoS Computational Biology. Public Library of Science, 2014. https://doi.org/10.1371/journal.pcbi.1003408. ieee: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching for collective behavior in a large network of sensory neurons,” PLoS Computational Biology, vol. 10, no. 1. Public Library of Science, 2014. ista: Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. 2014. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 10(1), e1003408. mla: Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” PLoS Computational Biology, vol. 10, no. 1, e1003408, Public Library of Science, 2014, doi:10.1371/journal.pcbi.1003408. short: G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS Computational Biology 10 (2014). date_created: 2018-12-11T11:56:36Z date_published: 2014-01-02T00:00:00Z date_updated: 2024-02-21T13:46:14Z day: '02' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1003408 file: - access_level: open_access checksum: c720222c5e924a4acb17f23b9381a6ca content_type: application/pdf creator: system date_created: 2018-12-12T10:12:46Z date_updated: 2020-07-14T12:45:35Z file_id: '4965' file_name: IST-2016-436-v1+1_journal.pcbi.1003408.pdf file_size: 2194790 relation: main_file file_date_updated: 2020-07-14T12:45:35Z has_accepted_license: '1' intvolume: ' 10' issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: http://repository.ist.ac.at/id/eprint/436 month: '01' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_identifier: issn: - 1553734X publication_status: published publisher: Public Library of Science publist_id: '4689' pubrep_id: '436' quality_controlled: '1' related_material: record: - id: '5562' relation: popular_science status: public scopus_import: 1 status: public title: Searching for collective behavior in a large network of sensory neurons tmp: 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) short: CC BY (4.0) type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 10 year: '2014' ... --- _id: '2413' abstract: - lang: eng 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.' alternative_title: - Reviews of Nonlinear Dynamics and Complexity author: - first_name: Mario full_name: Valderrama, Mario last_name: Valderrama - first_name: Vicente full_name: Botella Soler, Vicente id: 421234E8-F248-11E8-B48F-1D18A9856A87 last_name: Botella Soler orcid: 0000-0002-8790-1914 - first_name: Michel full_name: Le Van Quyen, Michel last_name: Le Van Quyen citation: 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. Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH; 2013. doi:10.1002/9783527671632.ch08' apa: 'Valderrama, M., Botella Soler, V., & Le Van Quyen, M. (2013). Neuronal oscillations scale up and scale down the brain dynamics . In M. Meyer & Z. Pesenson (Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH. https://doi.org/10.1002/9783527671632.ch08' chicago: 'Valderrama, Mario, Vicente Botella Soler, and Michel Le Van Quyen. “Neuronal Oscillations Scale up and Scale down the Brain Dynamics .” In Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, edited by Misha Meyer and Z. Pesenson. Wiley-VCH, 2013. https://doi.org/10.1002/9783527671632.ch08.' ieee: 'M. Valderrama, V. Botella Soler, and M. Le Van Quyen, “Neuronal oscillations scale up and scale down the brain dynamics ,” in Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, M. Meyer and Z. Pesenson, Eds. Wiley-VCH, 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, .' mla: 'Valderrama, Mario, et al. “Neuronal Oscillations Scale up and Scale down the Brain Dynamics .” Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, edited by Misha Meyer and Z. Pesenson, Wiley-VCH, 2013, doi:10.1002/9783527671632.ch08.' 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-VCH, 2013.' date_created: 2018-12-11T11:57:31Z date_published: 2013-08-01T00:00:00Z date_updated: 2021-01-12T06:57:20Z day: '01' department: - _id: GaTk doi: 10.1002/9783527671632.ch08 editor: - first_name: Misha full_name: Meyer, Misha last_name: Meyer - first_name: Z. full_name: Pesenson, Z. last_name: Pesenson language: - iso: eng month: '08' oa_version: None publication: 'Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain' publication_identifier: eisbn: - '9783527671632' isbn: - '9783527411986 ' publication_status: published publisher: Wiley-VCH publist_id: '4513' quality_controlled: '1' scopus_import: 1 status: public title: 'Neuronal oscillations scale up and scale down the brain dynamics ' type: book_chapter user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 year: '2013' ... --- _id: '2818' abstract: - lang: eng 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. author: - first_name: Kanaka full_name: Rajan, Kanaka last_name: Rajan - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: Rajan K, Marre O, Tkačik G. Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation. 2013;25(7):1661-1692. doi:10.1162/NECO_a_00463 apa: Rajan, K., Marre, O., & Tkačik, G. (2013). Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation. MIT Press . https://doi.org/10.1162/NECO_a_00463 chicago: Rajan, Kanaka, Olivier Marre, and Gašper Tkačik. “Learning Quadratic Receptive Fields from Neural Responses to Natural Stimuli.” Neural Computation. MIT Press , 2013. https://doi.org/10.1162/NECO_a_00463. ieee: K. Rajan, O. Marre, and G. Tkačik, “Learning quadratic receptive fields from neural responses to natural stimuli,” Neural Computation, vol. 25, no. 7. MIT Press , pp. 1661–1692, 2013. ista: Rajan K, Marre O, Tkačik G. 2013. Learning quadratic receptive fields from neural responses to natural stimuli. Neural Computation. 25(7), 1661–1692. mla: Rajan, Kanaka, et al. “Learning Quadratic Receptive Fields from Neural Responses to Natural Stimuli.” Neural Computation, vol. 25, no. 7, MIT Press , 2013, pp. 1661–92, doi:10.1162/NECO_a_00463. short: K. Rajan, O. Marre, G. Tkačik, Neural Computation 25 (2013) 1661–1692. date_created: 2018-12-11T11:59:45Z date_published: 2013-07-01T00:00:00Z date_updated: 2021-01-12T06:59:56Z day: '01' department: - _id: GaTk doi: 10.1162/NECO_a_00463 external_id: arxiv: - '1209.0121' intvolume: ' 25' issue: '7' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1209.0121 month: '07' oa: 1 oa_version: Preprint page: 1661 - 1692 publication: Neural Computation publication_status: published publisher: 'MIT Press ' publist_id: '3983' quality_controlled: '1' scopus_import: 1 status: public title: Learning quadratic receptive fields from neural responses to natural stimuli type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 25 year: '2013' ... --- _id: '2850' abstract: - lang: eng 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" 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" article_number: P03011 article_processing_charge: No article_type: original author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Thierry full_name: Mora, Thierry last_name: Mora - first_name: Dario full_name: Amodei, Dario last_name: Amodei - first_name: Michael full_name: Berry, Michael last_name: Berry - first_name: William full_name: Bialek, William last_name: Bialek citation: 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. Journal of Statistical Mechanics Theory and Experiment. 2013;2013(3). doi:10.1088/1742-5468/2013/03/P03011 apa: 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. IOP Publishing Ltd. https://doi.org/10.1088/1742-5468/2013/03/P03011 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.” Journal of Statistical Mechanics Theory and Experiment. IOP Publishing Ltd., 2013. https://doi.org/10.1088/1742-5468/2013/03/P03011. 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,” Journal of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3. IOP Publishing Ltd., 2013. 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. mla: Tkačik, Gašper, et al. “The Simplest Maximum Entropy Model for Collective Behavior in a Neural Network.” Journal of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3, P03011, IOP Publishing Ltd., 2013, doi:10.1088/1742-5468/2013/03/P03011. short: G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, W. Bialek, Journal of Statistical Mechanics Theory and Experiment 2013 (2013). date_created: 2018-12-11T11:59:55Z date_published: 2013-03-12T00:00:00Z date_updated: 2021-01-12T07:00:14Z day: '12' department: - _id: GaTk doi: 10.1088/1742-5468/2013/03/P03011 external_id: arxiv: - '1207.6319' intvolume: ' 2013' issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1207.6319 month: '03' oa: 1 oa_version: Preprint publication: Journal of Statistical Mechanics Theory and Experiment publication_status: published publisher: IOP Publishing Ltd. publist_id: '3942' quality_controlled: '1' scopus_import: 1 status: public title: The simplest maximum entropy model for collective behavior in a neural network type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2013 year: '2013' ... --- _id: '2851' abstract: - lang: eng 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. article_number: P03015 author: - first_name: Michael full_name: Berry, Michael last_name: Berry - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Julien full_name: Dubuis, Julien last_name: Dubuis - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Ravá full_name: Da Silveira, Ravá last_name: Da Silveira citation: ama: Berry M, Tkačik G, Dubuis J, Marre O, Da Silveira R. A simple method for estimating the entropy of neural activity. Journal of Statistical Mechanics Theory and Experiment. 2013;2013(3). doi:10.1088/1742-5468/2013/03/P03015 apa: 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. IOP Publishing Ltd. https://doi.org/10.1088/1742-5468/2013/03/P03015 chicago: Berry, Michael, Gašper Tkačik, Julien Dubuis, Olivier Marre, and Ravá Da Silveira. “A Simple Method for Estimating the Entropy of Neural Activity.” Journal of Statistical Mechanics Theory and Experiment. IOP Publishing Ltd., 2013. https://doi.org/10.1088/1742-5468/2013/03/P03015. ieee: M. Berry, G. Tkačik, J. Dubuis, O. Marre, and R. Da Silveira, “A simple method for estimating the entropy of neural activity,” Journal of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3. IOP Publishing Ltd., 2013. 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. mla: Berry, Michael, et al. “A Simple Method for Estimating the Entropy of Neural Activity.” Journal of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3, P03015, IOP Publishing Ltd., 2013, doi:10.1088/1742-5468/2013/03/P03015. short: M. Berry, G. Tkačik, J. Dubuis, O. Marre, R. Da Silveira, Journal of Statistical Mechanics Theory and Experiment 2013 (2013). date_created: 2018-12-11T11:59:56Z date_published: 2013-03-12T00:00:00Z date_updated: 2021-01-12T07:00:14Z day: '12' department: - _id: GaTk doi: 10.1088/1742-5468/2013/03/P03015 intvolume: ' 2013' issue: '3' language: - iso: eng month: '03' oa_version: None publication: Journal of Statistical Mechanics Theory and Experiment publication_status: published publisher: IOP Publishing Ltd. publist_id: '3941' quality_controlled: '1' scopus_import: 1 status: public title: A simple method for estimating the entropy of neural activity type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2013 year: '2013' ... --- _id: '2863' abstract: - lang: eng 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. article_number: e1002922 author: - first_name: Einat full_name: Granot Atedgi, Einat last_name: Granot Atedgi - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Ronen full_name: Segev, Ronen last_name: Segev - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman citation: ama: Granot Atedgi E, Tkačik G, Segev R, Schneidman E. Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. 2013;9(3). doi:10.1371/journal.pcbi.1002922 apa: Granot Atedgi, E., Tkačik, G., Segev, R., & Schneidman, E. (2013). Stimulus-dependent maximum entropy models of neural population codes. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1002922 chicago: Granot Atedgi, Einat, Gašper Tkačik, Ronen Segev, and Elad Schneidman. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS Computational Biology. Public Library of Science, 2013. https://doi.org/10.1371/journal.pcbi.1002922. ieee: E. Granot Atedgi, G. Tkačik, R. Segev, and E. Schneidman, “Stimulus-dependent maximum entropy models of neural population codes,” PLoS Computational Biology, vol. 9, no. 3. Public Library of Science, 2013. 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. mla: Granot Atedgi, Einat, et al. “Stimulus-Dependent Maximum Entropy Models of Neural Population Codes.” PLoS Computational Biology, vol. 9, no. 3, e1002922, Public Library of Science, 2013, doi:10.1371/journal.pcbi.1002922. short: E. Granot Atedgi, G. Tkačik, R. Segev, E. Schneidman, PLoS Computational Biology 9 (2013). date_created: 2018-12-11T12:00:00Z date_published: 2013-03-01T00:00:00Z date_updated: 2021-01-12T07:00:20Z day: '01' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1002922 file: - access_level: open_access checksum: 5a30876c193209fa05b26db71845dd16 content_type: application/pdf creator: system date_created: 2018-12-12T10:14:45Z date_updated: 2020-07-14T12:45:52Z file_id: '5099' file_name: IST-2013-120-v1+1_journal.pcbi.1002922.pdf file_size: 1548120 relation: main_file file_date_updated: 2020-07-14T12:45:52Z has_accepted_license: '1' intvolume: ' 9' issue: '3' language: - iso: eng month: '03' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_status: published publisher: Public Library of Science publist_id: '3926' pubrep_id: '120' quality_controlled: '1' scopus_import: 1 status: public title: Stimulus-dependent maximum entropy models of neural population codes tmp: 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) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 9 year: '2013' ... --- _id: '2861' 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. article_number: '125101' author: - first_name: Vicente full_name: Botella Soler, Vicente id: 421234E8-F248-11E8-B48F-1D18A9856A87 last_name: Botella Soler orcid: 0000-0002-8790-1914 - first_name: José full_name: Oteo, José last_name: Oteo - first_name: Javier full_name: Ros, Javier last_name: Ros - first_name: Paul full_name: Glendinning, Paul last_name: Glendinning citation: ama: 'Botella Soler V, Oteo J, Ros J, Glendinning P. Lyapunov exponent and topological entropy plateaus in piecewise linear maps. Journal of Physics A: Mathematical and Theoretical. 2013;46(12). doi:10.1088/1751-8113/46/12/125101' apa: '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. IOP Publishing Ltd. https://doi.org/10.1088/1751-8113/46/12/125101' chicago: 'Botella Soler, Vicente, José Oteo, Javier Ros, and Paul Glendinning. “Lyapunov Exponent and Topological Entropy Plateaus in Piecewise Linear Maps.” Journal of Physics A: Mathematical and Theoretical. IOP Publishing Ltd., 2013. https://doi.org/10.1088/1751-8113/46/12/125101.' ieee: 'V. Botella Soler, J. Oteo, J. Ros, and P. Glendinning, “Lyapunov exponent and topological entropy plateaus in piecewise linear maps,” Journal of Physics A: Mathematical and Theoretical, vol. 46, no. 12. IOP Publishing Ltd., 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.' mla: 'Botella Soler, Vicente, et al. “Lyapunov Exponent and Topological Entropy Plateaus in Piecewise Linear Maps.” Journal of Physics A: Mathematical and Theoretical, vol. 46, no. 12, 125101, IOP Publishing Ltd., 2013, doi:10.1088/1751-8113/46/12/125101.' short: 'V. Botella Soler, J. Oteo, J. Ros, P. Glendinning, Journal of Physics A: Mathematical and Theoretical 46 (2013).' date_created: 2018-12-11T11:59:59Z date_published: 2013-03-29T00:00:00Z date_updated: 2021-01-12T07:00:19Z day: '29' department: - _id: GaTk doi: 10.1088/1751-8113/46/12/125101 intvolume: ' 46' issue: '12' language: - iso: eng month: '03' oa_version: None publication: 'Journal of Physics A: Mathematical and Theoretical' publication_status: published publisher: IOP Publishing Ltd. publist_id: '3928' quality_controlled: '1' scopus_import: 1 status: public title: Lyapunov exponent and topological entropy plateaus in piecewise linear maps type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 46 year: '2013' ... --- _id: '2913' abstract: - lang: eng 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 "neural metric" 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.' article_number: '058104' author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Einat full_name: Granot Atedgi, Einat last_name: Granot Atedgi - first_name: Ronen full_name: Segev, Ronen last_name: Segev - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman citation: ama: 'Tkačik G, Granot Atedgi E, Segev R, Schneidman E. Retinal metric: a stimulus distance measure derived from population neural responses. Physical Review Letters. 2013;110(5). doi:10.1103/PhysRevLett.110.058104' apa: 'Tkačik, G., Granot Atedgi, E., Segev, R., & Schneidman, E. (2013). Retinal metric: a stimulus distance measure derived from population neural responses. Physical Review Letters. American Physical Society. https://doi.org/10.1103/PhysRevLett.110.058104' chicago: 'Tkačik, Gašper, Einat Granot Atedgi, Ronen Segev, and Elad Schneidman. “Retinal Metric: A Stimulus Distance Measure Derived from Population Neural Responses.” Physical Review Letters. American Physical Society, 2013. https://doi.org/10.1103/PhysRevLett.110.058104.' ieee: 'G. Tkačik, E. Granot Atedgi, R. Segev, and E. Schneidman, “Retinal metric: a stimulus distance measure derived from population neural responses,” Physical Review Letters, vol. 110, no. 5. American Physical Society, 2013.' 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.' mla: 'Tkačik, Gašper, et al. “Retinal Metric: A Stimulus Distance Measure Derived from Population Neural Responses.” Physical Review Letters, vol. 110, no. 5, 058104, American Physical Society, 2013, doi:10.1103/PhysRevLett.110.058104.' short: G. Tkačik, E. Granot Atedgi, R. Segev, E. Schneidman, Physical Review Letters 110 (2013). date_created: 2018-12-11T12:00:18Z date_published: 2013-01-28T00:00:00Z date_updated: 2021-01-12T07:00:39Z day: '28' department: - _id: GaTk doi: 10.1103/PhysRevLett.110.058104 intvolume: ' 110' issue: '5' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1205.6598 month: '01' oa: 1 oa_version: Preprint publication: Physical Review Letters publication_status: published publisher: American Physical Society publist_id: '3830' quality_controlled: '1' scopus_import: 1 status: public title: 'Retinal metric: a stimulus distance measure derived from population neural responses' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 110 year: '2013' ... --- _id: '3261' abstract: - lang: eng text: Cells in a developing embryo have no direct way of "measuring" 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 "positional information." 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. author: - first_name: Julien full_name: Dubuis, Julien last_name: Dubuis - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Eric full_name: Wieschaus, Eric last_name: Wieschaus - first_name: Thomas full_name: Gregor, Thomas last_name: Gregor - first_name: William full_name: Bialek, William last_name: Bialek citation: ama: Dubuis J, Tkačik G, Wieschaus E, Gregor T, Bialek W. Positional information, in bits. PNAS. 2013;110(41):16301-16308. doi:10.1073/pnas.1315642110 apa: Dubuis, J., Tkačik, G., Wieschaus, E., Gregor, T., & Bialek, W. (2013). Positional information, in bits. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1315642110 chicago: Dubuis, Julien, Gašper Tkačik, Eric Wieschaus, Thomas Gregor, and William Bialek. “Positional Information, in Bits.” PNAS. National Academy of Sciences, 2013. https://doi.org/10.1073/pnas.1315642110. ieee: J. Dubuis, G. Tkačik, E. Wieschaus, T. Gregor, and W. Bialek, “Positional information, in bits,” PNAS, vol. 110, no. 41. National Academy of Sciences, pp. 16301–16308, 2013. ista: Dubuis J, Tkačik G, Wieschaus E, Gregor T, Bialek W. 2013. Positional information, in bits. PNAS. 110(41), 16301–16308. mla: Dubuis, Julien, et al. “Positional Information, in Bits.” PNAS, vol. 110, no. 41, National Academy of Sciences, 2013, pp. 16301–08, doi:10.1073/pnas.1315642110. short: J. Dubuis, G. Tkačik, E. Wieschaus, T. Gregor, W. Bialek, PNAS 110 (2013) 16301–16308. date_created: 2018-12-11T12:02:19Z date_published: 2013-10-08T00:00:00Z date_updated: 2021-01-12T07:42:13Z day: '08' ddc: - '570' department: - _id: GaTk doi: 10.1073/pnas.1315642110 external_id: pmid: - '24089448' file: - access_level: open_access checksum: ecd859fe52a562193027d428b5524a8d content_type: application/pdf creator: dernst date_created: 2019-01-22T13:53:23Z date_updated: 2020-07-14T12:46:06Z file_id: '5873' file_name: 2013_PNAS_Dubuis.pdf file_size: 1670548 relation: main_file file_date_updated: 2020-07-14T12:46:06Z has_accepted_license: '1' intvolume: ' 110' issue: '41' language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 16301 - 16308 pmid: 1 publication: PNAS publication_status: published publisher: National Academy of Sciences publist_id: '3387' quality_controlled: '1' scopus_import: 1 status: public title: Positional information, in bits type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 110 year: '2013' ... --- _id: '499' abstract: - lang: eng 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 "persisters." 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. author: - first_name: Yurichi full_name: Wakamoto, Yurichi last_name: Wakamoto - first_name: Neraaj full_name: Dhar, Neraaj last_name: Dhar - first_name: Remy P full_name: Chait, Remy P id: 3464AE84-F248-11E8-B48F-1D18A9856A87 last_name: Chait orcid: 0000-0003-0876-3187 - first_name: Katrin full_name: Schneider, Katrin last_name: Schneider - first_name: François full_name: Signorino Gelo, François last_name: Signorino Gelo - first_name: Stanislas full_name: Leibler, Stanislas last_name: Leibler - first_name: John full_name: Mckinney, John last_name: Mckinney citation: ama: Wakamoto Y, Dhar N, Chait RP, et al. Dynamic persistence of antibiotic-stressed mycobacteria. Science. 2013;339(6115):91-95. doi:10.1126/science.1229858 apa: Wakamoto, Y., Dhar, N., Chait, R. P., Schneider, K., Signorino Gelo, F., Leibler, S., & Mckinney, J. (2013). Dynamic persistence of antibiotic-stressed mycobacteria. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.1229858 chicago: Wakamoto, Yurichi, Neraaj Dhar, Remy P Chait, Katrin Schneider, François Signorino Gelo, Stanislas Leibler, and John Mckinney. “Dynamic Persistence of Antibiotic-Stressed Mycobacteria.” Science. American Association for the Advancement of Science, 2013. https://doi.org/10.1126/science.1229858. ieee: Y. Wakamoto et al., “Dynamic persistence of antibiotic-stressed mycobacteria,” Science, vol. 339, no. 6115. American Association for the Advancement of Science, pp. 91–95, 2013. 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. mla: Wakamoto, Yurichi, et al. “Dynamic Persistence of Antibiotic-Stressed Mycobacteria.” Science, vol. 339, no. 6115, American Association for the Advancement of Science, 2013, pp. 91–95, doi:10.1126/science.1229858. short: Y. Wakamoto, N. Dhar, R.P. Chait, K. Schneider, F. Signorino Gelo, S. Leibler, J. Mckinney, Science 339 (2013) 91–95. date_created: 2018-12-11T11:46:48Z date_published: 2013-01-04T00:00:00Z date_updated: 2021-01-12T08:01:06Z day: '04' department: - _id: CaGu - _id: GaTk doi: 10.1126/science.1229858 intvolume: ' 339' issue: '6115' language: - iso: eng month: '01' oa_version: None page: 91 - 95 publication: Science publication_status: published publisher: American Association for the Advancement of Science publist_id: '7321' quality_controlled: '1' scopus_import: 1 status: public title: Dynamic persistence of antibiotic-stressed mycobacteria type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 339 year: '2013' ... --- _id: '2277' 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. article_number: e1003344 author: - first_name: Kristina full_name: Simmons, Kristina last_name: Simmons - first_name: Jason full_name: Prentice, Jason last_name: Prentice - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Jan full_name: Homann, Jan last_name: Homann - first_name: Heather full_name: Yee, Heather last_name: Yee - first_name: Stephanie full_name: Palmer, Stephanie last_name: Palmer - first_name: Philip full_name: Nelson, Philip last_name: Nelson - first_name: Vijay full_name: Balasubramanian, Vijay last_name: Balasubramanian citation: ama: Simmons K, Prentice J, Tkačik G, et al. Transformation of stimulus correlations by the retina. PLoS Computational Biology. 2013;9(12). doi:10.1371/journal.pcbi.1003344 apa: Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., … Balasubramanian, V. (2013). Transformation of stimulus correlations by the retina. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1003344 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.” PLoS Computational Biology. Public Library of Science, 2013. https://doi.org/10.1371/journal.pcbi.1003344. ieee: K. Simmons et al., “Transformation of stimulus correlations by the retina,” PLoS Computational Biology, vol. 9, no. 12. Public Library of Science, 2013. 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. mla: Simmons, Kristina, et al. “Transformation of Stimulus Correlations by the Retina.” PLoS Computational Biology, vol. 9, no. 12, e1003344, Public Library of Science, 2013, doi:10.1371/journal.pcbi.1003344. short: K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson, V. Balasubramanian, PLoS Computational Biology 9 (2013). date_created: 2018-12-11T11:56:43Z date_published: 2013-12-05T00:00:00Z date_updated: 2023-02-23T14:07:04Z day: '05' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1003344 file: - access_level: open_access checksum: 46722afc4f7eabb0831165d9c1b171ad content_type: application/pdf creator: system date_created: 2018-12-12T10:14:36Z date_updated: 2020-07-14T12:45:36Z file_id: '5089' file_name: IST-2016-410-v1+1_journal.pcbi.1003344.pdf file_size: 3115568 relation: main_file file_date_updated: 2020-07-14T12:45:36Z has_accepted_license: '1' intvolume: ' 9' issue: '12' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_status: published publisher: Public Library of Science publist_id: '4667' pubrep_id: '410' quality_controlled: '1' related_material: record: - id: '9752' relation: research_data status: public scopus_import: 1 status: public title: Transformation of stimulus correlations by the retina tmp: 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) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 9 year: '2013' ... --- _id: '2914' 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 "surprising" 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. 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" article_number: '018701' article_processing_charge: No article_type: original author: - first_name: Greg full_name: Stephens, Greg last_name: Stephens - first_name: Thierry full_name: Mora, Thierry last_name: Mora - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: William full_name: Bialek, William last_name: Bialek citation: ama: Stephens G, Mora T, Tkačik G, Bialek W. Statistical thermodynamics of natural images. Physical Review Letters. 2013;110(1). doi:10.1103/PhysRevLett.110.018701 apa: Stephens, G., Mora, T., Tkačik, G., & Bialek, W. (2013). Statistical thermodynamics of natural images. Physical Review Letters. American Physical Society. https://doi.org/10.1103/PhysRevLett.110.018701 chicago: Stephens, Greg, Thierry Mora, Gašper Tkačik, and William Bialek. “Statistical Thermodynamics of Natural Images.” Physical Review Letters. American Physical Society, 2013. https://doi.org/10.1103/PhysRevLett.110.018701. ieee: G. Stephens, T. Mora, G. Tkačik, and W. Bialek, “Statistical thermodynamics of natural images,” Physical Review Letters, vol. 110, no. 1. American Physical Society, 2013. ista: Stephens G, Mora T, Tkačik G, Bialek W. 2013. Statistical thermodynamics of natural images. Physical Review Letters. 110(1), 018701. mla: Stephens, Greg, et al. “Statistical Thermodynamics of Natural Images.” Physical Review Letters, vol. 110, no. 1, 018701, American Physical Society, 2013, doi:10.1103/PhysRevLett.110.018701. short: G. Stephens, T. Mora, G. Tkačik, W. Bialek, Physical Review Letters 110 (2013). date_created: 2018-12-11T12:00:19Z date_published: 2013-01-02T00:00:00Z date_updated: 2023-09-04T11:47:51Z day: '02' ddc: - '530' department: - _id: GaTk doi: 10.1103/PhysRevLett.110.018701 external_id: arxiv: - '0806.2694' file: - access_level: open_access checksum: 72bfbc2094c4680e8a8a6bed668cd06d content_type: application/pdf creator: system date_created: 2018-12-12T10:18:44Z date_updated: 2020-07-14T12:45:53Z file_id: '5366' file_name: IST-2016-401-v1+1_1281.full.pdf file_size: 416965 relation: main_file file_date_updated: 2020-07-14T12:45:53Z has_accepted_license: '1' intvolume: ' 110' issue: '1' language: - iso: eng month: '01' oa: 1 oa_version: Published Version publication: Physical Review Letters publication_status: published publisher: American Physical Society publist_id: '3829' pubrep_id: '401' quality_controlled: '1' status: public title: Statistical thermodynamics of natural images tmp: 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) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 110 year: '2013' ... --- _id: '3262' abstract: - lang: eng text: Living cells must control the reading out or "expression" 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. 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." article_number: '041903' author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Aleksandra full_name: Walczak, Aleksandra last_name: Walczak - first_name: William full_name: Bialek, William last_name: Bialek citation: ama: Tkačik G, Walczak A, Bialek W. Optimizing information flow in small genetic networks. III. A self-interacting gene. Physical Review E statistical nonlinear and soft matter physics . 2012;85(4). doi:10.1103/PhysRevE.85.041903 apa: Tkačik, G., Walczak, A., & Bialek, W. (2012). Optimizing information flow in small genetic networks. III. A self-interacting gene. Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.85.041903 chicago: Tkačik, Gašper, Aleksandra Walczak, and William Bialek. “Optimizing Information Flow in Small Genetic Networks. III. A Self-Interacting Gene.” Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics, 2012. https://doi.org/10.1103/PhysRevE.85.041903. ieee: G. Tkačik, A. Walczak, and W. Bialek, “Optimizing information flow in small genetic networks. III. A self-interacting gene,” Physical Review E statistical nonlinear and soft matter physics , vol. 85, no. 4. American Institute of Physics, 2012. ista: Tkačik G, Walczak A, Bialek W. 2012. Optimizing information flow in small genetic networks. III. A self-interacting gene. Physical Review E statistical nonlinear and soft matter physics . 85(4), 041903. mla: Tkačik, Gašper, et al. “Optimizing Information Flow in Small Genetic Networks. III. A Self-Interacting Gene.” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 85, no. 4, 041903, American Institute of Physics, 2012, doi:10.1103/PhysRevE.85.041903. short: G. Tkačik, A. Walczak, W. Bialek, Physical Review E Statistical Nonlinear and Soft Matter Physics 85 (2012). date_created: 2018-12-11T12:02:20Z date_published: 2012-04-01T00:00:00Z date_updated: 2021-01-12T07:42:14Z day: '01' department: - _id: GaTk doi: 10.1103/PhysRevE.85.041903 intvolume: ' 85' issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1112.5026 month: '04' oa: 1 oa_version: Preprint publication: ' Physical Review E statistical nonlinear and soft matter physics ' publication_status: published publisher: American Institute of Physics publist_id: '3386' quality_controlled: '1' scopus_import: 1 status: public title: Optimizing information flow in small genetic networks. III. A self-interacting gene type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 85 year: '2012' ... --- _id: '3274' abstract: - lang: eng 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. 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. author: - first_name: Georg full_name: Rieckh, Georg id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87 last_name: Rieckh - first_name: Wolfgang full_name: Kreuzer, Wolfgang last_name: Kreuzer - first_name: Holger full_name: Waubke, Holger last_name: Waubke - first_name: Peter full_name: Balazs, Peter last_name: Balazs citation: 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. Engineering Analysis with Boundary Elements. 2012;36(6):960-967. doi:10.1016/j.enganabound.2011.12.014 apa: 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. Elsevier. https://doi.org/10.1016/j.enganabound.2011.12.014 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.” Engineering Analysis with Boundary Elements. Elsevier, 2012. https://doi.org/10.1016/j.enganabound.2011.12.014. 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,” Engineering Analysis with Boundary Elements, vol. 36, no. 6. Elsevier, pp. 960–967, 2012. 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. mla: Rieckh, Georg, et al. “A 2.5D-Fourier-BEM Model for Vibrations in a Tunnel Running through Layered Anisotropic Soil.” Engineering Analysis with Boundary Elements, vol. 36, no. 6, Elsevier, 2012, pp. 960–67, doi:10.1016/j.enganabound.2011.12.014. short: G. Rieckh, W. Kreuzer, H. Waubke, P. Balazs, Engineering Analysis with Boundary Elements 36 (2012) 960–967. date_created: 2018-12-11T12:02:24Z date_published: 2012-06-01T00:00:00Z date_updated: 2021-01-12T07:42:19Z day: '01' department: - _id: GaTk doi: 10.1016/j.enganabound.2011.12.014 intvolume: ' 36' issue: '6' language: - iso: eng month: '06' oa_version: None page: 960 - 967 publication: ' Engineering Analysis with Boundary Elements' publication_status: published publisher: Elsevier publist_id: '3372' quality_controlled: '1' scopus_import: 1 status: public title: A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 36 year: '2012' ... --- _id: '3374' abstract: - lang: eng 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'. article_number: '153102' author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Aleksandra full_name: Walczak, Aleksandra last_name: Walczak citation: ama: 'Tkačik G, Walczak A. Information transmission in genetic regulatory networks a review. Journal of Physics: Condensed Matter. 2011;23(15). doi:10.1088/0953-8984/23/15/153102' apa: 'Tkačik, G., & Walczak, A. (2011). Information transmission in genetic regulatory networks a review. Journal of Physics: Condensed Matter. IOP Publishing Ltd. https://doi.org/10.1088/0953-8984/23/15/153102' chicago: 'Tkačik, Gašper, and Aleksandra Walczak. “Information Transmission in Genetic Regulatory Networks a Review.” Journal of Physics: Condensed Matter. IOP Publishing Ltd., 2011. https://doi.org/10.1088/0953-8984/23/15/153102.' ieee: 'G. Tkačik and A. Walczak, “Information transmission in genetic regulatory networks a review,” Journal of Physics: Condensed Matter, vol. 23, no. 15. IOP Publishing Ltd., 2011.' ista: 'Tkačik G, Walczak A. 2011. Information transmission in genetic regulatory networks a review. Journal of Physics: Condensed Matter. 23(15), 153102.' mla: 'Tkačik, Gašper, and Aleksandra Walczak. “Information Transmission in Genetic Regulatory Networks a Review.” Journal of Physics: Condensed Matter, vol. 23, no. 15, 153102, IOP Publishing Ltd., 2011, doi:10.1088/0953-8984/23/15/153102.' short: 'G. Tkačik, A. Walczak, Journal of Physics: Condensed Matter 23 (2011).' date_created: 2018-12-11T12:02:58Z date_published: 2011-04-01T00:00:00Z date_updated: 2021-01-12T07:43:03Z day: '01' department: - _id: GaTk doi: 10.1088/0953-8984/23/15/153102 intvolume: ' 23' issue: '15' language: - iso: eng main_file_link: - open_access: '1' url: http://arxiv.org/abs/1101.4240 month: '04' oa: 1 oa_version: Submitted Version publication: 'Journal of Physics: Condensed Matter' publication_status: published publisher: IOP Publishing Ltd. publist_id: '3233' quality_controlled: '1' scopus_import: 1 status: public title: Information transmission in genetic regulatory networks a review type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 23 year: '2011' ... --- _id: '3384' abstract: - lang: eng 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. article_number: e20409 author: - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Patrick full_name: Garrigan, Patrick last_name: Garrigan - first_name: Charles full_name: Ratliff, Charles last_name: Ratliff - first_name: Grega full_name: Milcinski, Grega last_name: Milcinski - first_name: Jennifer full_name: Klein, Jennifer last_name: Klein - first_name: Lucia full_name: Seyfarth, Lucia last_name: Seyfarth - first_name: Peter full_name: Sterling, Peter last_name: Sterling - first_name: David full_name: Brainard, David last_name: Brainard - first_name: Vijay full_name: Balasubramanian, Vijay last_name: Balasubramanian citation: ama: Tkačik G, Garrigan P, Ratliff C, et al. Natural images from the birthplace of the human eye. PLoS One. 2011;6(6). doi:10.1371/journal.pone.0020409 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. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0020409 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.” PLoS One. Public Library of Science, 2011. https://doi.org/10.1371/journal.pone.0020409. ieee: G. Tkačik et al., “Natural images from the birthplace of the human eye,” PLoS One, vol. 6, no. 6. Public Library of Science, 2011. 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. mla: Tkačik, Gašper, et al. “Natural Images from the Birthplace of the Human Eye.” PLoS One, vol. 6, no. 6, e20409, Public Library of Science, 2011, doi:10.1371/journal.pone.0020409. short: G. Tkačik, P. Garrigan, C. Ratliff, G. Milcinski, J. Klein, L. Seyfarth, P. Sterling, D. Brainard, V. Balasubramanian, PLoS One 6 (2011). date_created: 2018-12-11T12:03:01Z date_published: 2011-06-16T00:00:00Z date_updated: 2021-01-12T07:43:07Z day: '16' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pone.0020409 file: - access_level: open_access checksum: 307d4356916471306e3705ac65b82fa1 content_type: application/pdf creator: system date_created: 2018-12-12T10:09:25Z date_updated: 2020-07-14T12:46:11Z file_id: '4749' file_name: IST-2015-379-v1+1_journal.pone.0020409.pdf file_size: 1424768 relation: main_file file_date_updated: 2020-07-14T12:46:11Z has_accepted_license: '1' intvolume: ' 6' issue: '6' language: - iso: eng month: '06' oa: 1 oa_version: Published Version publication: PLoS One publication_status: published publisher: Public Library of Science publist_id: '3223' pubrep_id: '379' quality_controlled: '1' scopus_import: 1 status: public title: Natural images from the birthplace of the human eye tmp: 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) short: CC BY (4.0) type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 6 year: '2011' ...