[{"title":"The simplest maximum entropy model for collective behavior in a neural network","status":"public","intvolume":" 2013","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"2850","oa_version":"Preprint","type":"journal_article","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"}],"issue":"3","article_type":"original","publication":"Journal of Statistical Mechanics Theory and Experiment","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","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.","short":"G. Tkačik, O. Marre, T. Mora, D. Amodei, M. Berry, W. Bialek, Journal of Statistical Mechanics Theory and Experiment 2013 (2013).","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.","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."},"date_published":"2013-03-12T00:00:00Z","scopus_import":1,"day":"12","article_processing_charge":"No","publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"IOP Publishing Ltd.","year":"2013","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","date_updated":"2021-01-12T07:00:14Z","date_created":"2018-12-11T11:59:55Z","volume":2013,"author":[{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"full_name":"Mora, Thierry","first_name":"Thierry","last_name":"Mora"},{"first_name":"Dario","last_name":"Amodei","full_name":"Amodei, Dario"},{"last_name":"Berry","first_name":"Michael","full_name":"Berry, Michael"},{"full_name":"Bialek, William","first_name":"William","last_name":"Bialek"}],"article_number":"P03011","publist_id":"3942","quality_controlled":"1","external_id":{"arxiv":["1207.6319"]},"oa":1,"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1207.6319"}],"language":[{"iso":"eng"}],"doi":"10.1088/1742-5468/2013/03/P03011","month":"03"},{"day":"12","month":"03","scopus_import":1,"language":[{"iso":"eng"}],"date_published":"2013-03-12T00:00:00Z","doi":"10.1088/1742-5468/2013/03/P03015","quality_controlled":"1","citation":{"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., & 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","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.","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","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.","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)."},"publication":"Journal of Statistical Mechanics Theory and Experiment","issue":"3","publist_id":"3941","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."}],"type":"journal_article","article_number":"P03015","oa_version":"None","volume":2013,"date_updated":"2021-01-12T07:00:14Z","date_created":"2018-12-11T11:59:56Z","author":[{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"},{"last_name":"Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"},{"last_name":"Dubuis","first_name":"Julien","full_name":"Dubuis, Julien"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"full_name":"Da Silveira, Ravá","last_name":"Da Silveira","first_name":"Ravá"}],"department":[{"_id":"GaTk"}],"intvolume":" 2013","publisher":"IOP Publishing Ltd.","title":"A simple method for estimating the entropy of neural activity","publication_status":"published","status":"public","_id":"2851","year":"2013","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"status":"public","ddc":["570"],"title":"Optical control of ligand-gated ion channels","intvolume":" 998","_id":"2857","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"checksum":"1701f0d989f27ddac471b19a894ec0d1","date_updated":"2020-07-14T12:45:51Z","date_created":"2018-12-12T10:12:34Z","relation":"main_file","file_id":"4952","file_size":336734,"content_type":"application/pdf","creator":"system","access_level":"open_access","file_name":"IST-2017-834-v1+1_szobota.pdf"}],"oa_version":"Submitted Version","pubrep_id":"834","alternative_title":["MIMB"],"type":"journal_article","abstract":[{"lang":"eng","text":"In the vibrant field of optogenetics, optics and genetic targeting are combined to commandeer cellular functions, such as the neuronal action potential, by optically stimulating light-sensitive ion channels expressed in the cell membrane. One broadly applicable manifestation of this approach are covalently attached photochromic tethered ligands (PTLs) that allow activating ligand-gated ion channels with outstanding spatial and temporal resolution. Here, we describe all steps towards the successful development and application of PTL-gated ion channels in cell lines and primary cells. The basis for these experiments forms a combination of molecular modeling, genetic engineering, cell culture, and electrophysiology. The light-gated glutamate receptor (LiGluR), which consists of the PTL-functionalized GluK2 receptor, serves as a model."}],"page":"417 - 435","publication":"Methods in Molecular Biology","citation":{"short":"S. Szobota, C. Mckenzie, H.L. Janovjak, Methods in Molecular Biology 998 (2013) 417–435.","mla":"Szobota, Stephanie, et al. “Optical Control of Ligand-Gated Ion Channels.” Methods in Molecular Biology, vol. 998, Springer, 2013, pp. 417–35, doi:10.1007/978-1-62703-351-0_32.","chicago":"Szobota, Stephanie, Catherine Mckenzie, and Harald L Janovjak. “Optical Control of Ligand-Gated Ion Channels.” Methods in Molecular Biology. Springer, 2013. https://doi.org/10.1007/978-1-62703-351-0_32.","ama":"Szobota S, Mckenzie C, Janovjak HL. Optical control of ligand-gated ion channels. Methods in Molecular Biology. 2013;998:417-435. doi:10.1007/978-1-62703-351-0_32","ieee":"S. Szobota, C. Mckenzie, and H. L. Janovjak, “Optical control of ligand-gated ion channels,” Methods in Molecular Biology, vol. 998. Springer, pp. 417–435, 2013.","apa":"Szobota, S., Mckenzie, C., & Janovjak, H. L. (2013). Optical control of ligand-gated ion channels. Methods in Molecular Biology. Springer. https://doi.org/10.1007/978-1-62703-351-0_32","ista":"Szobota S, Mckenzie C, Janovjak HL. 2013. Optical control of ligand-gated ion channels. Methods in Molecular Biology. 998, 417–435."},"date_published":"2013-02-22T00:00:00Z","scopus_import":1,"day":"22","has_accepted_license":"1","publication_status":"published","department":[{"_id":"HaJa"}],"publisher":"Springer","year":"2013","date_created":"2018-12-11T11:59:57Z","date_updated":"2021-01-12T07:00:17Z","volume":998,"author":[{"first_name":"Stephanie","last_name":"Szobota","full_name":"Szobota, Stephanie"},{"last_name":"Mckenzie","first_name":"Catherine","id":"3EEDE19A-F248-11E8-B48F-1D18A9856A87","full_name":"Mckenzie, Catherine"},{"full_name":"Janovjak, Harald L","orcid":"0000-0002-8023-9315","id":"33BA6C30-F248-11E8-B48F-1D18A9856A87","last_name":"Janovjak","first_name":"Harald L"}],"file_date_updated":"2020-07-14T12:45:51Z","ec_funded":1,"publist_id":"3932","quality_controlled":"1","project":[{"grant_number":"RGY0084/2012","_id":"255BFFFA-B435-11E9-9278-68D0E5697425","name":"In situ real-time imaging of neurotransmitter signaling using designer optical sensors (HFSP Young Investigator)"},{"_id":"25548C20-B435-11E9-9278-68D0E5697425","grant_number":"303564","name":"Microbial Ion Channels for Synthetic Neurobiology","call_identifier":"FP7"}],"oa":1,"language":[{"iso":"eng"}],"doi":"10.1007/978-1-62703-351-0_32","month":"02"},{"citation":{"chicago":"Dupret, David, Joseph O’Neill, and Jozsef L Csicsvari. “Dynamic Reconfiguration of Hippocampal Interneuron Circuits during Spatial Learning.” Neuron. Elsevier, 2013. https://doi.org/10.1016/j.neuron.2013.01.033.","mla":"Dupret, David, et al. “Dynamic Reconfiguration of Hippocampal Interneuron Circuits during Spatial Learning.” Neuron, vol. 78, no. 1, Elsevier, 2013, pp. 166–80, doi:10.1016/j.neuron.2013.01.033.","short":"D. Dupret, J. O’Neill, J.L. Csicsvari, Neuron 78 (2013) 166–180.","ista":"Dupret D, O’Neill J, Csicsvari JL. 2013. Dynamic reconfiguration of hippocampal interneuron circuits during spatial learning. Neuron. 78(1), 166–180.","ieee":"D. Dupret, J. O’Neill, and J. L. Csicsvari, “Dynamic reconfiguration of hippocampal interneuron circuits during spatial learning,” Neuron, vol. 78, no. 1. Elsevier, pp. 166–180, 2013.","apa":"Dupret, D., O’Neill, J., & Csicsvari, J. L. (2013). Dynamic reconfiguration of hippocampal interneuron circuits during spatial learning. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2013.01.033","ama":"Dupret D, O’Neill J, Csicsvari JL. Dynamic reconfiguration of hippocampal interneuron circuits during spatial learning. Neuron. 2013;78(1):166-180. doi:10.1016/j.neuron.2013.01.033"},"publication":"Neuron","page":"166 - 180","date_published":"2013-03-21T00:00:00Z","scopus_import":1,"has_accepted_license":"1","day":"21","_id":"2860","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 78","title":"Dynamic reconfiguration of hippocampal interneuron circuits during spatial learning","ddc":["570"],"status":"public","oa_version":"Published Version","file":[{"creator":"dernst","file_size":2637837,"content_type":"application/pdf","access_level":"open_access","file_name":"2013_Neuron_Dupret.pdf","checksum":"0e18cb8561153ddb50bb5af16e7c9e97","date_created":"2019-01-23T08:08:07Z","date_updated":"2020-07-14T12:45:52Z","file_id":"5877","relation":"main_file"}],"type":"journal_article","issue":"1","abstract":[{"text":"In the hippocampus, cell assemblies forming mnemonic representations of space are thought to arise as a result of changes in functional connections of pyramidal cells. We have found that CA1 interneuron circuits are also reconfigured during goal-oriented spatial learning through modification of inputs from pyramidal cells. As learning progressed, new pyramidal assemblies expressed in theta cycles alternated with previously established ones, and eventually overtook them. The firing patterns of interneurons developed a relationship to new, learning-related assemblies: some interneurons associated their activity with new pyramidal assemblies while some others dissociated from them. These firing associations were explained by changes in the weight of monosynaptic inputs received by interneurons from new pyramidal assemblies, as these predicted the associational changes. Spatial learning thus engages circuit modifications in the hippocampus that incorporate a redistribution of inhibitory activity that might assist in the segregation of competing pyramidal cell assembly patterns in space and time.","lang":"eng"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"project":[{"grant_number":"281511","_id":"257A4776-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex"}],"quality_controlled":"1","doi":"10.1016/j.neuron.2013.01.033","language":[{"iso":"eng"}],"month":"03","year":"2013","acknowledgement":"D.D. and J.C. were supported by a MRC Intramural Programme Grant U138197111","publisher":"Elsevier","department":[{"_id":"JoCs"}],"publication_status":"published","author":[{"first_name":"David","last_name":"Dupret","full_name":"Dupret, David"},{"full_name":"O'Neill, Joseph","last_name":"O'Neill","first_name":"Joseph","id":"426376DC-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Csicsvari, Jozsef L","orcid":"0000-0002-5193-4036","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","last_name":"Csicsvari","first_name":"Jozsef L"}],"volume":78,"date_created":"2018-12-11T11:59:59Z","date_updated":"2021-01-12T07:00:19Z","publist_id":"3929","ec_funded":1,"file_date_updated":"2020-07-14T12:45:52Z","license":"https://creativecommons.org/licenses/by/4.0/"},{"scopus_import":1,"day":"28","has_accepted_license":"1","publication":"Cell Reports","citation":{"chicago":"Hippenmeyer, Simon, Randy Johnson, and Liqun Luo. “Mosaic Analysis with Double Markers Reveals Cell Type Specific Paternal Growth Dominance.” Cell Reports. Cell Press, 2013. https://doi.org/10.1016/j.celrep.2013.02.002.","short":"S. Hippenmeyer, R. Johnson, L. Luo, Cell Reports 3 (2013) 960–967.","mla":"Hippenmeyer, Simon, et al. “Mosaic Analysis with Double Markers Reveals Cell Type Specific Paternal Growth Dominance.” Cell Reports, vol. 3, no. 3, Cell Press, 2013, pp. 960–67, doi:10.1016/j.celrep.2013.02.002.","ieee":"S. Hippenmeyer, R. Johnson, and L. Luo, “Mosaic analysis with double markers reveals cell type specific paternal growth dominance,” Cell Reports, vol. 3, no. 3. Cell Press, pp. 960–967, 2013.","apa":"Hippenmeyer, S., Johnson, R., & Luo, L. (2013). Mosaic analysis with double markers reveals cell type specific paternal growth dominance. Cell Reports. Cell Press. https://doi.org/10.1016/j.celrep.2013.02.002","ista":"Hippenmeyer S, Johnson R, Luo L. 2013. Mosaic analysis with double markers reveals cell type specific paternal growth dominance. Cell Reports. 3(3), 960–967.","ama":"Hippenmeyer S, Johnson R, Luo L. Mosaic analysis with double markers reveals cell type specific paternal growth dominance. Cell Reports. 2013;3(3):960-967. doi:10.1016/j.celrep.2013.02.002"},"page":"960 - 967","date_published":"2013-03-28T00:00:00Z","type":"journal_article","abstract":[{"text":"Genomic imprinting leads to preferred expression of either the maternal or paternal alleles of a subset of genes. Imprinting is essential for mammalian development, and its deregulation causes many diseases. However, the functional relevance of imprinting at the cellular level is poorly understood for most imprinted genes. We used mosaic analysis with double markers (MADM) in mice to create uniparental disomies (UPDs) and to visualize imprinting effects with single-cell resolution. Although chromosome 12 UPD did not produce detectable phenotypes, chromosome 7 UPD caused highly significant paternal growth dominance in the liver and lung, but not in the brain or heart. A single gene on chromosome 7, encoding the secreted insulin-like growth factor 2 (IGF2), accounts for most of the paternal dominance effect. Mosaic analyses implied additional imprinted loci on chromosome 7 acting cell autonomously to transmit the IGF2 signal. Our study reveals chromosome- and cell-type specificity of genomic imprinting effects.","lang":"eng"}],"issue":"3","_id":"2855","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Mosaic analysis with double markers reveals cell type specific paternal growth dominance","ddc":["570"],"status":"public","intvolume":" 3","pubrep_id":"405","file":[{"checksum":"6e977b918e81384cd571ec5a9d812289","date_updated":"2020-07-14T12:45:51Z","date_created":"2018-12-12T10:17:20Z","file_id":"5274","relation":"main_file","creator":"system","file_size":1907211,"content_type":"application/pdf","access_level":"open_access","file_name":"IST-2016-405-v1+1_1-s2.0-S2211124713000612-main.pdf"}],"oa_version":"Published Version","month":"03","oa":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","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"},"quality_controlled":"1","doi":"10.1016/j.celrep.2013.02.002","language":[{"iso":"eng"}],"file_date_updated":"2020-07-14T12:45:51Z","publist_id":"3937","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","year":"2013","publication_status":"published","publisher":"Cell Press","department":[{"_id":"SiHi"}],"author":[{"id":"37B36620-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2279-1061","first_name":"Simon","last_name":"Hippenmeyer","full_name":"Hippenmeyer, Simon"},{"first_name":"Randy","last_name":"Johnson","full_name":"Johnson, Randy"},{"first_name":"Liqun","last_name":"Luo","full_name":"Luo, Liqun"}],"date_created":"2018-12-11T11:59:57Z","date_updated":"2021-01-12T07:00:16Z","volume":3}]