[{"acknowledgement":"We thank Bela Mulder, Tom Shimizu, Fotios Avgidis, Peter Bolhuis, and Daan Frenkel for useful discussions and a careful reading of the manuscript, and we thank Age Tjalma for support with obtaining the Gaussian approximation of the chemotaxis system. This work is part of the Dutch Research Council (NWO) and was performed at the research institute AMOLF. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 885065) and was\r\nfinancially supported by NWO through the “Building a Synthetic Cell (BaSyC)” Gravitation Grant (024.003.019).","date_published":"2023-10-26T00:00:00Z","publication":"Physical Review X","status":"public","external_id":{"arxiv":["2203.03461"]},"year":"2023","quality_controlled":"1","ddc":["530"],"type":"journal_article","_id":"14515","date_updated":"2023-11-13T09:03:30Z","publisher":"American Physical Society","article_processing_charge":"Yes","doi":"10.1103/PhysRevX.13.041017","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Reinhardt, Manuel, Gašper Tkačik, and Pieter Rein Ten Wolde. “Path Weight Sampling: Exact Monte Carlo Computation of the Mutual Information between Stochastic Trajectories.” <i>Physical Review X</i>. American Physical Society, 2023. <a href=\"https://doi.org/10.1103/PhysRevX.13.041017\">https://doi.org/10.1103/PhysRevX.13.041017</a>.","ista":"Reinhardt M, Tkačik G, Ten Wolde PR. 2023. Path weight sampling: Exact Monte Carlo computation of the mutual information between stochastic trajectories. Physical Review X. 13(4), 041017.","apa":"Reinhardt, M., Tkačik, G., &#38; Ten Wolde, P. R. (2023). Path weight sampling: Exact Monte Carlo computation of the mutual information between stochastic trajectories. <i>Physical Review X</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevX.13.041017\">https://doi.org/10.1103/PhysRevX.13.041017</a>","mla":"Reinhardt, Manuel, et al. “Path Weight Sampling: Exact Monte Carlo Computation of the Mutual Information between Stochastic Trajectories.” <i>Physical Review X</i>, vol. 13, no. 4, 041017, American Physical Society, 2023, doi:<a href=\"https://doi.org/10.1103/PhysRevX.13.041017\">10.1103/PhysRevX.13.041017</a>.","ama":"Reinhardt M, Tkačik G, Ten Wolde PR. Path weight sampling: Exact Monte Carlo computation of the mutual information between stochastic trajectories. <i>Physical Review X</i>. 2023;13(4). doi:<a href=\"https://doi.org/10.1103/PhysRevX.13.041017\">10.1103/PhysRevX.13.041017</a>","ieee":"M. Reinhardt, G. Tkačik, and P. R. Ten Wolde, “Path weight sampling: Exact Monte Carlo computation of the mutual information between stochastic trajectories,” <i>Physical Review X</i>, vol. 13, no. 4. American Physical Society, 2023.","short":"M. Reinhardt, G. Tkačik, P.R. Ten Wolde, Physical Review X 13 (2023)."},"issue":"4","language":[{"iso":"eng"}],"oa":1,"file":[{"date_updated":"2023-11-13T09:00:19Z","creator":"dernst","date_created":"2023-11-13T09:00:19Z","file_size":1595223,"file_id":"14522","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2023_PhysReviewX_Reinhardt.pdf","checksum":"32574aeebcca7347a4152c611b66b3d5","relation":"main_file"}],"article_number":"041017","department":[{"_id":"GaTk"}],"arxiv":1,"month":"10","file_date_updated":"2023-11-13T09:00:19Z","publication_identifier":{"eissn":["2160-3308"]},"publication_status":"published","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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"license":"https://creativecommons.org/licenses/by/4.0/","intvolume":"        13","abstract":[{"text":"Most natural and engineered information-processing systems transmit information via signals that vary in time. Computing the information transmission rate or the information encoded in the temporal characteristics of these signals requires the mutual information between the input and output signals as a function of time, i.e., between the input and output trajectories. Yet, this is notoriously difficult because of the high-dimensional nature of the trajectory space, and all existing techniques require approximations. We present an exact Monte Carlo technique called path weight sampling (PWS) that, for the first time, makes it possible to compute the mutual information between input and output trajectories for any stochastic system that is described by a master equation. The principal idea is to use the master equation to evaluate the exact conditional probability of an individual output trajectory for a given input trajectory and average this via Monte Carlo sampling in trajectory space to obtain the mutual information. We present three variants of PWS, which all generate the trajectories using the standard stochastic simulation algorithm. While direct PWS is a brute-force method, Rosenbluth-Rosenbluth PWS exploits the analogy between signal trajectory sampling and polymer sampling, and thermodynamic integration PWS is based on a reversible work calculation in trajectory space. PWS also makes it possible to compute the mutual information between input and output trajectories for systems with hidden internal states as well as systems with feedback from output to input. Applying PWS to the bacterial chemotaxis system, consisting of 182 coupled chemical reactions, demonstrates not only that the scheme is highly efficient but also that the number of receptor clusters is much smaller than hitherto believed, while their size is much larger.","lang":"eng"}],"has_accepted_license":"1","date_created":"2023-11-12T23:00:55Z","article_type":"original","volume":13,"oa_version":"Published Version","title":"Path weight sampling: Exact Monte Carlo computation of the mutual information between stochastic trajectories","scopus_import":"1","day":"26","author":[{"full_name":"Reinhardt, Manuel","last_name":"Reinhardt","first_name":"Manuel"},{"last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"full_name":"Ten Wolde, Pieter Rein","last_name":"Ten Wolde","first_name":"Pieter Rein"}]},{"language":[{"iso":"eng"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal CA1 interactions optimizes spatial coding across experience,” <i>The Journal of Neuroscience</i>, vol. 43, no. 48. Society of Neuroscience, pp. 8140–8156, 2023.","short":"M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, The Journal of Neuroscience 43 (2023) 8140–8156.","ama":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>The Journal of Neuroscience</i>. 2023;43(48):8140-8156. doi:<a href=\"https://doi.org/10.1523/JNEUROSCI.0194-23.2023\">10.1523/JNEUROSCI.0194-23.2023</a>","apa":"Nardin, M., Csicsvari, J. L., Tkačik, G., &#38; Savin, C. (2023). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>The Journal of Neuroscience</i>. Society of Neuroscience. <a href=\"https://doi.org/10.1523/JNEUROSCI.0194-23.2023\">https://doi.org/10.1523/JNEUROSCI.0194-23.2023</a>","mla":"Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>The Journal of Neuroscience</i>, vol. 43, no. 48, Society of Neuroscience, 2023, pp. 8140–56, doi:<a href=\"https://doi.org/10.1523/JNEUROSCI.0194-23.2023\">10.1523/JNEUROSCI.0194-23.2023</a>.","chicago":"Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>The Journal of Neuroscience</i>. Society of Neuroscience, 2023. <a href=\"https://doi.org/10.1523/JNEUROSCI.0194-23.2023\">https://doi.org/10.1523/JNEUROSCI.0194-23.2023</a>.","ista":"Nardin M, Csicsvari JL, Tkačik G, Savin C. 2023. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. The Journal of Neuroscience. 43(48), 8140–8156."},"issue":"48","month":"11","file":[{"date_updated":"2023-12-11T11:30:37Z","embargo":"2024-06-01","creator":"dernst","date_created":"2023-12-11T11:30:37Z","file_size":2280632,"embargo_to":"open_access","file_id":"14674","content_type":"application/pdf","access_level":"closed","file_name":"2023_JourNeuroscience_Nardin.pdf","checksum":"e2503c8f84be1050e28f64320f1d5bd2","relation":"main_file"}],"department":[{"_id":"JoCs"},{"_id":"GaTk"}],"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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        43","abstract":[{"lang":"eng","text":"Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain."}],"has_accepted_license":"1","file_date_updated":"2023-12-11T11:30:37Z","publication_identifier":{"eissn":["1529-2401"]},"publication_status":"published","oa_version":"Published Version","title":"The structure of hippocampal CA1 interactions optimizes spatial coding across experience","day":"29","scopus_import":"1","author":[{"orcid":"0000-0001-8849-6570","first_name":"Michele","full_name":"Nardin, Michele","id":"30BD0376-F248-11E8-B48F-1D18A9856A87","last_name":"Nardin"},{"orcid":"0000-0002-5193-4036","first_name":"Jozsef L","last_name":"Csicsvari","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","full_name":"Csicsvari, Jozsef L"},{"first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"}],"date_created":"2023-12-10T23:00:58Z","article_type":"original","volume":43,"publication":"The Journal of Neuroscience","status":"public","project":[{"grant_number":"281511","name":"Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex","call_identifier":"FP7","_id":"257A4776-B435-11E9-9278-68D0E5697425"},{"_id":"626c45b5-2b32-11ec-9570-e509828c1ba6","name":"Efficient coding with biophysical realism","grant_number":"P34015"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385","name":"International IST Doctoral Program","call_identifier":"H2020"}],"date_published":"2023-11-29T00:00:00Z","acknowledgement":"M.N. was supported by the European Union Horizon 2020 Grant 665385. J.C. was supported by the European Research Council Consolidator Grant 281511. G.T. was supported by the Austrian Science Fund (FWF) Grant P34015. C.S. was supported by an Institute of Science and Technology fellow award and by the National Science Foundation (NSF) Award No. 1922658. We thank Peter Baracskay, Karola Kaefer, and Hugo Malagon-Vina for the acquisition of the data. We also thank Federico Stella, Wiktor Młynarski, Dori Derdikman, Colin Bredenberg, Roman Huszar, Heloisa Chiossi, Lorenzo Posani, and Mohamady El-Gaby for comments on an earlier version of the manuscript.","ec_funded":1,"pmid":1,"external_id":{"pmid":["37758476"]},"year":"2023","ddc":["570"],"page":"8140-8156","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1523/JNEUROSCI.0194-23.2023"}],"publisher":"Society of Neuroscience","article_processing_charge":"Yes (in subscription journal)","doi":"10.1523/JNEUROSCI.0194-23.2023","type":"journal_article","_id":"14656","date_updated":"2023-12-11T11:37:20Z"},{"article_type":"original","date_created":"2023-06-11T22:00:40Z","volume":14,"title":"Dynamic pathogen detection and social feedback shape collective hygiene in ants","oa_version":"Published Version","author":[{"full_name":"Casillas Perez, Barbara E","id":"351ED2AA-F248-11E8-B48F-1D18A9856A87","last_name":"Casillas Perez","first_name":"Barbara E"},{"last_name":"Bod'Ová","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bod'Ová, Katarína","first_name":"Katarína","orcid":"0000-0002-7214-0171"},{"last_name":"Grasse","id":"406F989C-F248-11E8-B48F-1D18A9856A87","full_name":"Grasse, Anna V","first_name":"Anna V"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik"},{"orcid":"0000-0002-2193-3868","first_name":"Sylvia","full_name":"Cremer, Sylvia","id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87","last_name":"Cremer"}],"scopus_import":"1","day":"03","publication_status":"published","publication_identifier":{"eissn":["2041-1723"]},"file_date_updated":"2023-06-13T08:05:46Z","abstract":[{"lang":"eng","text":"Cooperative disease defense emerges as group-level collective behavior, yet how group members make the underlying individual decisions is poorly understood. Using garden ants and fungal pathogens as an experimental model, we derive the rules governing individual ant grooming choices and show how they produce colony-level hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic modeling reveal that ants increase grooming and preferentially target highly-infectious individuals when perceiving high pathogen load, but transiently suppress grooming after having been groomed by nestmates. Ants thus react to both, the infectivity of others and the social feedback they receive on their own contagiousness. While inferred solely from momentary ant decisions, these behavioral rules quantitatively predict hour-long experimental dynamics, and synergistically combine into efficient colony-wide pathogen removal. Our analyses show that noisy individual decisions based on only local, incomplete, yet dynamically-updated information on pathogen threat and social feedback can lead to potent collective disease defense."}],"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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        14","acknowledged_ssus":[{"_id":"LifeSc"}],"has_accepted_license":"1","article_number":"3232","file":[{"file_id":"13132","creator":"dernst","date_updated":"2023-06-13T08:05:46Z","date_created":"2023-06-13T08:05:46Z","file_size":2358167,"checksum":"4af0393e3ed47b3fc46e68b81c3c1007","relation":"main_file","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2023_NatureComm_CasillasPerez.pdf"}],"department":[{"_id":"SyCr"},{"_id":"GaTk"}],"month":"06","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"B.E. Casillas Perez, K. Bodova, A.V. Grasse, G. Tkačik, S. Cremer, Nature Communications 14 (2023).","ieee":"B. E. Casillas Perez, K. Bodova, A. V. Grasse, G. Tkačik, and S. Cremer, “Dynamic pathogen detection and social feedback shape collective hygiene in ants,” <i>Nature Communications</i>, vol. 14. Springer Nature, 2023.","ama":"Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. Dynamic pathogen detection and social feedback shape collective hygiene in ants. <i>Nature Communications</i>. 2023;14. doi:<a href=\"https://doi.org/10.1038/s41467-023-38947-y\">10.1038/s41467-023-38947-y</a>","mla":"Casillas Perez, Barbara E., et al. “Dynamic Pathogen Detection and Social Feedback Shape Collective Hygiene in Ants.” <i>Nature Communications</i>, vol. 14, 3232, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41467-023-38947-y\">10.1038/s41467-023-38947-y</a>.","apa":"Casillas Perez, B. E., Bodova, K., Grasse, A. V., Tkačik, G., &#38; Cremer, S. (2023). Dynamic pathogen detection and social feedback shape collective hygiene in ants. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-023-38947-y\">https://doi.org/10.1038/s41467-023-38947-y</a>","chicago":"Casillas Perez, Barbara E, Katarina Bodova, Anna V Grasse, Gašper Tkačik, and Sylvia Cremer. “Dynamic Pathogen Detection and Social Feedback Shape Collective Hygiene in Ants.” <i>Nature Communications</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41467-023-38947-y\">https://doi.org/10.1038/s41467-023-38947-y</a>.","ista":"Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. 2023. Dynamic pathogen detection and social feedback shape collective hygiene in ants. Nature Communications. 14, 3232."},"language":[{"iso":"eng"}],"oa":1,"type":"journal_article","date_updated":"2023-08-07T13:09:09Z","_id":"13127","publisher":"Springer Nature","doi":"10.1038/s41467-023-38947-y","article_processing_charge":"Yes","quality_controlled":"1","ddc":["570"],"external_id":{"isi":["001002562700005"],"pmid":["37270641"]},"related_material":{"record":[{"relation":"research_data","status":"public","id":"12945"}]},"isi":1,"year":"2023","date_published":"2023-06-03T00:00:00Z","acknowledgement":"We thank Mike Bidochka for the fungal strains, the ISTA Social Immunity Team for ant collection, Hanna Leitner for experimental and molecular support, Jennifer Robb and Lukas Lindorfer for microscopy, and the LabSupport Facility at ISTA for general laboratory support. We further thank Victor Mireles, Iain Couzin, Fabian Theis and the Social Immunity Team for continued feedback throughout, and Michael Sixt, Yuko Ulrich, Koos Boomsma, Erika Dawson, Megan Kutzer and Hinrich Schulenburg for comments on the manuscript. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant No. 771402; EPIDEMICSonCHIP) to SC, from the Scientific Grant Agency of the Slovak Republic (Grant No. 1/0521/20) to KB, and the Human Frontier Science Program (Grant No. RGP0065/2012) to GT.","pmid":1,"ec_funded":1,"project":[{"call_identifier":"H2020","name":"Epidemics in ant societies on a chip","grant_number":"771402","_id":"2649B4DE-B435-11E9-9278-68D0E5697425"},{"_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012","name":"Information processing and computation in fish groups"}],"status":"public","publication":"Nature Communications"},{"quality_controlled":"1","ddc":["570"],"page":"254-263","type":"journal_article","date_updated":"2023-08-16T12:41:53Z","_id":"12762","publisher":"Springer Nature","doi":"10.1038/s43588-023-00410-9","article_processing_charge":"No","date_published":"2023-03-20T00:00:00Z","acknowledgement":"This research was funded in whole, or in part, by the Austrian Science Fund (FWF) (grant no. PT1013M03318 to F.L. and no. P34015 to G.T.). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The study was supported by the European Union Horizon 2020 research and innovation program under the Marie Sklodowska-Curie action (grant agreement No. 754411 to F.L.).","ec_funded":1,"project":[{"_id":"260C2330-B435-11E9-9278-68D0E5697425","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","call_identifier":"H2020"},{"grant_number":"M03318","name":"Functional Advantages of Critical Brain Dynamics","_id":"eb943429-77a9-11ec-83b8-9f471cdf5c67"},{"name":"Efficient coding with biophysical realism","grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6"}],"publication":"Nature Computational Science","status":"public","external_id":{"arxiv":["2108.06686"]},"year":"2023","publication_status":"published","publication_identifier":{"eissn":["2662-8457"]},"file_date_updated":"2023-08-16T12:39:57Z","abstract":[{"lang":"eng","text":"Neurons in the brain are wired into adaptive networks that exhibit collective dynamics as diverse as scale-specific oscillations and scale-free neuronal avalanches. Although existing models account for oscillations and avalanches separately, they typically do not explain both phenomena, are too complex to analyze analytically or intractable to infer from data rigorously. Here we propose a feedback-driven Ising-like class of neural networks that captures avalanches and oscillations simultaneously and quantitatively. In the simplest yet fully microscopic model version, we can analytically compute the phase diagram and make direct contact with human brain resting-state activity recordings via tractable inference of the model’s two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor oscillations to collective behaviors of extreme events and neuronal avalanches. Importantly, the inferred parameters indicate that the co-existence of scale-specific (oscillations) and scale-free (avalanches) dynamics occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations."}],"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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"         3","has_accepted_license":"1","article_type":"original","date_created":"2023-03-26T22:01:08Z","volume":3,"oa_version":"Published Version","title":"Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain","author":[{"orcid":"0000-0003-2623-5249","first_name":"Fabrizio","full_name":"Lombardi, Fabrizio","id":"A057D288-3E88-11E9-986D-0CF4E5697425","last_name":"Lombardi"},{"first_name":"Selver","full_name":"Pepic, Selver","id":"F93245C4-C3CA-11E9-B4F0-C6F4E5697425","last_name":"Pepic"},{"first_name":"Oren","full_name":"Shriki, Oren","last_name":"Shriki"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"orcid":"0000-0002-5214-4706","first_name":"Daniele","full_name":"De Martino, Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","last_name":"De Martino"}],"scopus_import":"1","day":"20","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ieee":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain,” <i>Nature Computational Science</i>, vol. 3. Springer Nature, pp. 254–263, 2023.","short":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, Nature Computational Science 3 (2023) 254–263.","ama":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. <i>Nature Computational Science</i>. 2023;3:254-263. doi:<a href=\"https://doi.org/10.1038/s43588-023-00410-9\">10.1038/s43588-023-00410-9</a>","apa":"Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., &#38; De Martino, D. (2023). Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. <i>Nature Computational Science</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s43588-023-00410-9\">https://doi.org/10.1038/s43588-023-00410-9</a>","mla":"Lombardi, Fabrizio, et al. “Statistical Modeling of Adaptive Neural Networks Explains Co-Existence of Avalanches and Oscillations in Resting Human Brain.” <i>Nature Computational Science</i>, vol. 3, Springer Nature, 2023, pp. 254–63, doi:<a href=\"https://doi.org/10.1038/s43588-023-00410-9\">10.1038/s43588-023-00410-9</a>.","ista":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. 2023. Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. Nature Computational Science. 3, 254–263.","chicago":"Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele De Martino. “Statistical Modeling of Adaptive Neural Networks Explains Co-Existence of Avalanches and Oscillations in Resting Human Brain.” <i>Nature Computational Science</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s43588-023-00410-9\">https://doi.org/10.1038/s43588-023-00410-9</a>."},"language":[{"iso":"eng"}],"oa":1,"file":[{"file_id":"14073","date_updated":"2023-08-16T12:39:57Z","creator":"dernst","date_created":"2023-08-16T12:39:57Z","file_size":4474284,"checksum":"7c63b2b2edfd68aaffe96d70ca6a865a","relation":"main_file","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2023_NatureCompScience_Lombardi.pdf"}],"department":[{"_id":"GaTk"},{"_id":"GradSch"}],"arxiv":1,"month":"03"},{"scopus_import":"1","day":"26","author":[{"first_name":"Mato","id":"345D25EC-F248-11E8-B48F-1D18A9856A87","full_name":"Lagator, Mato","last_name":"Lagator"},{"full_name":"Sarikas, Srdjan","id":"35F0286E-F248-11E8-B48F-1D18A9856A87","last_name":"Sarikas","first_name":"Srdjan"},{"first_name":"Magdalena","full_name":"Steinrueck, Magdalena","last_name":"Steinrueck"},{"first_name":"David","last_name":"Toledo-Aparicio","full_name":"Toledo-Aparicio, David"},{"first_name":"Jonathan P","orcid":"0000-0002-4624-4612","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","full_name":"Bollback, Jonathan P","last_name":"Bollback"},{"last_name":"Guet","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","orcid":"0000-0001-6220-2052"},{"last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper"}],"title":"Predicting bacterial promoter function and evolution from random sequences","oa_version":"Published Version","volume":11,"date_created":"2022-02-06T23:01:32Z","article_type":"original","has_accepted_license":"1","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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought."}],"intvolume":"        11","file_date_updated":"2022-02-07T07:14:09Z","publication_identifier":{"eissn":["2050-084X"]},"publication_status":"published","month":"01","department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"NiBa"}],"file":[{"file_id":"10739","date_created":"2022-02-07T07:14:09Z","file_size":5604343,"date_updated":"2022-02-07T07:14:09Z","creator":"cchlebak","relation":"main_file","checksum":"decdcdf600ff51e9a9703b49ca114170","success":1,"file_name":"2022_ELife_Lagator.pdf","access_level":"open_access","content_type":"application/pdf"}],"article_number":"e64543","oa":1,"language":[{"iso":"eng"}],"citation":{"ama":"Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function and evolution from random sequences. <i>eLife</i>. 2022;11. doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>","short":"M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback, C.C. Guet, G. Tkačik, ELife 11 (2022).","ieee":"M. Lagator <i>et al.</i>, “Predicting bacterial promoter function and evolution from random sequences,” <i>eLife</i>, vol. 11. eLife Sciences Publications, 2022.","chicago":"Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio, Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>. eLife Sciences Publications, 2022. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>.","ista":"Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. 2022. Predicting bacterial promoter function and evolution from random sequences. eLife. 11, e64543.","mla":"Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>, vol. 11, e64543, eLife Sciences Publications, 2022, doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>.","apa":"Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J. P., Guet, C. C., &#38; Tkačik, G. (2022). Predicting bacterial promoter function and evolution from random sequences. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>"},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","article_processing_charge":"No","doi":"10.7554/eLife.64543","publisher":"eLife Sciences Publications","_id":"10736","date_updated":"2023-08-02T14:09:02Z","type":"journal_article","ddc":["576"],"quality_controlled":"1","isi":1,"year":"2022","external_id":{"pmid":["35080492"],"isi":["000751104400001"]},"publication":"eLife","status":"public","project":[{"_id":"2578D616-B435-11E9-9278-68D0E5697425","grant_number":"648440","name":"Selective Barriers to Horizontal Gene Transfer","call_identifier":"H2020"}],"ec_funded":1,"pmid":1,"acknowledgement":"We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao, Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML; IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme 7 (2007–2013, grant agreement number 648440) to JPB.","date_published":"2022-01-26T00:00:00Z"},{"month":"08","file":[{"checksum":"6dec51f6567da9039982a571508a8e4d","relation":"main_file","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2022_PNAS_Hledik.pdf","file_id":"12091","date_updated":"2022-09-12T08:08:12Z","creator":"dernst","file_size":2165752,"date_created":"2022-09-12T08:08:12Z"}],"article_number":"e2123152119","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"36","citation":{"ista":"Hledik M, Barton NH, Tkačik G. 2022. Accumulation and maintenance of information in evolution. Proceedings of the National Academy of Sciences. 119(36), e2123152119.","chicago":"Hledik, Michal, Nicholas H Barton, and Gašper Tkačik. “Accumulation and Maintenance of Information in Evolution.” <i>Proceedings of the National Academy of Sciences</i>. Proceedings of the National Academy of Sciences, 2022. <a href=\"https://doi.org/10.1073/pnas.2123152119\">https://doi.org/10.1073/pnas.2123152119</a>.","apa":"Hledik, M., Barton, N. H., &#38; Tkačik, G. (2022). Accumulation and maintenance of information in evolution. <i>Proceedings of the National Academy of Sciences</i>. Proceedings of the National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2123152119\">https://doi.org/10.1073/pnas.2123152119</a>","mla":"Hledik, Michal, et al. “Accumulation and Maintenance of Information in Evolution.” <i>Proceedings of the National Academy of Sciences</i>, vol. 119, no. 36, e2123152119, Proceedings of the National Academy of Sciences, 2022, doi:<a href=\"https://doi.org/10.1073/pnas.2123152119\">10.1073/pnas.2123152119</a>.","ama":"Hledik M, Barton NH, Tkačik G. Accumulation and maintenance of information in evolution. <i>Proceedings of the National Academy of Sciences</i>. 2022;119(36). doi:<a href=\"https://doi.org/10.1073/pnas.2123152119\">10.1073/pnas.2123152119</a>","ieee":"M. Hledik, N. H. Barton, and G. Tkačik, “Accumulation and maintenance of information in evolution,” <i>Proceedings of the National Academy of Sciences</i>, vol. 119, no. 36. Proceedings of the National Academy of Sciences, 2022.","short":"M. Hledik, N.H. Barton, G. Tkačik, Proceedings of the National Academy of Sciences 119 (2022)."},"title":"Accumulation and maintenance of information in evolution","oa_version":"Published Version","author":[{"first_name":"Michal","id":"4171253A-F248-11E8-B48F-1D18A9856A87","full_name":"Hledik, Michal","last_name":"Hledik"},{"last_name":"Barton","full_name":"Barton, Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H","orcid":"0000-0002-8548-5240"},{"last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"1","first_name":"Gašper"}],"scopus_import":"1","day":"29","article_type":"original","date_created":"2022-09-11T22:01:55Z","volume":119,"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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"       119","abstract":[{"lang":"eng","text":"Selection accumulates information in the genome—it guides stochastically evolving populations toward states (genotype frequencies) that would be unlikely under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence between the actual distribution of genotype frequencies and the corresponding neutral distribution. First, we show that this population-level information sets an upper bound on the information at the level of genotype and phenotype, limiting how precisely they can be specified by selection. Next, we study how the accumulation and maintenance of information is limited by the cost of selection, measured as the genetic load or the relative fitness variance, both of which we connect to the control-theoretic KL cost of control. The information accumulation rate is upper bounded by the population size times the cost of selection. This bound is very general, and applies across models (Wright–Fisher, Moran, diffusion) and to arbitrary forms of selection, mutation, and recombination. Finally, the cost of maintaining information depends on how it is encoded: Specifying a single allele out of two is expensive, but one bit encoded among many weakly specified loci (as in a polygenic trait) is cheap."}],"has_accepted_license":"1","publication_status":"published","publication_identifier":{"issn":["0027-8424"],"eissn":["1091-6490"]},"file_date_updated":"2022-09-12T08:08:12Z","external_id":{"isi":["000889278400014"],"pmid":["36037343"]},"related_material":{"record":[{"id":"15020","status":"public","relation":"dissertation_contains"}]},"isi":1,"year":"2022","project":[{"_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7"},{"grant_number":"RGP0034/2018","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?","_id":"2665AAFE-B435-11E9-9278-68D0E5697425"}],"publication":"Proceedings of the National Academy of Sciences","status":"public","acknowledgement":"We thank Ksenia Khudiakova, Wiktor Młynarski, Sean Stankowski, and two anonymous reviewers for discussions and comments on the manuscript. G.T. and M.H. acknowledge funding from the Human Frontier Science Program Grant RGP0032/2018. N.B. acknowledges funding from ERC Grant 250152 “Information and Evolution.”","date_published":"2022-08-29T00:00:00Z","pmid":1,"ec_funded":1,"publisher":"Proceedings of the National Academy of Sciences","doi":"10.1073/pnas.2123152119","article_processing_charge":"No","type":"journal_article","date_updated":"2025-06-30T13:21:05Z","_id":"12081","ddc":["570"],"quality_controlled":"1"},{"date_published":"2022-09-01T00:00:00Z","acknowledgement":"This work was supported through the Center for the Physics of Biological Function (PHYe1734030) and by National Institutes of Health Grants R01GM097275 and U01DK127429 (TG). GT acknowledges the support of the Austrian Science Fund grant FWF P28844 and the Human Frontiers Science Program. ","publication":"Current Opinion in Systems Biology","status":"public","project":[{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"keyword":["Applied Mathematics","Computer Science Applications","Drug Discovery","General Biochemistry","Genetics and Molecular Biology","Modeling and Simulation"],"year":"2022","quality_controlled":"1","ddc":["570"],"_id":"12156","date_updated":"2023-02-13T09:20:34Z","type":"journal_article","article_processing_charge":"Yes (via OA deal)","doi":"10.1016/j.coisb.2022.100435","publisher":"Elsevier","citation":{"mla":"Zoller, Benjamin, et al. “Eukaryotic Gene Regulation at Equilibrium, or Non?” <i>Current Opinion in Systems Biology</i>, vol. 31, no. 9, 100435, Elsevier, 2022, doi:<a href=\"https://doi.org/10.1016/j.coisb.2022.100435\">10.1016/j.coisb.2022.100435</a>.","apa":"Zoller, B., Gregor, T., &#38; Tkačik, G. (2022). Eukaryotic gene regulation at equilibrium, or non? <i>Current Opinion in Systems Biology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.coisb.2022.100435\">https://doi.org/10.1016/j.coisb.2022.100435</a>","chicago":"Zoller, Benjamin, Thomas Gregor, and Gašper Tkačik. “Eukaryotic Gene Regulation at Equilibrium, or Non?” <i>Current Opinion in Systems Biology</i>. Elsevier, 2022. <a href=\"https://doi.org/10.1016/j.coisb.2022.100435\">https://doi.org/10.1016/j.coisb.2022.100435</a>.","ista":"Zoller B, Gregor T, Tkačik G. 2022. Eukaryotic gene regulation at equilibrium, or non? Current Opinion in Systems Biology. 31(9), 100435.","short":"B. Zoller, T. Gregor, G. Tkačik, Current Opinion in Systems Biology 31 (2022).","ieee":"B. Zoller, T. Gregor, and G. Tkačik, “Eukaryotic gene regulation at equilibrium, or non?,” <i>Current Opinion in Systems Biology</i>, vol. 31, no. 9. Elsevier, 2022.","ama":"Zoller B, Gregor T, Tkačik G. Eukaryotic gene regulation at equilibrium, or non? <i>Current Opinion in Systems Biology</i>. 2022;31(9). doi:<a href=\"https://doi.org/10.1016/j.coisb.2022.100435\">10.1016/j.coisb.2022.100435</a>"},"issue":"9","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}],"file":[{"file_id":"12362","file_size":2214944,"date_created":"2023-01-24T12:14:10Z","date_updated":"2023-01-24T12:14:10Z","creator":"dernst","relation":"main_file","checksum":"97ef01e0cc60cdc84f45640a0f248fb0","success":1,"file_name":"2022_CurrentBiology_Zoller.pdf","access_level":"open_access","content_type":"application/pdf"}],"article_number":"100435","month":"09","file_date_updated":"2023-01-24T12:14:10Z","publication_identifier":{"issn":["2452-3100"]},"publication_status":"published","has_accepted_license":"1","abstract":[{"lang":"eng","text":"Models of transcriptional regulation that assume equilibrium binding of transcription factors have been less successful at predicting gene expression from sequence in eukaryotes than in bacteria. This could be due to the non-equilibrium nature of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory—theory that prescribes rather than just describes—in providing such a focus. Theory should expand its remit beyond inferring mechanistic models from data, towards identifying non-equilibrium gene regulatory schemes that may have been evolutionarily selected, despite their energy consumption, because they are precise, reliable, fast, or otherwise outperform regulation at equilibrium. We illustrate our reasoning by toy examples for which we provide simulation code."}],"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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        31","volume":31,"date_created":"2023-01-12T12:08:51Z","article_type":"original","scopus_import":"1","day":"01","author":[{"first_name":"Benjamin","last_name":"Zoller","full_name":"Zoller, Benjamin"},{"last_name":"Gregor","full_name":"Gregor, Thomas","first_name":"Thomas"},{"orcid":"1","first_name":"Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"}],"oa_version":"Published Version","title":"Eukaryotic gene regulation at equilibrium, or non?"},{"project":[{"name":"Efficient coding with biophysical realism","grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6"},{"call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"publication":"PLoS Biology","status":"public","ec_funded":1,"acknowledgement":"We thank Robbe Goris for generously providing figures from his work and Ann M. Hermundstad for helpful discussions.\r\nGT & WM were supported by the Austrian Science Fund Standalone Grant P 34015 \"Efficient Coding with Biophysical Realism\" (https://pf.fwf.ac.at/) WM was additionally supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 754411 (https://ec.europa.eu/research/mariecurieactions/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","date_published":"2022-12-21T00:00:00Z","year":"2022","isi":1,"external_id":{"isi":["000925192000001"]},"page":"e3001889","ddc":["570"],"quality_controlled":"1","doi":"10.1371/journal.pbio.3001889","article_processing_charge":"No","publisher":"Public Library of Science","date_updated":"2023-08-03T14:23:49Z","_id":"12332","type":"journal_article","oa":1,"language":[{"iso":"eng"}],"issue":"12","citation":{"apa":"Mlynarski, W. F., &#38; Tkačik, G. (2022). Efficient coding theory of dynamic attentional modulation. <i>PLoS Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pbio.3001889\">https://doi.org/10.1371/journal.pbio.3001889</a>","mla":"Mlynarski, Wiktor F., and Gašper Tkačik. “Efficient Coding Theory of Dynamic Attentional Modulation.” <i>PLoS Biology</i>, vol. 20, no. 12, Public Library of Science, 2022, p. e3001889, doi:<a href=\"https://doi.org/10.1371/journal.pbio.3001889\">10.1371/journal.pbio.3001889</a>.","chicago":"Mlynarski, Wiktor F, and Gašper Tkačik. “Efficient Coding Theory of Dynamic Attentional Modulation.” <i>PLoS Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pbio.3001889\">https://doi.org/10.1371/journal.pbio.3001889</a>.","ista":"Mlynarski WF, Tkačik G. 2022. Efficient coding theory of dynamic attentional modulation. PLoS Biology. 20(12), e3001889.","ieee":"W. F. Mlynarski and G. Tkačik, “Efficient coding theory of dynamic attentional modulation,” <i>PLoS Biology</i>, vol. 20, no. 12. Public Library of Science, p. e3001889, 2022.","short":"W.F. Mlynarski, G. Tkačik, PLoS Biology 20 (2022) e3001889.","ama":"Mlynarski WF, Tkačik G. Efficient coding theory of dynamic attentional modulation. <i>PLoS Biology</i>. 2022;20(12):e3001889. doi:<a href=\"https://doi.org/10.1371/journal.pbio.3001889\">10.1371/journal.pbio.3001889</a>"},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"12","department":[{"_id":"GaTk"}],"file":[{"file_id":"12337","date_updated":"2023-01-23T08:46:40Z","creator":"dernst","date_created":"2023-01-23T08:46:40Z","file_size":4248838,"checksum":"5d7f1111a87e5f2c1bf92f8886738894","relation":"main_file","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2022_PloSBiology_Mlynarski.pdf"}],"has_accepted_license":"1","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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"Activity of sensory neurons is driven not only by external stimuli but also by feedback signals from higher brain areas. Attention is one particularly important internal signal whose presumed role is to modulate sensory representations such that they only encode information currently relevant to the organism at minimal cost. This hypothesis has, however, not yet been expressed in a normative computational framework. Here, by building on normative principles of probabilistic inference and efficient coding, we developed a model of dynamic population coding in the visual cortex. By continuously adapting the sensory code to changing demands of the perceptual observer, an attention-like modulation emerges. This modulation can dramatically reduce the amount of neural activity without deteriorating the accuracy of task-specific inferences. Our results suggest that a range of seemingly disparate cortical phenomena such as intrinsic gain modulation, attention-related tuning modulation, and response variability could be manifestations of the same underlying principles, which combine efficient sensory coding with optimal probabilistic inference in dynamic environments."}],"intvolume":"        20","publication_identifier":{"eissn":["1545-7885"]},"publication_status":"published","file_date_updated":"2023-01-23T08:46:40Z","author":[{"full_name":"Mlynarski, Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87","last_name":"Mlynarski","first_name":"Wiktor F"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","orcid":"1","first_name":"Gašper"}],"scopus_import":"1","day":"21","title":"Efficient coding theory of dynamic attentional modulation","oa_version":"Published Version","volume":20,"article_type":"original","date_created":"2023-01-22T23:00:55Z"},{"abstract":[{"text":"Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time-bins. Importantly, the inferred parameters correlate with model-independent signatures of \"closeness to criticality\", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.","lang":"eng"}],"ddc":["570"],"page":"37","publication_status":"submitted","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2108.06686"}],"publisher":"arXiv","oa_version":"Preprint","title":"Quantifying the coexistence of neuronal oscillations and avalanches","author":[{"full_name":"Lombardi, Fabrizio","id":"A057D288-3E88-11E9-986D-0CF4E5697425","last_name":"Lombardi","orcid":"0000-0003-2623-5249","first_name":"Fabrizio"},{"last_name":"Pepic","full_name":"Pepic, Selver","id":"F93245C4-C3CA-11E9-B4F0-C6F4E5697425","first_name":"Selver"},{"full_name":"Shriki, Oren","last_name":"Shriki","first_name":"Oren"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"full_name":"De Martino, Daniele","last_name":"De Martino","first_name":"Daniele"}],"doi":"10.48550/ARXIV.2108.06686","day":"17","article_processing_charge":"No","type":"preprint","date_created":"2022-03-21T11:41:28Z","date_updated":"2022-03-22T07:53:18Z","_id":"10912","project":[{"name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425"},{"name":"Efficient coding with biophysical realism","grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6"}],"language":[{"iso":"eng"}],"status":"public","oa":1,"acknowledgement":"FL acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 754411. GT\r\nacknowledges the support of the Austrian Science Fund (FWF) under Stand-Alone Grant\r\nNo. P34015.","date_published":"2021-08-17T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>.","chicago":"Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele De Martino. “Quantifying the Coexistence of Neuronal Oscillations and Avalanches.” arXiv, n.d. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">https://doi.org/10.48550/ARXIV.2108.06686</a>.","mla":"Lombardi, Fabrizio, et al. <i>Quantifying the Coexistence of Neuronal Oscillations and Avalanches</i>. arXiv, doi:<a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>.","apa":"Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., &#38; De Martino, D. (n.d.). Quantifying the coexistence of neuronal oscillations and avalanches. arXiv. <a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">https://doi.org/10.48550/ARXIV.2108.06686</a>","ama":"Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Quantifying the coexistence of neuronal oscillations and avalanches. doi:<a href=\"https://doi.org/10.48550/ARXIV.2108.06686\">10.48550/ARXIV.2108.06686</a>","short":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, (n.d.).","ieee":"F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Quantifying the coexistence of neuronal oscillations and avalanches.” arXiv."},"ec_funded":1,"external_id":{"arxiv":["2108.06686"]},"month":"08","arxiv":1,"year":"2021","department":[{"_id":"GaTk"}]},{"date_published":"2021-01-07T00:00:00Z","acknowledgement":"This work was supported in part by Tum stipend of Knafelj foundation (to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). ","status":"public","publication":"PLOS Computational Biology","project":[{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"keyword":["Modelling and Simulation","Genetics","Molecular Biology","Antibiotics","Drug interactions"],"related_material":{"record":[{"status":"public","relation":"earlier_version","id":"7673"},{"status":"public","relation":"research_data","id":"8930"}]},"external_id":{"isi":["000608045000010"]},"year":"2021","isi":1,"quality_controlled":"1","ddc":["570"],"type":"journal_article","_id":"8997","date_updated":"2024-02-21T12:41:41Z","publisher":"Public Library of Science","article_processing_charge":"Yes","doi":"10.1371/journal.pcbi.1008529","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"ama":"Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic action. <i>PLOS Computational Biology</i>. 2021;17. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">10.1371/journal.pcbi.1008529</a>","short":"B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021).","ieee":"B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of combined antibiotic action,” <i>PLOS Computational Biology</i>, vol. 17. Public Library of Science, 2021.","chicago":"Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical Model of Combined Antibiotic Action.” <i>PLOS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">https://doi.org/10.1371/journal.pcbi.1008529</a>.","ista":"Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 17, e1008529.","mla":"Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.” <i>PLOS Computational Biology</i>, vol. 17, e1008529, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">10.1371/journal.pcbi.1008529</a>.","apa":"Kavcic, B., Tkačik, G., &#38; Bollenbach, M. T. (2021). Minimal biophysical model of combined antibiotic action. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">https://doi.org/10.1371/journal.pcbi.1008529</a>"},"language":[{"iso":"eng"}],"oa":1,"file":[{"file_id":"9092","file_size":3690053,"date_created":"2021-02-04T12:30:48Z","creator":"dernst","date_updated":"2021-02-04T12:30:48Z","relation":"main_file","checksum":"e29f2b42651bef8e034781de8781ffac","success":1,"file_name":"2021_PlosComBio_Kavcic.pdf","access_level":"open_access","content_type":"application/pdf"}],"article_number":"e1008529","department":[{"_id":"GaTk"}],"month":"01","file_date_updated":"2021-02-04T12:30:48Z","publication_identifier":{"issn":["1553-7358"]},"publication_status":"published","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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"text":"Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.","lang":"eng"}],"intvolume":"        17","has_accepted_license":"1","date_created":"2021-01-08T07:16:18Z","article_type":"original","volume":17,"title":"Minimal biophysical model of combined antibiotic action","oa_version":"Published Version","day":"07","author":[{"first_name":"Bor","orcid":"0000-0001-6041-254X","full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","last_name":"Kavcic"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","first_name":"Gašper","orcid":"0000-0002-6699-1455"},{"full_name":"Bollenbach, Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","first_name":"Tobias"}]},{"month":"04","department":[{"_id":"GaTk"}],"oa":1,"language":[{"iso":"eng"}],"citation":{"ieee":"W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” <i>Neuron</i>, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021.","short":"W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5.","ama":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. <i>Neuron</i>. 2021;109(7):1227-1241.e5. doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>","apa":"Mlynarski, W. F., Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2021). Statistical analysis and optimality of neural systems. <i>Neuron</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>","mla":"Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>.","chicago":"Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>. Cell Press, 2021. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>.","ista":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5."},"issue":"7","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","day":"07","scopus_import":"1","author":[{"first_name":"Wiktor F","last_name":"Mlynarski","full_name":"Mlynarski, Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Hledik","id":"4171253A-F248-11E8-B48F-1D18A9856A87","full_name":"Hledik, Michal","first_name":"Michal"},{"last_name":"Sokolowski","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R","orcid":"0000-0002-1287-3779"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"}],"oa_version":"Preprint","title":"Statistical analysis and optimality of neural systems","volume":109,"date_created":"2020-02-28T11:00:12Z","intvolume":"       109","abstract":[{"text":"Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.","lang":"eng"}],"publication_status":"published","year":"2021","isi":1,"related_material":{"record":[{"id":"15020","relation":"dissertation_contains","status":"public"}],"link":[{"relation":"press_release","description":"News on IST Homepage","url":"https://ist.ac.at/en/news/can-evolution-be-predicted/"}]},"external_id":{"isi":["000637809600006"]},"publication":"Neuron","status":"public","project":[{"_id":"260C2330-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411"}],"ec_funded":1,"acknowledgement":"The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018.","date_published":"2021-04-07T00:00:00Z","article_processing_charge":"No","doi":"10.1016/j.neuron.2021.01.020","publisher":"Cell Press","_id":"7553","date_updated":"2025-06-30T13:21:05Z","type":"journal_article","page":"1227-1241.e5","main_file_link":[{"url":"https://doi.org/10.1101/848374","open_access":"1"}],"quality_controlled":"1"},{"quality_controlled":"1","main_file_link":[{"url":"https://doi.org/10.1242/dev.176065","open_access":"1"}],"publisher":"The Company of Biologists","doi":"10.1242/dev.176065","article_processing_charge":"No","type":"journal_article","date_updated":"2023-08-07T13:57:30Z","_id":"9226","project":[{"name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"publication":"Development","status":"public","date_published":"2021-02-01T00:00:00Z","acknowledgement":"This work was supported in part by the National Science Foundation, through the Center for the Physics of Biological Function (PHY-1734030), by the National Institutes of Health (R01GM097275) and by the Fonds zur Förderung der wissenschaftlichen Forschung (FWF P28844). Deposited in PMC for release after 12 months.","pmid":1,"external_id":{"pmid":["33526425"],"isi":["000613906000007"]},"year":"2021","isi":1,"intvolume":"       148","abstract":[{"text":"Half a century after Lewis Wolpert's seminal conceptual advance on how cellular fates distribute in space, we provide a brief historical perspective on how the concept of positional information emerged and influenced the field of developmental biology and beyond. We focus on a modern interpretation of this concept in terms of information theory, largely centered on its application to cell specification in the early Drosophila embryo. We argue that a true physical variable (position) is encoded in local concentrations of patterning molecules, that this mapping is stochastic, and that the processes by which positions and corresponding cell fates are determined based on these concentrations need to take such stochasticity into account. With this approach, we shift the focus from biological mechanisms, molecules, genes and pathways to quantitative systems-level questions: where does positional information reside, how it is transformed and accessed during development, and what fundamental limits it is subject to?","lang":"eng"}],"publication_identifier":{"eissn":["1477-9129"]},"publication_status":"published","oa_version":"Published Version","title":"The many bits of positional information","author":[{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","first_name":"Gašper","orcid":"0000-0002-6699-1455"},{"first_name":"Thomas","full_name":"Gregor, Thomas","last_name":"Gregor"}],"day":"01","scopus_import":"1","article_type":"original","date_created":"2021-03-07T23:01:25Z","volume":148,"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"2","citation":{"mla":"Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.” <i>Development</i>, vol. 148, no. 2, dev176065, The Company of Biologists, 2021, doi:<a href=\"https://doi.org/10.1242/dev.176065\">10.1242/dev.176065</a>.","apa":"Tkačik, G., &#38; Gregor, T. (2021). The many bits of positional information. <i>Development</i>. The Company of Biologists. <a href=\"https://doi.org/10.1242/dev.176065\">https://doi.org/10.1242/dev.176065</a>","chicago":"Tkačik, Gašper, and Thomas Gregor. “The Many Bits of Positional Information.” <i>Development</i>. The Company of Biologists, 2021. <a href=\"https://doi.org/10.1242/dev.176065\">https://doi.org/10.1242/dev.176065</a>.","ista":"Tkačik G, Gregor T. 2021. The many bits of positional information. Development. 148(2), dev176065.","short":"G. Tkačik, T. Gregor, Development 148 (2021).","ieee":"G. Tkačik and T. Gregor, “The many bits of positional information,” <i>Development</i>, vol. 148, no. 2. The Company of Biologists, 2021.","ama":"Tkačik G, Gregor T. The many bits of positional information. <i>Development</i>. 2021;148(2). doi:<a href=\"https://doi.org/10.1242/dev.176065\">10.1242/dev.176065</a>"},"month":"02","article_number":"dev176065","department":[{"_id":"GaTk"}]},{"year":"2021","isi":1,"external_id":{"isi":["000641474900072"],"pmid":["33857170"]},"pmid":1,"date_published":"2021-04-15T00:00:00Z","acknowledgement":"The authors would like to thank Ulisse Ferrari for useful discussions and feedback.","publication":"PLoS ONE","status":"public","date_updated":"2023-10-18T08:17:42Z","_id":"9362","type":"journal_article","doi":"10.1371/journal.pone.0248940","article_processing_charge":"No","publisher":"Public Library of Science","quality_controlled":"1","ddc":["570"],"department":[{"_id":"GaTk"}],"article_number":"e0248940","file":[{"file_id":"9371","file_size":2768282,"date_created":"2021-05-04T13:22:19Z","date_updated":"2021-05-04T13:22:19Z","creator":"kschuh","relation":"main_file","checksum":"c52da133850307d2031f552d998f00e8","file_name":"2021_pone_Chalk.pdf","success":1,"content_type":"application/pdf","access_level":"open_access"}],"month":"04","issue":"4","citation":{"ama":"Chalk MJ, Tkačik G, Marre O. Inferring the function performed by a recurrent neural network. <i>PLoS ONE</i>. 2021;16(4). doi:<a href=\"https://doi.org/10.1371/journal.pone.0248940\">10.1371/journal.pone.0248940</a>","ieee":"M. J. Chalk, G. Tkačik, and O. Marre, “Inferring the function performed by a recurrent neural network,” <i>PLoS ONE</i>, vol. 16, no. 4. Public Library of Science, 2021.","short":"M.J. Chalk, G. Tkačik, O. Marre, PLoS ONE 16 (2021).","chicago":"Chalk, Matthew J, Gašper Tkačik, and Olivier Marre. “Inferring the Function Performed by a Recurrent Neural Network.” <i>PLoS ONE</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pone.0248940\">https://doi.org/10.1371/journal.pone.0248940</a>.","ista":"Chalk MJ, Tkačik G, Marre O. 2021. Inferring the function performed by a recurrent neural network. PLoS ONE. 16(4), e0248940.","apa":"Chalk, M. J., Tkačik, G., &#38; Marre, O. (2021). Inferring the function performed by a recurrent neural network. <i>PLoS ONE</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0248940\">https://doi.org/10.1371/journal.pone.0248940</a>","mla":"Chalk, Matthew J., et al. “Inferring the Function Performed by a Recurrent Neural Network.” <i>PLoS ONE</i>, vol. 16, no. 4, e0248940, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pone.0248940\">10.1371/journal.pone.0248940</a>."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"volume":16,"article_type":"original","date_created":"2021-05-02T22:01:28Z","author":[{"orcid":"0000-0001-7782-4436","first_name":"Matthew J","last_name":"Chalk","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","full_name":"Chalk, Matthew J"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"last_name":"Marre","full_name":"Marre, Olivier","first_name":"Olivier"}],"scopus_import":"1","day":"15","title":"Inferring the function performed by a recurrent neural network","oa_version":"Published Version","publication_identifier":{"eissn":["19326203"]},"publication_status":"published","file_date_updated":"2021-05-04T13:22:19Z","has_accepted_license":"1","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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        16","abstract":[{"text":"A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.","lang":"eng"}]},{"date_updated":"2024-03-25T23:30:09Z","_id":"10077","type":"preprint","date_created":"2021-10-04T06:23:34Z","doi":"10.1101/2021.09.28.460602","author":[{"orcid":"0000-0001-8849-6570","first_name":"Michele","last_name":"Nardin","id":"30BD0376-F248-11E8-B48F-1D18A9856A87","full_name":"Nardin, Michele"},{"last_name":"Csicsvari","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","full_name":"Csicsvari, Jozsef L","orcid":"0000-0002-5193-4036","first_name":"Jozsef L"},{"first_name":"Gašper","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik"},{"first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"}],"article_processing_charge":"No","day":"29","publisher":"Cold Spring Harbor Laboratory","oa_version":"Preprint","title":"The structure of hippocampal CA1 interactions optimizes spatial coding across experience","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/2021.09.28.460602"}],"publication_status":"submitted","abstract":[{"text":"Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.","lang":"eng"}],"license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","tmp":{"image":"/images/cc_by_nc_nd.png","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)"},"department":[{"_id":"GradSch"},{"_id":"JoCs"},{"_id":"GaTk"}],"year":"2021","related_material":{"record":[{"id":"11932","status":"public","relation":"dissertation_contains"}]},"month":"09","citation":{"chicago":"Nardin, Michele, Jozsef L Csicsvari, Gašper Tkačik, and Cristina Savin. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, n.d. <a href=\"https://doi.org/10.1101/2021.09.28.460602\">https://doi.org/10.1101/2021.09.28.460602</a>.","ista":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. bioRxiv, <a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>.","mla":"Nardin, Michele, et al. “The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, doi:<a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>.","apa":"Nardin, M., Csicsvari, J. L., Tkačik, G., &#38; Savin, C. (n.d.). The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2021.09.28.460602\">https://doi.org/10.1101/2021.09.28.460602</a>","ama":"Nardin M, Csicsvari JL, Tkačik G, Savin C. The structure of hippocampal CA1 interactions optimizes spatial coding across experience. <i>bioRxiv</i>. doi:<a href=\"https://doi.org/10.1101/2021.09.28.460602\">10.1101/2021.09.28.460602</a>","short":"M. Nardin, J.L. Csicsvari, G. Tkačik, C. Savin, BioRxiv (n.d.).","ieee":"M. Nardin, J. L. Csicsvari, G. Tkačik, and C. Savin, “The structure of hippocampal CA1 interactions optimizes spatial coding across experience,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory."},"ec_funded":1,"acknowledgement":"We thank Peter Baracskay, Karola Kaefer and Hugo Malagon-Vina for the acquisition of the data. We thank Federico Stella for comments on an earlier version of the manuscript. MN was supported by European Union Horizon 2020 grant 665385, JC was supported by European Research Council consolidator grant 281511, GT was supported by the Austrian Science Fund (FWF) grant P34015, CS was supported by an IST fellow grant, National Institute of Mental Health Award 1R01MH125571-01, by the National Science Foundation under NSF Award No. 1922658 and a Google faculty award.","date_published":"2021-09-29T00:00:00Z","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","oa":1,"project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"grant_number":"665385","name":"International IST Doctoral Program","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FP7","name":"Memory-related information processing in neuronal circuits of the hippocampus and entorhinal cortex","grant_number":"281511","_id":"257A4776-B435-11E9-9278-68D0E5697425"},{"grant_number":"P34015","name":"Efficient coding with biophysical realism","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6"}],"publication":"bioRxiv","language":[{"iso":"eng"}],"status":"public"},{"date_created":"2021-12-28T06:52:09Z","type":"preprint","_id":"10579","date_updated":"2023-05-03T10:54:05Z","oa_version":"Preprint","title":"Token-driven totally asymmetric simple exclusion process","article_processing_charge":"No","day":"27","doi":"10.48550/arXiv.2112.13558","author":[{"full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","last_name":"Kavcic","first_name":"Bor","orcid":"0000-0001-6041-254X"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik"}],"publication_status":"submitted","main_file_link":[{"url":"https://arxiv.org/abs/2112.13558","open_access":"1"}],"ddc":["530"],"abstract":[{"lang":"eng","text":"We consider a totally asymmetric simple exclusion process (TASEP) consisting of particles on a lattice that require binding by a \"token\" to move. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation; (ii) How the current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such a slow site) over the entire lattice; (iv) How a shared pool of tokens couples concurrent TASEPs running on multiple lattices; (v) How our results translate to TASEPs with open boundaries that exchange particles with the reservoir. Since real particle motion (including in systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations."}],"tmp":{"image":"/images/cc_by_nc_nd.png","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)"},"has_accepted_license":"1","article_number":"2112.13558","department":[{"_id":"GaTk"}],"arxiv":1,"month":"12","external_id":{"arxiv":["2112.13558"]},"year":"2021","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"B.K. thanks Stefano Elefante, Simon Rella, and Michal Hledík for their help with the usage of the cluster. B.K. additionally thanks Călin Guet and his group for help and advice. We thank M. Hennessey-Wesen for constructive comments on the manuscript. We thank Ankita Gupta (Indian Institute of Technology) for spotting a typographical error in Eq. (49) in the preprint version of this paper.","date_published":"2021-12-27T00:00:00Z","citation":{"ieee":"B. Kavcic and G. Tkačik, “Token-driven totally asymmetric simple exclusion process,” <i>arXiv</i>. .","short":"B. Kavcic, G. Tkačik, ArXiv (n.d.).","ama":"Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2112.13558\">10.48550/arXiv.2112.13558</a>","apa":"Kavcic, B., &#38; Tkačik, G. (n.d.). Token-driven totally asymmetric simple exclusion process. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2112.13558\">https://doi.org/10.48550/arXiv.2112.13558</a>","mla":"Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” <i>ArXiv</i>, 2112.13558, doi:<a href=\"https://doi.org/10.48550/arXiv.2112.13558\">10.48550/arXiv.2112.13558</a>.","ista":"Kavcic B, Tkačik G. Token-driven totally asymmetric simple exclusion process. arXiv, 2112.13558.","chicago":"Kavcic, Bor, and Gašper Tkačik. “Token-Driven Totally Asymmetric Simple Exclusion Process.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2112.13558\">https://doi.org/10.48550/arXiv.2112.13558</a>."},"language":[{"iso":"eng"}],"status":"public","publication":"arXiv","oa":1},{"ddc":["570"],"quality_controlled":"1","publisher":"Springer Nature","doi":"10.1038/s41467-020-17734-z","article_processing_charge":"No","type":"journal_article","date_updated":"2024-03-25T23:30:05Z","_id":"8250","project":[{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions"},{"_id":"254E9036-B435-11E9-9278-68D0E5697425","grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF"}],"publication":"Nature Communications","status":"public","acknowledgement":"We thank M. Hennessey-Wesen, I. Tomanek, K. Jain, A. Staron, K. Tomasek, M. Scott,\r\nK.C. Huang, and Z. Gitai for reading the manuscript and constructive comments. B.K. is\r\nindebted to C. Guet for additional guidance and generous support, which rendered this\r\nwork possible. B.K. thanks all members of Guet group for many helpful discussions and\r\nsharing of resources. B.K. additionally acknowledges the tremendous support from A.\r\nAngermayr and K. Mitosch with experimental work. We further thank E. Brown for\r\nhelpful comments regarding lamotrigine, and A. Buskirk for valuable suggestions\r\nregarding the ribosome footprint size. This work was supported in part by Austrian\r\nScience Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844 (to G.T.),\r\nHFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG)\r\nstandalone grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG)\r\nCollaborative Research Centre (SFB) 1310 (to T.B.). Open access funding provided by\r\nProjekt DEAL.","date_published":"2020-08-11T00:00:00Z","external_id":{"isi":["000562769300008"]},"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"8657"}]},"isi":1,"year":"2020","intvolume":"        11","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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by “translation bottlenecks”: points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of “continuous epistasis” in bacterial physiology."}],"has_accepted_license":"1","publication_identifier":{"issn":["2041-1723"]},"publication_status":"published","file_date_updated":"2020-08-17T07:36:57Z","oa_version":"Published Version","title":"Mechanisms of drug interactions between translation-inhibiting antibiotics","author":[{"first_name":"Bor","orcid":"0000-0001-6041-254X","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","full_name":"Kavcic, Bor","last_name":"Kavcic"},{"last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"first_name":"Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Tobias","last_name":"Bollenbach"}],"day":"11","article_type":"original","date_created":"2020-08-12T09:13:50Z","volume":11,"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"mla":"Kavcic, Bor, et al. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” <i>Nature Communications</i>, vol. 11, 4013, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-17734-z\">10.1038/s41467-020-17734-z</a>.","apa":"Kavcic, B., Tkačik, G., &#38; Bollenbach, M. T. (2020). Mechanisms of drug interactions between translation-inhibiting antibiotics. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-17734-z\">https://doi.org/10.1038/s41467-020-17734-z</a>","ista":"Kavcic B, Tkačik G, Bollenbach MT. 2020. Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications. 11, 4013.","chicago":"Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-17734-z\">https://doi.org/10.1038/s41467-020-17734-z</a>.","short":"B. Kavcic, G. Tkačik, M.T. Bollenbach, Nature Communications 11 (2020).","ieee":"B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Mechanisms of drug interactions between translation-inhibiting antibiotics,” <i>Nature Communications</i>, vol. 11. Springer Nature, 2020.","ama":"Kavcic B, Tkačik G, Bollenbach MT. Mechanisms of drug interactions between translation-inhibiting antibiotics. <i>Nature Communications</i>. 2020;11. doi:<a href=\"https://doi.org/10.1038/s41467-020-17734-z\">10.1038/s41467-020-17734-z</a>"},"month":"08","file":[{"file_id":"8275","date_created":"2020-08-17T07:36:57Z","file_size":1965672,"creator":"dernst","date_updated":"2020-08-17T07:36:57Z","relation":"main_file","checksum":"986bebb308850a55850028d3d2b5b664","success":1,"file_name":"2020_NatureComm_Kavcic.pdf","access_level":"open_access","content_type":"application/pdf"}],"article_number":"4013","department":[{"_id":"GaTk"}]},{"status":"public","publication":"Proceedings of the National Academy of Sciences of the United States of America","pmid":1,"date_published":"2020-10-06T00:00:00Z","acknowledgement":"We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn for invaluable discussions of the learning rule. This work was supported by European Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12 (to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs. Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative Research in Computational Neuroscience) grant (to R.K. and E.S.).","year":"2020","isi":1,"external_id":{"isi":["000579045200012"],"pmid":["32948691"]},"page":"25066-25073","ddc":["570"],"quality_controlled":"1","doi":"10.1073/pnas.1912804117","article_processing_charge":"No","publisher":"National Academy of Sciences","date_updated":"2023-08-22T12:11:23Z","_id":"8698","type":"journal_article","oa":1,"language":[{"iso":"eng"}],"issue":"40","citation":{"ama":"Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic neural representations with randomly connected circuits. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2020;117(40):25066-25073. doi:<a href=\"https://doi.org/10.1073/pnas.1912804117\">10.1073/pnas.1912804117</a>","ieee":"O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic neural representations with randomly connected circuits,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 117, no. 40. National Academy of Sciences, pp. 25066–25073, 2020.","short":"O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of the National Academy of Sciences of the United States of America 117 (2020) 25066–25073.","chicago":"Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.1912804117\">https://doi.org/10.1073/pnas.1912804117</a>.","ista":"Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 25066–25073.","apa":"Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., &#38; Schneidman, E. (2020). Learning probabilistic neural representations with randomly connected circuits. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1912804117\">https://doi.org/10.1073/pnas.1912804117</a>","mla":"Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 117, no. 40, National Academy of Sciences, 2020, pp. 25066–73, doi:<a href=\"https://doi.org/10.1073/pnas.1912804117\">10.1073/pnas.1912804117</a>."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"10","department":[{"_id":"GaTk"}],"file":[{"file_id":"8713","date_updated":"2020-10-27T14:57:50Z","creator":"cziletti","file_size":1755359,"date_created":"2020-10-27T14:57:50Z","checksum":"c6a24fdecf3f28faf447078e7a274a88","relation":"main_file","content_type":"application/pdf","access_level":"open_access","file_name":"2020_PNAS_Maoz.pdf","success":1}],"has_accepted_license":"1","intvolume":"       117","tmp":{"image":"/images/cc_by_nc_nd.png","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)"},"abstract":[{"text":"The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.","lang":"eng"}],"publication_status":"published","publication_identifier":{"issn":["00278424"],"eissn":["10916490"]},"file_date_updated":"2020-10-27T14:57:50Z","author":[{"first_name":"Ori","last_name":"Maoz","full_name":"Maoz, Ori"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"first_name":"Mohamad Saleh","last_name":"Esteki","full_name":"Esteki, Mohamad Saleh"},{"last_name":"Kiani","full_name":"Kiani, Roozbeh","first_name":"Roozbeh"},{"first_name":"Elad","last_name":"Schneidman","full_name":"Schneidman, Elad"}],"scopus_import":"1","day":"06","title":"Learning probabilistic neural representations with randomly connected circuits","oa_version":"Published Version","volume":117,"article_type":"original","date_created":"2020-10-25T23:01:16Z"},{"external_id":{"pmid":["33268497"],"isi":["000600608300015"]},"related_material":{"link":[{"description":"News on IST Homepage","url":"https://ist.ac.at/en/news/new-compact-model-for-gene-regulation-in-higher-organisms/","relation":"press_release"}]},"year":"2020","isi":1,"project":[{"grant_number":"RGP0034/2018","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?","_id":"2665AAFE-B435-11E9-9278-68D0E5697425"},{"name":"Biophysically realistic genotype-phenotype maps for regulatory networks","_id":"267C84F4-B435-11E9-9278-68D0E5697425"}],"publication":"PNAS","status":"public","acknowledgement":"G.T. was supported by Human Frontiers Science Program Grant RGP0034/2018. R.G. was supported by the Austrian Academy of Sciences DOC Fellowship. R.G. thanks S. Avvakumov for helpful discussions.","date_published":"2020-12-15T00:00:00Z","pmid":1,"publisher":"National Academy of Sciences","doi":"10.1073/pnas.2006731117","article_processing_charge":"No","type":"journal_article","date_updated":"2023-08-24T11:10:22Z","_id":"9000","ddc":["570"],"page":"31614-31622","quality_controlled":"1","month":"12","file":[{"file_id":"9004","date_updated":"2021-01-11T08:37:31Z","creator":"dernst","file_size":1199247,"date_created":"2021-01-11T08:37:31Z","checksum":"69039cd402a571983aa6cb4815ffa863","relation":"main_file","access_level":"open_access","content_type":"application/pdf","success":1,"file_name":"2020_PNAS_Grah.pdf"}],"department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","issue":"50","citation":{"mla":"Grah, Rok, et al. “Nonequilibrium Models of Optimal Enhancer Function.” <i>PNAS</i>, vol. 117, no. 50, National Academy of Sciences, 2020, pp. 31614–22, doi:<a href=\"https://doi.org/10.1073/pnas.2006731117\">10.1073/pnas.2006731117</a>.","apa":"Grah, R., Zoller, B., &#38; Tkačik, G. (2020). Nonequilibrium models of optimal enhancer function. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2006731117\">https://doi.org/10.1073/pnas.2006731117</a>","ista":"Grah R, Zoller B, Tkačik G. 2020. Nonequilibrium models of optimal enhancer function. PNAS. 117(50), 31614–31622.","chicago":"Grah, Rok, Benjamin Zoller, and Gašper Tkačik. “Nonequilibrium Models of Optimal Enhancer Function.” <i>PNAS</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.2006731117\">https://doi.org/10.1073/pnas.2006731117</a>.","short":"R. Grah, B. Zoller, G. Tkačik, PNAS 117 (2020) 31614–31622.","ieee":"R. Grah, B. Zoller, and G. Tkačik, “Nonequilibrium models of optimal enhancer function,” <i>PNAS</i>, vol. 117, no. 50. National Academy of Sciences, pp. 31614–31622, 2020.","ama":"Grah R, Zoller B, Tkačik G. Nonequilibrium models of optimal enhancer function. <i>PNAS</i>. 2020;117(50):31614-31622. doi:<a href=\"https://doi.org/10.1073/pnas.2006731117\">10.1073/pnas.2006731117</a>"},"oa_version":"Published Version","title":"Nonequilibrium models of optimal enhancer function","author":[{"first_name":"Rok","orcid":"0000-0003-2539-3560","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","full_name":"Grah, Rok","last_name":"Grah"},{"first_name":"Benjamin","full_name":"Zoller, Benjamin","last_name":"Zoller"},{"last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","first_name":"Gašper","orcid":"0000-0002-6699-1455"}],"day":"15","scopus_import":"1","article_type":"original","date_created":"2021-01-10T23:01:17Z","volume":117,"tmp":{"image":"/images/cc_by_nc_nd.png","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)"},"abstract":[{"lang":"eng","text":"In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future."}],"intvolume":"       117","has_accepted_license":"1","publication_identifier":{"eissn":["10916490"],"issn":["00278424"]},"publication_status":"published","file_date_updated":"2021-01-11T08:37:31Z"},{"abstract":[{"lang":"eng","text":"Organisms cope with change by taking advantage of transcriptional regulators. However, when faced with rare environments, the evolution of transcriptional regulators and their promoters may be too slow. Here, we investigate whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. Using real-time monitoring of gene-copy-number mutations in Escherichia coli, we show that gene duplications and amplifications enable adaptation to fluctuating environments by rapidly generating copy-number and, therefore, expression-level polymorphisms. This amplification-mediated gene expression tuning (AMGET) occurs on timescales that are similar to canonical gene regulation and can respond to rapid environmental changes. Mathematical modelling shows that amplifications also tune gene expression in stochastic environments in which transcription-factor-based schemes are hard to evolve or maintain. The fleeting nature of gene amplifications gives rise to a generic population-level mechanism that relies on genetic heterogeneity to rapidly tune the expression of any gene, without leaving any genomic signature."}],"intvolume":"         4","has_accepted_license":"1","file_date_updated":"2020-10-09T09:56:01Z","publication_identifier":{"issn":["2397-334X"]},"publication_status":"published","oa_version":"Submitted Version","title":"Gene amplification as a form of population-level gene expression regulation","day":"01","scopus_import":"1","author":[{"orcid":"0000-0001-6197-363X","first_name":"Isabella","id":"3981F020-F248-11E8-B48F-1D18A9856A87","full_name":"Tomanek, Isabella","last_name":"Tomanek"},{"orcid":"0000-0003-2539-3560","first_name":"Rok","last_name":"Grah","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","full_name":"Grah, Rok"},{"last_name":"Lagator","full_name":"Lagator, M.","first_name":"M."},{"first_name":"A. M. C.","full_name":"Andersson, A. M. C.","last_name":"Andersson"},{"first_name":"Jonathan P","orcid":"0000-0002-4624-4612","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","full_name":"Bollback, Jonathan P","last_name":"Bollback"},{"last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"first_name":"Calin C","orcid":"0000-0001-6220-2052","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","last_name":"Guet"}],"date_created":"2020-04-08T15:20:53Z","article_type":"original","volume":4,"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"apa":"Tomanek, I., Grah, R., Lagator, M., Andersson, A. M. C., Bollback, J. P., Tkačik, G., &#38; Guet, C. C. (2020). Gene amplification as a form of population-level gene expression regulation. <i>Nature Ecology &#38; Evolution</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41559-020-1132-7\">https://doi.org/10.1038/s41559-020-1132-7</a>","mla":"Tomanek, Isabella, et al. “Gene Amplification as a Form of Population-Level Gene Expression Regulation.” <i>Nature Ecology &#38; Evolution</i>, vol. 4, no. 4, Springer Nature, 2020, pp. 612–25, doi:<a href=\"https://doi.org/10.1038/s41559-020-1132-7\">10.1038/s41559-020-1132-7</a>.","chicago":"Tomanek, Isabella, Rok Grah, M. Lagator, A. M. C. Andersson, Jonathan P Bollback, Gašper Tkačik, and Calin C Guet. “Gene Amplification as a Form of Population-Level Gene Expression Regulation.” <i>Nature Ecology &#38; Evolution</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41559-020-1132-7\">https://doi.org/10.1038/s41559-020-1132-7</a>.","ista":"Tomanek I, Grah R, Lagator M, Andersson AMC, Bollback JP, Tkačik G, Guet CC. 2020. Gene amplification as a form of population-level gene expression regulation. Nature Ecology &#38; Evolution. 4(4), 612–625.","ieee":"I. Tomanek <i>et al.</i>, “Gene amplification as a form of population-level gene expression regulation,” <i>Nature Ecology &#38; Evolution</i>, vol. 4, no. 4. Springer Nature, pp. 612–625, 2020.","short":"I. Tomanek, R. Grah, M. Lagator, A.M.C. Andersson, J.P. Bollback, G. Tkačik, C.C. Guet, Nature Ecology &#38; Evolution 4 (2020) 612–625.","ama":"Tomanek I, Grah R, Lagator M, et al. Gene amplification as a form of population-level gene expression regulation. <i>Nature Ecology &#38; Evolution</i>. 2020;4(4):612-625. doi:<a href=\"https://doi.org/10.1038/s41559-020-1132-7\">10.1038/s41559-020-1132-7</a>"},"issue":"4","month":"04","file":[{"success":1,"file_name":"2020_NatureEcolEvo_Tomanek.pdf","access_level":"open_access","content_type":"application/pdf","relation":"main_file","checksum":"ef3bbf42023e30b2c24a6278025d2040","date_created":"2020-10-09T09:56:01Z","file_size":745242,"date_updated":"2020-10-09T09:56:01Z","creator":"dernst","file_id":"8640"}],"department":[{"_id":"GaTk"},{"_id":"CaGu"}],"ddc":["570"],"page":"612-625","quality_controlled":"1","publisher":"Springer Nature","article_processing_charge":"No","doi":"10.1038/s41559-020-1132-7","type":"journal_article","_id":"7652","date_updated":"2024-03-25T23:30:20Z","publication":"Nature Ecology & Evolution","status":"public","project":[{"name":"Biophysically realistic genotype-phenotype maps for regulatory networks","_id":"267C84F4-B435-11E9-9278-68D0E5697425"}],"date_published":"2020-04-01T00:00:00Z","acknowledgement":"We thank L. Hurst, N. Barton, M. Pleska, M. Steinrück, B. Kavcic and A. Staron for input on the manuscript, and To. Bergmiller and R. Chait for help with microfluidics experiments. I.T. is a recipient the OMV fellowship. R.G. is a recipient of a DOC (Doctoral Fellowship Programme of the Austrian Academy of Sciences) Fellowship of the Austrian Academy of Sciences.","related_material":{"link":[{"description":"News on IST Homepage","url":"https://ist.ac.at/en/news/how-to-thrive-without-gene-regulation/","relation":"press_release"}],"record":[{"status":"public","relation":"dissertation_contains","id":"8155"},{"status":"public","relation":"research_data","id":"7383"},{"relation":"research_data","status":"public","id":"7016"},{"id":"8653","status":"public","relation":"used_in_publication"}]},"external_id":{"isi":["000519008300005"]},"isi":1,"year":"2020"},{"ddc":["570"],"quality_controlled":"1","publisher":"Frontiers","article_processing_charge":"No","doi":"10.3389/fncom.2020.00020","type":"journal_article","_id":"7656","date_updated":"2023-08-18T10:30:11Z","status":"public","publication":"Frontiers in Computational Neuroscience","date_published":"2020-03-13T00:00:00Z","pmid":1,"external_id":{"pmid":["32231528"],"isi":["000525543200001"]},"year":"2020","isi":1,"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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"intvolume":"        14","abstract":[{"text":"We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.","lang":"eng"}],"has_accepted_license":"1","file_date_updated":"2020-07-14T12:48:01Z","publication_identifier":{"eissn":["16625188"]},"publication_status":"published","oa_version":"Published Version","title":"Clustering of neural activity: A design principle for population codes","day":"13","scopus_import":"1","author":[{"first_name":"Michael J.","last_name":"Berry","full_name":"Berry, Michael J."},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper"}],"date_created":"2020-04-12T22:00:40Z","article_type":"original","volume":14,"language":[{"iso":"eng"}],"oa":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"short":"M.J. Berry, G. Tkačik, Frontiers in Computational Neuroscience 14 (2020).","ieee":"M. J. Berry and G. Tkačik, “Clustering of neural activity: A design principle for population codes,” <i>Frontiers in Computational Neuroscience</i>, vol. 14. Frontiers, 2020.","ama":"Berry MJ, Tkačik G. Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. 2020;14. doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>","mla":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>, vol. 14, 20, Frontiers, 2020, doi:<a href=\"https://doi.org/10.3389/fncom.2020.00020\">10.3389/fncom.2020.00020</a>.","apa":"Berry, M. J., &#38; Tkačik, G. (2020). Clustering of neural activity: A design principle for population codes. <i>Frontiers in Computational Neuroscience</i>. Frontiers. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>","ista":"Berry MJ, Tkačik G. 2020. Clustering of neural activity: A design principle for population codes. Frontiers in Computational Neuroscience. 14, 20.","chicago":"Berry, Michael J., and Gašper Tkačik. “Clustering of Neural Activity: A Design Principle for Population Codes.” <i>Frontiers in Computational Neuroscience</i>. Frontiers, 2020. <a href=\"https://doi.org/10.3389/fncom.2020.00020\">https://doi.org/10.3389/fncom.2020.00020</a>."},"month":"03","file":[{"relation":"main_file","checksum":"2b1da23823eae9cedbb42d701945b61e","file_name":"2020_Frontiers_Berry.pdf","content_type":"application/pdf","access_level":"open_access","file_id":"7659","date_created":"2020-04-14T12:20:39Z","file_size":4082937,"creator":"dernst","date_updated":"2020-07-14T12:48:01Z"}],"article_number":"20","department":[{"_id":"GaTk"}]}]