[{"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"title":"Novel plasticity rule can explain the development of sensorimotor intelligence","author":[{"last_name":"Der","full_name":"Der, Ralf","first_name":"Ralf"},{"full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"5601","external_id":{"pmid":["26504200"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1508400112.","ista":"Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.","mla":"Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112.","ieee":"R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015.","short":"R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.","apa":"Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1508400112","ama":"Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112"},"publisher":"National Academy of Sciences","quality_controlled":"1","oa":1,"date_published":"2015-11-10T00:00:00Z","doi":"10.1073/pnas.1508400112","date_created":"2018-12-11T11:52:47Z","page":"E6224 - E6232","day":"10","publication":"PNAS","year":"2015","status":"public","type":"journal_article","_id":"1570","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"date_updated":"2021-01-12T06:51:40Z","month":"11","intvolume":" 112","scopus_import":1,"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"pmid":1,"oa_version":"Submitted Version","abstract":[{"text":"Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no systemspecific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking,which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.","lang":"eng"}],"issue":"45","volume":112,"ec_funded":1,"language":[{"iso":"eng"}],"publication_status":"published"},{"abstract":[{"lang":"eng","text":"Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space."}],"oa_version":"None","alternative_title":["LNCS"],"scopus_import":1,"month":"09","intvolume":" 9308","publication_status":"published","language":[{"iso":"eng"}],"related_material":{"record":[{"relation":"later_version","id":"1148","status":"public"}]},"volume":9308,"ec_funded":1,"series_title":"Lecture Notes in Computer Science","_id":"1658","type":"conference","conference":{"name":"CMSB: Computational Methods in Systems Biology","start_date":"2015-09-16","end_date":"2015-09-18","location":"Nantes, France"},"status":"public","date_updated":"2023-02-21T16:17:24Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"Springer","quality_controlled":"1","year":"2015","day":"01","page":"77 - 89","date_published":"2015-09-01T00:00:00Z","doi":"10.1007/978-3-319-23401-4_8","date_created":"2018-12-11T11:53:18Z","project":[{"name":"Quantitative Reactive Modeling","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","grant_number":"Z211"},{"_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Rigorous Systems Engineering","grant_number":"S 11407_N23"},{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"citation":{"mla":"Bogomolov, Sergiy, et al. Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks. Vol. 9308, Springer, 2015, pp. 77–89, doi:10.1007/978-3-319-23401-4_8.","apa":"Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., & Schilling, C. (2015). Adaptive moment closure for parameter inference of biochemical reaction networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes, France: Springer. https://doi.org/10.1007/978-3-319-23401-4_8","ama":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. Adaptive moment closure for parameter inference of biochemical reaction networks. 2015;9308:77-89. doi:10.1007/978-3-319-23401-4_8","ieee":"S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive moment closure for parameter inference of biochemical reaction networks,” vol. 9308. Springer, pp. 77–89, 2015.","short":"S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.","chicago":"Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-319-23401-4_8.","ista":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. 2015. Adaptive moment closure for parameter inference of biochemical reaction networks. 9308, 77–89."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5492","author":[{"id":"369D9A44-F248-11E8-B48F-1D18A9856A87","first_name":"Sergiy","last_name":"Bogomolov","orcid":"0000-0002-0686-0365","full_name":"Bogomolov, Sergiy"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","orcid":"0000−0002−2985−7724","full_name":"Henzinger, Thomas A","last_name":"Henzinger"},{"last_name":"Podelski","full_name":"Podelski, Andreas","first_name":"Andreas"},{"first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess"},{"first_name":"Christian","last_name":"Schilling","full_name":"Schilling, Christian"}],"title":"Adaptive moment closure for parameter inference of biochemical reaction networks"},{"date_published":"2015-07-01T00:00:00Z","doi":"10.1371/journal.pcbi.1004304","date_created":"2018-12-11T11:53:31Z","day":"01","publication":"PLoS Computational Biology","has_accepted_license":"1","year":"2015","publisher":"Public Library of Science","quality_controlled":"1","oa":1,"acknowledgement":"This work was supported by grants EY 014196 and EY 017934 to MJB, ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010- 22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).","title":"High accuracy decoding of dynamical motion from a large retinal population","author":[{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"first_name":"Vicente","id":"421234E8-F248-11E8-B48F-1D18A9856A87","full_name":"Botella Soler, Vicente","orcid":"0000-0002-8790-1914","last_name":"Botella Soler"},{"first_name":"Kristina","last_name":"Simmons","full_name":"Simmons, Kristina"},{"first_name":"Thierry","last_name":"Mora","full_name":"Mora, Thierry"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"publist_id":"5447","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora, Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004304.","ista":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 11(7), e1004304.","mla":"Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology, vol. 11, no. 7, e1004304, Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004304.","ama":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 2015;11(7). doi:10.1371/journal.pcbi.1004304","apa":"Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., & Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004304","ieee":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” PLoS Computational Biology, vol. 11, no. 7. Public Library of Science, 2015.","short":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (2015)."},"project":[{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26"}],"article_number":"e1004304","issue":"7","volume":11,"file":[{"creator":"system","file_size":4673930,"date_updated":"2020-07-14T12:45:12Z","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf","date_created":"2018-12-12T10:16:25Z","relation":"main_file","access_level":"open_access","content_type":"application/pdf","checksum":"472b979f3f1cffb37b3e503f085115ca","file_id":"5212"}],"language":[{"iso":"eng"}],"publication_status":"published","month":"07","intvolume":" 11","scopus_import":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits."}],"department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:45:12Z","ddc":["570"],"date_updated":"2021-01-12T06:52:35Z","status":"public","pubrep_id":"455","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"_id":"1697"},{"year":"2015","day":"15","publication":"PNAS","page":"11508 - 11513","doi":"10.1073/pnas.1514188112","date_published":"2015-09-15T00:00:00Z","date_created":"2018-12-11T11:53:33Z","acknowledgement":"Research was supported in part by National Science Foundation Grants PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation, by the W. M. Keck Foundation, and by the Simons Foundation.","quality_controlled":"1","publisher":"National Academy of Sciences","oa":1,"citation":{"chicago":"Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1514188112.","ista":"Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37), 11508–11513.","mla":"Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS, vol. 112, no. 37, National Academy of Sciences, 2015, pp. 11508–13, doi:10.1073/pnas.1514188112.","apa":"Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., & Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1514188112","ama":"Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 2015;112(37):11508-11513. doi:10.1073/pnas.1514188112","ieee":"G. Tkačik et al., “Thermodynamics and signatures of criticality in a network of neurons,” PNAS, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015.","short":"G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"},{"full_name":"Mora, Thierry","last_name":"Mora","first_name":"Thierry"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"full_name":"Amodei, Dario","last_name":"Amodei","first_name":"Dario"},{"full_name":"Palmer, Stephanie","last_name":"Palmer","first_name":"Stephanie"},{"full_name":"Berry Ii, Michael","last_name":"Berry Ii","first_name":"Michael"},{"first_name":"William","full_name":"Bialek, William","last_name":"Bialek"}],"publist_id":"5440","external_id":{"pmid":["26330611"]},"title":"Thermodynamics and signatures of criticality in a network of neurons","project":[{"grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"publication_status":"published","language":[{"iso":"eng"}],"issue":"37","volume":112,"abstract":[{"lang":"eng","text":"The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. "}],"oa_version":"Submitted Version","pmid":1,"scopus_import":1,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/"}],"month":"09","intvolume":" 112","date_updated":"2021-01-12T06:52:37Z","department":[{"_id":"GaTk"}],"_id":"1701","type":"journal_article","status":"public"},{"scopus_import":1,"publisher":"ACM","quality_controlled":"1","intvolume":" 25","month":"02","abstract":[{"lang":"eng","text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance."}],"acknowledgement":"HYCON2; EC; European Commission\r\n","oa_version":"None","date_created":"2018-12-11T11:54:25Z","issue":"2","date_published":"2015-02-01T00:00:00Z","volume":25,"doi":"10.1145/2688906","year":"2015","publication_status":"published","publication":"ACM Transactions on Modeling and Computer Simulation","language":[{"iso":"eng"}],"day":"01","type":"journal_article","status":"public","_id":"1861","article_number":"8","author":[{"last_name":"Ruess","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob"},{"first_name":"John","full_name":"Lygeros, John","last_name":"Lygeros"}],"publist_id":"5238","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","citation":{"chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation. ACM, 2015. https://doi.org/10.1145/2688906.","ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8.","mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2, 8, ACM, 2015, doi:10.1145/2688906.","short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).","ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2. ACM, 2015.","ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 2015;25(2). doi:10.1145/2688906","apa":"Ruess, J., & Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. ACM. https://doi.org/10.1145/2688906"},"date_updated":"2021-01-12T06:53:41Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"}]