--- _id: '1197' abstract: - lang: eng text: Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina. acknowledgement: JSP was supported by a C.V. Starr Fellowship from the Starr Foundation (http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation (https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler for helpful comments on the manuscript. article_number: e1005855 author: - first_name: Jason full_name: Prentice, Jason last_name: Prentice - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Mark full_name: Ioffe, Mark last_name: Ioffe - first_name: Adrianna full_name: Loback, Adrianna last_name: Loback - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Michael full_name: Berry, Michael last_name: Berry citation: ama: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes of the retinal population code. PLoS Computational Biology. 2016;12(11). doi:10.1371/journal.pcbi.1005148 apa: Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M. (2016). Error-robust modes of the retinal population code. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005148 chicago: Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik, and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology. Public Library of Science, 2016. https://doi.org/10.1371/journal.pcbi.1005148. ieee: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust modes of the retinal population code,” PLoS Computational Biology, vol. 12, no. 11. Public Library of Science, 2016. ista: Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855. mla: Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology, vol. 12, no. 11, e1005855, Public Library of Science, 2016, doi:10.1371/journal.pcbi.1005148. short: J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational Biology 12 (2016). date_created: 2018-12-11T11:50:40Z date_published: 2016-11-17T00:00:00Z date_updated: 2023-02-23T14:05:40Z day: '17' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1005148 file: - access_level: open_access checksum: 47b08cbd4dbf32b25ba161f5f4b262cc content_type: application/pdf creator: kschuh date_created: 2019-01-25T10:35:00Z date_updated: 2020-07-14T12:44:38Z file_id: '5884' file_name: 2016_PLOS_Prentice.pdf file_size: 4492021 relation: main_file file_date_updated: 2020-07-14T12:44:38Z has_accepted_license: '1' intvolume: ' 12' issue: '11' language: - iso: eng month: '11' oa: 1 oa_version: Published Version project: - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: PLoS Computational Biology publication_status: published publisher: Public Library of Science publist_id: '6153' quality_controlled: '1' related_material: record: - id: '9709' relation: research_data status: public scopus_import: 1 status: public title: Error-robust modes of the retinal population code tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 12 year: '2016' ... --- _id: '948' abstract: - lang: eng text: Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations. acknowledgement: DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1 (JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement no. 291734 (CS) alternative_title: - Advances in Neural Information Processing Systems author: - first_name: Travis full_name: Monk, Travis last_name: Monk - first_name: Cristina full_name: Savin, Cristina id: 3933349E-F248-11E8-B48F-1D18A9856A87 last_name: Savin - first_name: Jörg full_name: Lücke, Jörg last_name: Lücke citation: ama: 'Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems; 2016:4285-4293.' apa: 'Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural Information Processing Systems.' chicago: Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information Processing Systems, 2016. ieee: 'T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity learn stimulus intensity statistics,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.' ista: 'Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 4285–4293.' mla: Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93. short: T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293. conference: end_date: 2016-12-10 location: Barcelona, Spaine name: 'NIPS: Neural Information Processing Systems' start_date: 2016-12-05 date_created: 2018-12-11T11:49:21Z date_published: 2016-01-01T00:00:00Z date_updated: 2021-01-12T08:22:08Z day: '01' department: - _id: GaTk ec_funded: 1 intvolume: ' 29' language: - iso: eng main_file_link: - url: https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics month: '01' oa_version: None page: 4285 - 4293 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication_status: published publisher: Neural Information Processing Systems publist_id: '6469' quality_controlled: '1' scopus_import: 1 status: public title: Neurons equipped with intrinsic plasticity learn stimulus intensity statistics type: conference user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 29 year: '2016' ... --- _id: '1270' abstract: - lang: eng text: A crucial step in the early development of multicellular organisms involves the establishment of spatial patterns of gene expression which later direct proliferating cells to take on different cell fates. These patterns enable the cells to infer their global position within a tissue or an organism by reading out local gene expression levels. The patterning system is thus said to encode positional information, a concept that was formalized recently in the framework of information theory. Here we introduce a toy model of patterning in one spatial dimension, which can be seen as an extension of Wolpert's paradigmatic "French Flag" model, to patterning by several interacting, spatially coupled genes subject to intrinsic and extrinsic noise. Our model, a variant of an Ising spin system, allows us to systematically explore expression patterns that optimally encode positional information. We find that optimal patterning systems use positional cues, as in the French Flag model, together with gene-gene interactions to generate combinatorial codes for position which we call "Counter" patterns. Counter patterns can also be stabilized against noise and variations in system size or morphogen dosage by longer-range spatial interactions of the type invoked in the Turing model. The simple setup proposed here qualitatively captures many of the experimentally observed properties of biological patterning systems and allows them to be studied in a single, theoretically consistent framework. acknowledgement: The authors would like to thank Thomas Sokolowski and Filipe Tostevin for helpful discussions. PH and UG were funded by the German Excellence Initiative via the program "Nanosystems Initiative Munich" (https://www.nano-initiative-munich.de) and the German Research Foundation via the SFB 1032 "Nanoagents for Spatiotemporal Control of Molecular and Cellular Reactions" (http://www.sfb1032.physik.uni-muenchen.de). GT was funded by the Austrian Science Fund (FWF P 28844) (http://www.fwf.ac.at). article_number: e0163628 author: - first_name: Patrick full_name: Hillenbrand, Patrick last_name: Hillenbrand - first_name: Ulrich full_name: Gerland, Ulrich last_name: Gerland - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: 'Hillenbrand P, Gerland U, Tkačik G. Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. 2016;11(9). doi:10.1371/journal.pone.0163628' apa: 'Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628' chicago: 'Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information.” PLoS One. Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.' ieee: 'P. Hillenbrand, U. Gerland, and G. Tkačik, “Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information,” PLoS One, vol. 11, no. 9. Public Library of Science, 2016.' ista: 'Hillenbrand P, Gerland U, Tkačik G. 2016. Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. 11(9), e0163628.' mla: 'Hillenbrand, Patrick, et al. “Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information.” PLoS One, vol. 11, no. 9, e0163628, Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.' short: P. Hillenbrand, U. Gerland, G. Tkačik, PLoS One 11 (2016). date_created: 2018-12-11T11:51:03Z date_published: 2016-09-27T00:00:00Z date_updated: 2023-02-23T14:11:37Z day: '27' ddc: - '571' department: - _id: GaTk doi: 10.1371/journal.pone.0163628 file: - access_level: open_access checksum: 3d0d55d373096a033bd9cf79288c8586 content_type: application/pdf creator: system date_created: 2018-12-12T10:10:47Z date_updated: 2020-07-14T12:44:42Z file_id: '4837' file_name: IST-2016-696-v1+1_journal.pone.0163628.PDF file_size: 4950415 relation: main_file file_date_updated: 2020-07-14T12:44:42Z has_accepted_license: '1' intvolume: ' 11' issue: '9' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: PLoS One publication_status: published publisher: Public Library of Science publist_id: '6050' pubrep_id: '696' quality_controlled: '1' related_material: record: - id: '9869' relation: research_data status: public - id: '9870' relation: research_data status: public - id: '9871' relation: research_data status: public scopus_import: 1 status: public title: 'Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 11 year: '2016' ... --- _id: '9870' abstract: - lang: eng text: The effect of noise in the input field on an Ising model is approximated. Furthermore, methods to compute positional information in an Ising model by transfer matrices and Monte Carlo sampling are outlined. article_processing_charge: No author: - first_name: Patrick full_name: Hillenbrand, Patrick last_name: Hillenbrand - first_name: Ulrich full_name: Gerland, Ulrich last_name: Gerland - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002 apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional information in an Ising model. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s002 chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in an Ising Model.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s002. ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in an Ising model.” Public Library of Science, 2016. ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in an Ising model, Public Library of Science, 10.1371/journal.pone.0163628.s002. mla: Hillenbrand, Patrick, et al. Computation of Positional Information in an Ising Model. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s002. short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016). date_created: 2021-08-10T09:23:45Z date_published: 2016-09-27T00:00:00Z date_updated: 2023-02-21T16:56:40Z day: '27' department: - _id: GaTk doi: 10.1371/journal.pone.0163628.s002 month: '09' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1270' relation: used_in_publication status: public status: public title: Computation of positional information in an Ising model type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2016' ... --- _id: '9869' abstract: - lang: eng text: A lower bound on the error of a positional estimator with limited positional information is derived. article_processing_charge: No author: - first_name: Patrick full_name: Hillenbrand, Patrick last_name: Hillenbrand - first_name: Ulrich full_name: Gerland, Ulrich last_name: Gerland - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position. 2016. doi:10.1371/journal.pone.0163628.s001 apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Error bound on an estimator of position. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s001 chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on an Estimator of Position.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s001. ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of position.” Public Library of Science, 2016. ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position, Public Library of Science, 10.1371/journal.pone.0163628.s001. mla: Hillenbrand, Patrick, et al. Error Bound on an Estimator of Position. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s001. short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016). date_created: 2021-08-10T08:53:48Z date_published: 2016-09-27T00:00:00Z date_updated: 2023-02-21T16:56:40Z day: '27' department: - _id: GaTk doi: 10.1371/journal.pone.0163628.s001 month: '09' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1270' relation: used_in_publication status: public status: public title: Error bound on an estimator of position type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2016' ...