{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/112429"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/112429","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Effects of neuronal correlations on population decoding and encoding models","abstract":"In this thesis, we analyze the effect of the correlations in neural activity on the information that is encoded in and can be decoded from a population of neurons. Various noise models describing these correlations are considered - in particular, we use models that take into account the pairwise correlations and other, simpler models that assume shared global additive and/or multiplicative noise factors. The performance of these models on firing rate prediction (encoding) and population decoding are studied. Our analyses show a significant beneficial effect of pairwise correlations on encoding models, with much of this benefit being explained by the global noise models. However, the effects of correlations on decoding vary among our datasets, providing an empirical justification to the theoretical results suggesting correlations can be either helpful or harmful to decoding.","abstract_html":"In this thesis, we analyze the effect of the correlations in neural activity on the information that is encoded in and can be decoded from a population of neurons. Various noise models describing these correlations are considered - in particular, we use models that take into account the pairwise correlations and other, simpler models that assume shared global additive and/or multiplicative noise factors. The performance of these models on firing rate prediction (encoding) and population decoding are studied. Our analyses show a significant beneficial effect of pairwise correlations on encoding models, with much of this benefit being explained by the global noise models. However, the effects of correlations on decoding vary among our datasets, providing an empirical justification to the theoretical results suggesting correlations can be either helpful or harmful to decoding.","abstract_has_math":false,"creators":["Patel, Ami (Ami M.)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Thesis pagination reflects the way it was delivered to the Institute Archives and Special Collections, MIT.","Includes bibliographical references (pages 71-73)."]},{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, we analyze the effect of the correlations in neural activity on the information that is encoded in and can be decoded from a population of neurons. Various noise models describing these correlations are considered - in particular, we use models that take into account the pairwise correlations and other, simpler models that assume shared global additive and/or multiplicative noise factors. The performance of these models on firing rate prediction (encoding) and population decoding are studied. Our analyses show a significant beneficial effect of pairwise correlations on encoding models, with much of this benefit being explained by the global noise models. However, the effects of correlations on decoding vary among our datasets, providing an empirical justification to the theoretical results suggesting correlations can be either helpful or harmful to decoding."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Effects of neuronal correlations on population decoding and encoding models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Tomaso Poggio."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Patel, Ami (Ami M.)"],"dc:date.accessioned":["2017-12-05T19:12:26Z"],"dc:date.available":["2017-12-05T19:12:26Z"],"dc:date.issued":["2013"],"dc:description":["Thesis: M. 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Our analyses show a significant beneficial effect of pairwise correlations on encoding models, with much of this benefit being explained by the global noise models. However, the effects of correlations on decoding vary among our datasets, providing an empirical justification to the theoretical results suggesting correlations can be either helpful or harmful to decoding."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/112429"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. 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