{"paper":{"title":"Disambiguating the role of noise correlations when decoding neural populations together","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","q-bio.QM","stat.AP"],"primary_cat":"q-bio.NC","authors_text":"Hugo Gabriel Eyherabide","submitted_at":"2016-08-19T06:00:22Z","abstract_excerpt":"One of the most controversial problems in neural decoding is quantifying the information loss caused by ignoring noise correlations during optimal brain computations. For more than a decade, the measure here called $ \\Delta I^{DL} $ has been believed exact. However, we have recently shown that it can exceed the information loss $ \\Delta I^{B} $ caused by optimal decoders constructed ignoring noise correlations. Unfortunately, the different information notions underlying $ \\Delta I^{DL} $ and $ \\Delta I^{B} $, and the putative rigorous information-theoretical derivation of $ \\Delta I^{DL} $, bo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05501","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}