{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T17:11:13Z","timestamp":1773940273006,"version":"3.50.1"},"reference-count":0,"publisher":"MIT Press - Journals","issue":"6","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,5,13]]},"abstract":"<jats:p>An emerging paradigm proposes that neural computations can be understood at the level of dynamic systems that govern low-dimensional trajectories of collective neural activity. How the connectivity structure of a network determines the emergent dynamical system, however, remains to be clarified. Here we consider a novel class of models, gaussian-mixture, low-rank recurrent networks in which the rank of the connectivity matrix and the number of statistically defined populations are independent hyperparameters. We show that the resulting collective dynamics form a dynamical system, where the rank sets the dimensionality and the population structure shapes the dynamics. In particular, the collective dynamics can be described in terms of a simplified effective circuit of interacting latent variables. While having a single global population strongly restricts the possible dynamics, we demonstrate that if the number of populations is large enough, a rank R network can approximate any R-dimensional dynamical system.<\/jats:p>","DOI":"10.1162\/neco_a_01381","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:34:37Z","timestamp":1618446877000},"page":"1572-1615","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":82,"title":["Shaping Dynamics With Multiple Populations in Low-Rank Recurrent Networks"],"prefix":"10.1162","volume":"33","author":[{"given":"Manuel","family":"Beiran","sequence":"first","affiliation":[{"name":"Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure. PSL University, 75005 Paris, France manuel.beiran@ens.fr"}]},{"given":"Alexis","family":"Dubreuil","sequence":"additional","affiliation":[{"name":"Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure. PSL University, 75005 Paris, France alexis.dubreuil@gmail.com"}]},{"given":"Adrian","family":"Valente","sequence":"additional","affiliation":[{"name":"Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure. PSL University, 75005 Paris, France adrian.valente@ens.fr"}]},{"given":"Francesca","family":"Mastrogiuseppe","sequence":"additional","affiliation":[{"name":"Gatsby Computational Neuroscience Unit, UCL, London W1T 4JG, U.K. f.mastrogiuseppe@ucl.ac.uk"}]},{"given":"Srdjan","family":"Ostojic","sequence":"additional","affiliation":[{"name":"Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure. PSL University, 75005 Paris, France srdjan.ostojic@ens.fr"}]}],"member":"281","published-online":{"date-parts":[[2021,5,13]]},"container-title":["Neural Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/direct.mit.edu\/neco\/article-pdf\/33\/6\/1572\/1916378\/neco_a_01381.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/direct.mit.edu\/neco\/article-pdf\/33\/6\/1572\/1916378\/neco_a_01381.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T20:03:45Z","timestamp":1621281825000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/neco\/article\/33\/6\/1572\/98291\/Shaping-Dynamics-With-Multiple-Populations-in-Low"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,13]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,5,13]]},"published-print":{"date-parts":[[2021,5,13]]}},"URL":"https:\/\/doi.org\/10.1162\/neco_a_01381","relation":{},"ISSN":["0899-7667","1530-888X"],"issn-type":[{"value":"0899-7667","type":"print"},{"value":"1530-888X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,6]]},"published":{"date-parts":[[2021,5,13]]}}}