{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:31:11Z","timestamp":1777548671894,"version":"3.51.4"},"reference-count":35,"publisher":"MIT Press - Journals","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2009,4]]},"abstract":"<jats:p> Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly\u2013like and synfire chain\u2013like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view. <\/jats:p>","DOI":"10.1162\/neco.2008.03-08-727","type":"journal-article","created":{"date-parts":[[2008,10,17]],"date-time":"2008-10-17T15:27:25Z","timestamp":1224257245000},"page":"1038-1067","source":"Crossref","is-referenced-by-count":49,"title":["Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity"],"prefix":"10.1162","volume":"21","author":[{"given":"Takuma","family":"Tanaka","sequence":"first","affiliation":[{"name":"Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takeshi","family":"Kaneko","sequence":"additional","affiliation":[{"name":"Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan, and CREST, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshio","family":"Aoyagi","sequence":"additional","affiliation":[{"name":"Department of Applied Analysis and Complex Dynamical Systems, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan, and CREST, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/S0079-6123(06)65002-4"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511574566"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1088\/0954-898X\/3\/2\/009"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760805368"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.59.381"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.23-35-11167.2003"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.6.1129"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/S0042-6989(97)00121-1"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1152\/jn.01250.2004"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1038\/nature01614"},{"key":"B11","volume-title":"Elements of information theory","author":"Cover T. 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