{"ok":true,"nota":{"id":"d89e77b93d40760d","slug":"un-modelo-matematico-muestra-que-medir-la-complejidad-cerebral-depende-d89e77","titulo":"Un modelo matemático muestra que medir la complejidad cerebral depende de cómo están conectadas las redes neur","resumen":"Simularon redes neuronales con ecuaciones de campo medio, cambiando sistemáticamente sus conexiones, y compararon distintas métricas de complejidad. Buscaron entender qué rasgos de la arquitectura reflejan realmente esas medidas.","fuente":"biorxiv","fuenteNombre":"bioRxiv · preprints","color":"#be2628","autor":"Gaglioti, G., Cattani, A., Nieus, T., Momi, D., Comolatti, R., Dalla Porta, L., Sarasso, S., Massimini, M.","url":"https://www.biorxiv.org/content/10.64898/2026.10.21.760743v1?rss=1","imagen":"/hoy/portada/d89e77b93d40760d","tema":"ciencia","subtemas":["neurociencia computacional","complejidad cerebral","conectoma","metricas neuronales","conciencia"],"entidades":["bioRxiv"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-22T00:00:00.000Z","indexTs":"2026-10-22T10:38:41.125Z","hash":"0619882c2a2213873c5d2ebec9a6339399c9157219f28cdac21860bf42171219","tsa":{"time":"Oct 22 10:39:09 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"Estas métricas se usan cada vez más en investigación y en clínica para estimar el estado del cerebro a partir de registros de actividad. El trabajo sugiere que un mismo número puede significar cosas distintas según cómo está armada la red. Antes de confiar en un indicador, hay que saber qué mide realmente. Es un estudio de simulación, sin datos de pacientes.","quienFirma":"Preprint en bioRxiv (institución principal no identificada en la descripción)","confianza":"preprint","consulta":null},"sello":{"hash":"0619882c2a2213873c5d2ebec9a6339399c9157219f28cdac21860bf42171219","sello":"072d69a2333ad3e8ffc35d0bdf4fea69fb920b7f86a8a14cefe9628ff3440da7","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6574483,"aleatorio":"9801d1332798cbede2bf7a79e77928f4f345c08d34dd79d1c4c9c9c73783e9de","tiempo":"2026-10-22T10:38:30.000Z"},"nist":{"cadena":2,"pulso":1994441,"salida":"917B00588BBAF41DD8C461E7D93EA47DC73C00983776285722380061C2505D68D1C49D178A1E9E1A422EEA35DB6367141AB79828DD0F9592CE1805F752B02469","tiempo":"2026-10-22T10:32:00.000Z"},"noAntes":"2026-10-22T10:38:30.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 22 10:39:09 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"Inferring structural complexity from dynamical measures","descripcion":"Complexity, commonly defined as the coexistence of integration and differentiation, is a fundamental property of thalamocortical networks and is regarded as a prerequisite for adaptive brain function and consciousness. A variety of metrics have been developed to estimate neural complexity from recorded activity and are increasingly employed in both research and clinical settings. Yet, the extent to which these measures reflect specific architectural features of the underlying circuits remains poorly understood. Here, we used Wilson-Cowan mean-field equations to simulate neural dynamics across networks spanning a broad range of architectures. By systematically rewiring connectivity, we genera","pubDate":"2026-10-22","url":"https://www.biorxiv.org/content/10.64898/2026.10.21.760743v1?rss=1"},"indexado":"2026-10-22T10:38:41.125Z"}}