BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//6.4.7.2//EN TZID:Europe/Paris X-WR-TIMEZONE:Europe/Paris BEGIN:VEVENT UID:0-542@lptms.universite-paris-saclay.fr DTSTART:20180206T110000Z DTEND:20180206T120000Z DTSTAMP:20180129T115631Z URL:http://www.lptms.universite-paris-saclay.fr/seminars/seminaire-du-lptm s-beatriz-seoane-bartolome-3/ SUMMARY:Séminaire du LPTMS: Beatriz Seoane Bartolomé &\; Ulisse Ferrar i - LPTMS\, salle 201\, 2ème étage\, Bât 100\, Campus d'Orsay - 6 Fév 18 11:00 DESCRIPTION:Phase transitions in computer simulations : the Tethered Monte Carlo method\n\nBeatriz Seoane Bartolomé (LPT-ENS\, Paris)\nIn this talk\ , I will present a powerful Monte Carlo method that I developed during my PhD [1\,2] and extended recently [3]\, designed to efficiently study phase transitions at equilibrium. The principle is very simple\, by means of ex ternal constraints to the system\, we are able to avoid the traditional cr itical (exponential) slowing down associated to the second (first) order t ransition\, and thus reach much larger system sizes than with traditional methods. Furthermore\, the reconstruction of the constrained free energy i s much simpler than in other similar methods\, such as the famous Umbrella Sampling\, allowing us to both fix multiple constraints at the same time\ , and to extract magnitudes such as the inter-facial free energy with an u nusual high precision. In particular\, I will discuss the Tethered Monte C arlo strategy in the context of a toy model for crystalline porous media [ 3].\n[1] J. Stat. Phys. 144\, 554 (2011).\n[2] Phys. Rev. Lett. 108\, 1657 01 (2012).\n[3] The Journal of Chemical Physics 147 \, 084704 (2017).\n\n\ n\nStatistical Physics-inspired models of biological network: collective b ehavior in neuronal ensembles\nUlisse Ferrari (Institut de la Vision\, Ins erm &\; UPMC)\nIn both cortices and sensory systems\, information is re presented and transmitted through the correlated activity of large neurona l networks. Neurons\, in fact\, do not work independently: each of them dr ives the activity of the others\, thus working as a collective ensemble. M ethods borrowed from Statistical Physics and Machine Learning are powerful tools for characterizing the collective behavior of large systems and hen ce offer promising approaches to understand the activity of neuronal popul ations. In this talk I will show how the Maximum Entropy principle\, appli ed to cortical in-vivo recording\, allows for characterizing and comparing the population behavior during wakefulness and deep sleep. Then\, I will use hidden-layer models\, point processes and “experimental” linear re sponse theory to account for non-linear stimulus processing in sensory net works\, such as the retina. These approaches allow for constructing high p erforming models of the retinal population response to visual stimuli and thus for characterizing how a network of neurons can encode and transmit v isual information. CATEGORIES:seminars LOCATION:LPTMS\, salle 201\, 2ème étage\, Bât 100\, Campus d'Orsay\, 15 Rue Georges Clemenceau\, Orsay\, 91405\, France GEO:48.698185;2.181768 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=15 Rue Georges Clemenceau\, Orsay\, 91405\, France;X-APPLE-RADIUS=100;X-TITLE=LPTMS\, salle 201\, 2è me étage\, Bât 100\, Campus d'Orsay:geo:48.698185,2.181768 END:VEVENT END:VCALENDAR