Mathematically, agents can be represented either explicitly as individuals with their dynamics modelled e.g. The dynamics of complex physical or biophysical phenomena involves many agents, e.g. Historically, the BANG team, headed by Benoît Perthame during 11 years (2003-2013), has developed models, simulations and numerical algorithms for problems involving dynamics of Partial Differential Equations (PDEs). The MAMBA (Modelling and Analysis in Medical and Biological Applications) team is the continuation of the BANG (Biophysics, Numerical Analysis and Geophysics) team, which itself was a continuation of the former project-team M3N. Neuroscience and cognitive scienceġ Team members, visitors, external collaborators Research Scientists Other Research Topics and Application Domains 10.1.6 Leadership within the scientific community.10.1.5 Participation to scientific events.Member of the conference program committees.9.2.2 ITMO Cancer 2016 - 2020, HTE call (heterogeneity of tumours in their ecosystems).9.1.2 Participation in other international programs.9.1.1 Inria associate team not involved in an IIL.8 Bilateral contracts and grants with industry.7.9.4 Early morphogenesis of rod-shaped bacteria (M.7.9.3 Large-scale dynamics of self-propelled particles moving through obstacles.7.9.2 Kinetic approach to the collective dynamics of the rock-paper-scissors binary game.7.9.1 Collective dynamics with time-varying weights.
7.2.6 Stochastic Chemical Networks (L.7.2.5 The Stability of Non-Linear Hawkes Processes (Ph.7.2.4 Movement Disorders Analysis Using a Deep Learning Approach (C. Desjardins, Q. Salardaine, G. Vignoud, B. Degos).7.2.3 A synaptic theory for procedural and sequence learning in the striatum (G. Vignoud, J.D. Touboul (Brandeis University), L. Venance (Collège de France)).7.2.2 Online Sequence Learning In The Striatum With Anti-Hebbian Spike-Timing-Dependent Plasticity (G. Vignoud, J.D. Touboul (Brandeis University), L. Venance (Collège de France)).7.2.1 Stochastic models for spike-timing dependent plasticity (Ph. Robert and G. Vignoud).7.2 Stochastic Models of Biological Systems.7.1.4 Insights into protein filament division (M.7.1.3 Estimating the division rate from indirect measurements of single cells (M. Doumic, A. Olivier).7.1.2 Oscillatory asymptotic behaviour of structured-population equations (M.7.1.1 Heterogeneous aggregation: application to autophagy (J.7.1 Direct and inverse Problems in Structured-population equations.4.4 Applicative axis 3: Modelling and control in mathematical epidemiology.4.3 Applicative axis 2: Growth, evolution and regeneration in populations and tissues.4.2 Applicative axis 1: Focus on cancer.
1 Team members, visitors, external collaborators.