Exploring the multi-scale interactions of neural processes.

Our main interest is to

Develop computational methods and algorithms in Neuroscience and Medecine to analyze large data sets such as trajectories, time series, to model ionic interactions and to quantify spatial organization.

Develop Stochastic Modeling, Asymptotics, AI-Data-Driven Analysis, and Computational PDEs expansion.

Investigate brain computational & biophysical principles and functions across multiple scales

Quantify and predict the functionality of nano- and micro-domains in cell biology to brain dynamics using data science tools

Clinical applications and Medical predictions : We develop real-time based decision algorithm and methods to predict brain states during anesthesia, coma and tremor, sleep and during wakefullness.

Methods and Expertise :

Our expertise lies at the intersection of applied mathematics (stochastic modeling, asymptotic) data science and neuroscience, encompassing data modeling, statistical analysis, machine learning, numerical simulations, and algorithm development. We harness these methodologies to extract insights from diverse datasets, including fluorescence data, super-resolution single-particle trajectories, Hi-C analysis, calcium time series, and EEG databases, driving innovation in understanding brain function and its implications for healthcare.

Address :
46 Rue d'Ulm, 75005, Paris

Team leader :
Holcman David
Name of co-team leader :

Administrative Contact Name :
VILLA Tommaso

Website : Cliquez ici
Key words : #Modélisation neuronale multi-échelle #Intelligence artificielle en neurosciences #Processus stochastiques dans la dynamique cérébrale #Algorithmes prédictifs en temps réel #Simulations de réseaux neuronaux #Multiscale Neuronal Modeling #Artificial Intelligence in Neuroscience #Stochastic Processes in Brain Dynamics Real-Time #Predictive Algorithms Neural #Network reconstruction