My goal is to understand what is required for neuronal populations to be able to process information, then to be trained (for instance to reproduce perceptron-like capabilities).
Before turning to signal processing and learning, the NeuroPhysics team, where I’m working, focuses on the basic behavior of neuronal cultures. Indeed, the idea is to understand what drives collective behaviors of freely evolving cultures, so that we can obtain insights on how we can either exploit this intrinsic dynamics or prevent it from occuring.
To that end, I am using tools from dynamical systems and neuronal simulations to reproduce the experimental observation of our collaborators and other experimental teams around the world (see details on the Neuronal activity page).
Structuring the network seems a promising way to control the overall activity of a culture. Moreover, it is experimentally feasible through microfabrication, which is why we are considering it of prime importance. In order to predict the activity of such patterned culture and to compare our simulations to experimental observations, I am developing a parallel simulator to model the growth of neurons inside a culture (see Growing neurons page).
Using realistic network structures on which I will simulate neuronal activity, I aim at predicting structures that would allow for efficient separation of different input signals and learning.
Update the manuscript is now online! You can find it here.