To develop a computational brain tool as an intermediate layer between high-level features (behavioral and cognitive functions at the human level) and low-level features (biological layers studied in mice, i.e., an animal brain that can be a model of human brain, as well as modifications observed in pathologies).
To achieve this goal, we will focus on the hippocampus and the cortex, two brain structures highly implicated in learning. Here, learning is identified as the core integrative concept of the brain, but its complex processing within neuronal networks is still little understood. Using previous data obtained by biologists in this consortium and new biological data specifically obtained during this project, learning rules will be mathematized and implemented from synapses to behavioral and cognitive functions. In the long term, the computational brain can be conceived as a new local research tool providing in silico experiments to complete brain biocognitive data providing insights in neuronal learning modeling. In particular, this new tool will be used to study corresponding diseases (Alzheimer and epilepsy), to foster pedagogical innovation, and to develop new machine learning algorithms and artificial intelligence. 
More specifically, our common tasks are to: 
  • Dispose on the Côte d'Azur of a new in vivo biocomputational platform for cognitive/behavioral experiments to correlate the activity of local neuronal circuits (local field potential (LFP), single-unit recordings) with global measurements achieved by electroencephalography (EEG) and behavioral actions achieved at mouse/human levels.
  • Model human learning mechanisms based on specific neuronal circuits with biologically-realistic synaptic learning.
  • Achieve a new efficient reusable neurocognitive machine learning algorithm.
  • Dispose of a mathematical reusable model of neurocognitive machine learning and use it to extend current deep learning algorithms, e.g., testing their mathematical realization to electronic gates.
At the national level, the strengths of UCAJEDI concern mathematics, computer science, and biology. Fully integrated in this perspective, we propose ComputaBrain, which federates the main local actors in the domains of neurobiology, mathematics, computer science, and cognition (IBV, IPMC, LJAD, I3S, INRIA and BCL) through this unique opportunity. This consortium constitutes a strong local concentration of modelers and simulationists collaborating via this transdisciplinary project.