Being able to emulate the human brain (or approximations or parts of it) is one of the next major biological/technological breakthroughs that will have impacts in our daily life (health, autonomous machines, artificial intelligence, etc.). If recent progresses in machine learning have roots in bio-inspired techniques, mostly dated from the 70's, they usually represent very simple and specific structures without catching the essence of structure-function brain mechanisms. In machine learning, computer scientists build usually bio-inspired algorithms but not transdisciplinary biocognitive algorithms. In biology and cognition, researches are limited by the lack of experimental data (only lacunar data can be collected on animals and even less on humans). Our main goal is 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 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 in Nice Sophia Antipolis 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, 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 trans-disciplinary project.


The project is divided into 5 major work packages (WP). Consortium interactions between biocognitive experimental levels and mathematical/computational modeling is shown in Figure below.

ComputaBrain Project Structure

In both cognitive and neurobiological sciences, experiments are already performed in Nice Sophia Antipolis at different scales (from molecular mechanisms to individual behaviors) using different devices (whole-cell recording, Multi-Electrode Arrays (MEA), Local Field Potential (LFP), Electroencephalography (EEG), learning tasks, etc.). Most of these devices are used at animal scale (mouse), but some are also used at human scale (EEG, response times, error rates). Our goal is to extend current local biological experimental techniques to be able to fully and multimodally collect data from learning mice and to bridge the gap with human behavior and cognition through neural network modeling. Methodologically, the biocognitive experiments will be set in a microscale (synapse, neuron, circuits) to full organism approach. To increase pertinence of work, comparable experiments will be defined at behavioral cognitive level between mice and humans (cf. “Learning behaviors”). Corresponding circuits (focusing on hippocampus and cortex) will be identified to set experiments at this microscale level. Modelers will constantly interact with biologists and cognitivists to ensure the adequacy between the data to be provided and both analysis and modeling.

BioMap Experimental Platform

The modeling approach of ComputaBrain relies on the data collected in the BioMap experimental platform, from local neuronal circuits (local field potential (LFP), single-unit recordings) and global measurements achieved by electroencephalography (EEG) to behavioral actions achieved at mouse/human levels.Coordinators of this platform (Ingrid Bethus and Fabrizio Capitano).

At synaptic and local neuronal network levels, the synaptic responses are studied from ex vivo hippocampal  brain slices:

At EEG and brain substructure levels, signals are recorded chronically from the surface of the brain or within a targeted structure in control and disease model (in particular epileptic) mice:

At biocognitive learning level, data are collected in vivo from mice performing behavioral tasks (in particular learning tasks), in a T-maze:

Partners' expertise

At IPMC, Hélène Marie/Jacques Barik's team is a long-term expert in mechanisms of synaptic and neuronal plasticity associated to learning using whole-cell patch clamping on the one hand (e.g., Marie et al., 2005; Middei et al., 2013) and spatial organism-level learning using rodent behavioral tasks (e.g., Tse et al., 2007; Bethus et al. 2010) on the other hand, but no experimental links currently exist within the team between these two levels (purpose of the new IPMC platform proposed here). Massimo Mantegazza's team is expert in neuron excitability investigations at the single cell level by whole-cell recordings and in studies of neuronal activity by EEG recordings in rodents (Heidrich et al., 2014; Cestele et al 2013; Liautard et al., 2013, 2014). Both Hélène Marie/Jacques Barik and Massimo Mantegazza teams will provide knowledge on biological mechanisms of brain circuits in healthy and disease conditions (Alzheimer, epilepsy) (eg. Marchetti et al. 2011; Guerrini et al. 2014). At iBV, Michel Studer's team will provide knowledge on cortical circuit development and assembly (Alfano et al., 2014; Harb et al., 2016) and is currently developing microcircuit analysis ex vivo using multi-electrode arrays (MEA) in collaboration with LJAD and I3S. At I3S, Alexandre Muzy's project recently extended (theoretically and experimentally) the theory of sequential machines using a new kind of algorithms correlating the activity of structure-based components with the whole network behavior (Muzy & Zeigler, 2014a, 2014b) but lacking theoretical proofs for complex network structures. Alexandre Muzy's project, Modélisation, Simulation & Neurocognition (MS&N) (I3S and LJAD), emerged from previous project Bio-info at I3S. At LJAD, in NeuroStatMod, researchers have expertise in sequential learning and optimization (Devaine et al., 2013), which allows comparing theoretically learning algorithms but lacks network structures. Besides, several statistical methods have been developed to analyze neuronal activity, notably for single unit recording (Tuleau-Malot et al., 2014; Reynaud-Bouret et al., 2014; Albert et al., 2015; 2016). These methods will require renewed efforts in order to be applied on massive recordings, as MEA and secondarily for single unit recording on freely moving animals. Other approaches will be pursued in order to develop new statistical methods for spike sorting and LFP/single unit recordings correlations. ComputaBrain will also collaborate with LJAD through the proposal “Factorisation de matrices pour le traitement de données biomédicales” submitted to the IDEX AAP Data Sciences, for spike sorting and neuronal graph reconstruction. At INRIA, deterministic models are developed from synapses to learning of sequences of items in memory, and multilevel statistical analysis is well established. Network dynamics is also an area of expertise, in close collaboration with LJAD researchers (equivariant and heteroclinic dynamics (Chossat et al., 2016), slow-fast systems (Desroches et al., 2016)). At BCL, human behavior and cognitive processes are investigated experimentally and modeled by biologically inspired networks of the cerebral cortex (Brunel & Lavigne, 2009; Lavigne et al., 2011). However, cognitive modeling still lacks statistical models of the data and also biological data on synaptic learning. The collaboration between partners aims at filling all these gaps. External collaborations with UCC and UofA will provide additional knowledge for the mathematical analysis of learning and processing of concepts. 


Based on a local integration between biological/statistical/computer science/cognition laboratories, scientific impacts consist of:
1. Provide a multilevel bio-analysis of the specific networks implicated in cognitive networks (extending current competences to network analysis),
2. Optimize the statistical analysis of biological data to enhance pertinence,
3. Development of biologically-realistic in silico models of synapse and neuron function,
4. Mathematize brain network-based learning.

These goals allow reaching the perspectives described by the work packages, opening new local opportunities to develop new machine learning algorithms, allow further electronic implementations, study brain diseases (as Alzheimer and epilepsy) and determine corresponding therapeutics, as well as providing new educational research-based advantages to UCA Jedi .


Members1 members2

The main purpose of ComputaBrain is to bring together the local technical and human competences on learning & cognition and promote interdisciplinary work.

35 collaborators of 6 local institutes (IPMC, INRIA Sophia, I3S, IBV and BCL). 

Principal investigators: Alexandre Muzy (I3S) and Hélène Marie (IPMC).



Role in ComputaBrain

Alexandre Muzy (AM)


Mathematical analysis of sequential machines and activity-based learning/simulation

Gilles Bernot (GB)


Validation of the results in formal methods (logics)

Frédéric Precioso (FP)


Validation of the results in deep learning

Goeffrey Portelli (GP)


Validation of the results in deep learning


Guinaudeau (OG)


Study of dendrite impact in activity measures

Cyrille Marscat (CM)


Neuron synchronization modeling

Hélène Marie (HM)


Biological analysis of synaptic and neuronal plasticity

Paula Pousinha (PP)


Biological analysis of synaptic and neuronal plasticity

Ingrid Bethus (IB)


Biological learning and in vivo single unit activity (coordinator of BioMap Experimental Platform)

Massimo Mantegazza (MM)


Biological analysis of neuronal excitability

Fabrice Duprat (FD)


Biological analysis of neuronal excitability

Isabelle Lena (IL)


Biological analysis of neuronal excitability and behavior

Michele Studer (MS)


Biological analysis of neuronal assemblies in ex vivo experiments

Alessandro Simi (AS)


Biological analysis of neuronal assemblies in ex vivo experiments

Karim Lounici (KL)


Mathematical modeling of sequential learning

Franck Grammont (FG)


Mathematical modeling of neuronal assemblies in ex vivo experiments

Patricia Reynaud-Bouret (PR)


Mathematical modeling of sequential learning

Pascal Chossat (PC)


Sequential activation of concepts based on heteroclinic chains

Romain Veltz (RV)


Synaptic plasticity in silico modeling

Maureen Clerc (MC)


Coherency of multiscale data

Theo Papadopoulo (TP)


Coherency of multiscale data

Mathieu Desroches (MD)


Excitability in silico modeling

Elif Köksal Ersöz (EK)


Modeling of associative memory

Frédéric Lavigne (FL)


Human cognitive modeling

Fanny Meunier (FM)


Human cognitive modeling

Maciej (Martin) Krupa (MK)


Sequential activation on concepts based on heteroclinic chains

External collaborators bring very specific expertise in the project:



Role in Computabrain

David RC Hill

Université Clermont Auvergne

High Performance Computing

Francesca Sargolini

Laboratoire de Neurosciences cognitives, Marseille

Rat experiments an cognitive processes involved in a learning task (funded by the DYNAMO project from MI CNRS)


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