RESEARCH CENTRE

Inria Centre
at the University of Bordeaux

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

Université de Bordeaux, CNRS, Institut
Polytechnique de Bordeaux

Project-Team

MNEMOSYNE

Mnemonic Synergy

IN COLLABORATION WITH: Laboratoire Bordelais de Recherche en
Informatique (LaBRI)

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Neuroscience and
Medicine

Contents

Project-Team MNEMOSYNE

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Integrative and Cognitive Neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Computational Neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Autonomous Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Applications in life sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Application in digital sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Applications in human sciences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 New software, platforms, open data

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7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.1 ReservoirPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.2 Neurosmart
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.3 AIDElibs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

8 New results

12
8.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.2 Learning mechanisms and collaborative mnesic functions
. . . . . . . . . . . . . . . . . . . 12
8.3 Cognitive control
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.4 Integrating abstract symbolic knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.5 Integrating oscillations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.6 Language processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

9 Bilateral contracts and grants with industry

14
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

10 Partnerships and cooperations

14
10.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.1.1 Associate Teams in the framework of an Inria International Lab or in the framework

10.2 International research visitors

10.2.1 Visits to international teams

of an Inria International Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1.2 Inria associate team not involved in an IIL or an international program . . . . . . . . 15
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.3.1 ANR SOMA (PRCI)
10.3.2 ANR DeepPool (JCJC)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.3.3 Exploratory action BrainGPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

10.3.4 Exploratory action AIDE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.4 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.4.1 Observatory of surveillance in democracy . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.4.2 Hyperhum@in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.4.3 RT-HippoNeuroStim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.4.4 Ecomob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.4.5 PsyPhINe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
. . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.4.6 Regional and local Research Networks

11 Dissemination

20
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.1.3 Journal
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.1.5 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
11.1.6 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.3.1 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.3.2 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.3.3 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

12 Scientific production

23
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
12.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
12.4 Cited publications

Project MNEMOSYNE

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Project-Team MNEMOSYNE

Creation of the Project-Team: 2014 July 01

Keywords

Computer sciences and digital sciences

A1.1.12. – Non-conventional architectures

A1.5. – Complex systems

A3.1.1. – Modeling, representation

A3.1.7. – Open data

A3.2.2. – Knowledge extraction, cleaning

A3.2.5. – Ontologies

A3.3. – Data and knowledge analysis

A3.3.2. – Data mining

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A3.4.3. – Reinforcement learning

A3.4.4. – Optimization and learning

A3.4.6. – Neural networks

A3.4.8. – Deep learning

A5.1.1. – Engineering of interactive systems

A5.1.2. – Evaluation of interactive systems

A5.2. – Data visualization

A5.3.3. – Pattern recognition

A5.4.1. – Object recognition

A5.4.2. – Activity recognition

A5.7.1. – Sound

A5.7.3. – Speech

A5.7.4. – Analysis

A5.8. – Natural language processing

A5.9.1. – Sampling, acquisition

A5.10.5. – Robot interaction (with the environment, humans, other robots)

A5.10.7. – Learning

A5.10.8. – Cognitive robotics and systems

A5.11.1. – Human activity analysis and recognition

A7.1. – Algorithms

A9.2. – Machine learning

A9.5. – Robotics

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Inria Annual Report 2023

Other research topics and application domains

B1.2. – Neuroscience and cognitive science

B1.2.1. – Understanding and simulation of the brain and the nervous system

B1.2.2. – Cognitive science

B2.2.6. – Neurodegenerative diseases

B8.5.2. – Crowd sourcing

B9.1.1. – E-learning, MOOC

B9.5.1. – Computer science

B9.6.8. – Linguistics

B9.7. – Knowledge dissemination

B9.8. – Reproducibility

B9.11.1. – Environmental risks

Project MNEMOSYNE

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1 Team members, visitors, external collaborators

Research Scientists

• Frederic Alexandre [Team leader, INRIA, Senior Researcher, HDR]

• Amélie Aussel [INRIA, ISFP]

• Xavier Hinaut [INRIA, Researcher, HDR]

• Nicolas Rougier [INRIA, Senior Researcher, HDR]

• Thierry Viéville [INRIA, Senior Researcher, HDR]

Faculty Members

• Alexandre Pitti [ENSEA, Professor Delegation, from Sep 2023]

• Margarida Romero [UNIV COTE D’AZUR, Professor Delegation]

PhD Students

• Maeva Andriantsoamberomanga [INRIA]

• Yannis Bendi-Ouis [INRIA, from Oct 2023]

• Naomi Chaix-Eichel [UNIV BORDEAUX]

• Hugo Chateau Laurent [INRIA]

• Snigdha Dagar [UNIV BORDEAUX, until Mar 2023]

• Lucie Fontaine [UNIV BORDEAUX, from Oct 2023]

• Fjola Hyseni [UNIV BORDEAUX, until Sep 2023]

• Chloe Mercier [INRIA]

• Subba Reddy Oota [INRIA]

• Axel Palaude [INRIA]

• Baptiste Pesquet [BORDEAUX INP, from Sep 2023]

• Remya Sankar [UNIV BORDEAUX, until Jun 2023]

• Nathan Trouvain [POLE EMPLOI]

• Nikolaos Vardalakis [UNIV BORDEAUX, until Sep 2023]

Technical Staff

• Paul Bernard [INRIA, Engineer, from Oct 2023]

• Deepayan Das [UNIV BORDEAUX, Engineer, from Feb 2023]

• Eloise Guillem [INRIA, Engineer, from Dec 2023]

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Inria Annual Report 2023

Interns and Apprentices

• Paul Bernard [INRIA, Intern, from Feb 2023 until Aug 2023]

• Dolton Fernandes [INRIA, Intern, from Mar 2023 until Jun 2023]

• Alizee Fournier [INRIA, Intern, until Jun 2023]

• Vincent Gardies [ENS DE LYON, Intern, from Jun 2023 until Jul 2023]

• Louis Kohler [INRIA, Intern, from Mar 2023 until Aug 2023]

• Mathilde Reynes [INRIA, Intern, from Mar 2023 until Sep 2023]

• Gautham Venugopal [INRIA, Intern, from Mar 2023 until Jun 2023]

• Thomas Vitry [ENS RENNES, Intern, from Sep 2023]

Administrative Assistant

• Anne-Lise Pernel [INRIA, from Mar 2023]

External Collaborator

• Arthur Leblois [CNRS, HDR]

2 Overall objectives

2.1 Summary

At the frontier between integrative and computational neuroscience, we propose to model the brain as
a system of active memories in synergy and in interaction with the internal and external world and to
simulate it as a whole and in situation.

In integrative and cognitive neuroscience (cf. § 3.1), on the basis of current knowledge and exper-
imental data, we develop models of the main cerebral structures, taking a specific care of the kind of
mnemonic function they implement and of their interface with other cerebral and external structures.
Then, in a systemic approach, we build the main behavioral loops involving these cerebral structures,
connecting a wide spectrum of actions to various kinds of sensations. We observe at the behavioral level
the properties emerging from the interaction between these loops.

We claim that this approach is particularly fruitful for investigating cerebral structures like the basal
ganglia and the prefrontal cortex, difficult to comprehend today because of the rich and multimodal
information flows they integrate. We expect to cope with the high complexity of such systems, inspired
by behavioral and developmental sciences, explaining how behavioral loops gradually incorporate in
the system various kinds of information and associated mnesic representations. As a consequence, the
underlying cognitive architecture, emerging from the interplay between these sensations-actions loops,
results from a mnemonic synergy.

In computational neuroscience (cf. § 3.2), we concentrate on the efficiency of local mechanisms and
on the effectiveness of the distributed computations at the level of the system. We also take care of the
analysis of their dynamic properties, at different time scales. These fundamental properties are of high
importance to allow the deployment of very large systems and their simulation in a framework of high
performance computing

Running simulations at a large scale is particularly interesting to evaluate over a long period a
consistent and relatively complete network of cerebral structures in realistic interaction with the external
and internal world. We face this problem in the domain of autonomous robotics (cf. § 3.4) and ensure a
real autonomy by the design of an artificial physiology and convenient learning protocoles.

We are convinced that this original approach also permits to revisit and enrich algorithms and
methodologies in machine learning (cf. § 3.3) and in autonomous robotics (cf. § 3.4), in addition to
elaborate hypotheses to be tested in neuroscience and medicine, while offering to these latter domains a
new ground of experimentation similar to their daily experimental studies.

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3 Research program

3.1 Integrative and Cognitive Neuroscience

The human brain is often considered as the most complex system dedicated to information processing.
This multi-scale complexity, described from the metabolic to the network level, is particularly studied in
integrative neuroscience, the goal of which is to explain how cognitive functions (ranging from sensor-
imotor coordination to executive functions) emerge from (are the result of the interaction of) distributed
and adaptive computations of processing units, displayed along neural structures and information flows.
Indeed, beyond the astounding complexity reported in physiological studies, integrative neuroscience
aims at extracting, in simplifying models, regularities at various levels of description. From a mesoscopic
point of view, most neuronal structures (and particularly some of primary importance like the cortex,
cerebellum, striatum, hippocampus) can be described through a regular organization of information
flows and homogenous learning rules, whatever the nature of the processed information. From a macro-
scopic point of view, the arrangement in space of neuronal structures within the cerebral architecture
also obeys a functional logic, the sketch of which is captured in models describing the main information
flows in the brain, the corresponding loops built in interaction with the external and internal (bodily and
hormonal) world and the developmental steps leading to the acquisition of elementary sensorimotor
skills up to the most complex executive functions.

In summary, integrative neuroscience builds, on an overwhelming quantity of data, a simplifying and
interpretative grid suggesting homogenous local computations and a structured and logical plan for the
development of cognitive functions. They arise from interactions and information exchange between
neuronal structures and the external and internal world and also within the network of structures.

This domain is today very active and stimulating because it proposes, of course at the price of
simplifications, global views of cerebral functioning and more local hypotheses on the role of subsets of
neuronal structures in cognition. In the global approaches, the integration of data from experimental
psychology and clinical studies leads to an overview of the brain as a set of interacting memories,
each devoted to a specific kind of information processing [55]. It results also in longstanding and very
ambitious studies for the design of cognitive architectures aiming at embracing the whole cognition.
With the notable exception of works initiated by [50], most of these frameworks (e.g. Soar, ACT-R), though
sometimes justified on biological grounds, do not go up to a connectionist neuronal implementation.
Furthermore, because of the complexity of the resulting frameworks, they are restricted to simple symbolic
interfaces with the internal and external world and to (relatively) small-sized internal structures. Our
main research objective is undoubtly to build such a general purpose cognitive architecture (to model
the brain as a whole in a systemic way), using a connectionist implementation and able to cope with a
realistic environment.

3.2 Computational Neuroscience

From a general point of view, computational neuroscience can be defined as the development of methods
from computer science and applied mathematics, to explore more technically and theoretically the
relations between structures and functions in the brain [58, 48]. During the recent years this domain has
gained an increasing interest in neuroscience and has become an essential tool for scientific developments
in most fields in neuroscience, from the molecule to the system. In this view, all the objectives of our
team can be described as possible progresses in computational neuroscience. Accordingly, it can be
underlined that the systemic view that we promote can offer original contributions in the sense that,
whereas most classical models in computational neuroscience focus on the better understanding of the
structure/function relationship for isolated specific structures, we aim at exploring synergies between
structures. Consequently, we target interfaces and interplay between heterogenous modes of computing,
which is rarely addressed in classical computational neuroscience.

We also insist on another aspect of computational neuroscience which is, in our opinion, at the core
of the involvement of computer scientists and mathematicians in the domain and on which we think we
could particularly contribute. Indeed, we think that our primary abilities in numerical sciences imply that
our developments are characterized above all by the effectiveness of the corresponding computations: we
provide biologically inspired architectures with effective computational properties, such as robustness

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Inria Annual Report 2023

to noise, self-organization, on-line learning. We more generally underline the requirement that our
models must also mimick biology through its most general law of homeostasis and self-adaptability in
an unknown and changing environment. This means that we propose to numerically experiment such
models and thus provide effective methods to falsify them.

Here, computational neuroscience means mimicking original computations made by the neuronal
substratum and mastering their corresponding properties: computations are distributed and adaptive;
they are performed without an homonculus or any central clock. Numerical schemes developed for
distributed dynamical systems and algorithms elaborated for distributed computations are of central
interest here [44, 43] and were the basis for several contributions in our group [56, 52, 59]. Ensuring such
a rigor in the computations associated to our systemic and large scale approach is of central importance.
Equally important is the choice for the formalism of computation, extensively discussed in the connec-
tionist domain. Spiking neurons are today widely recognized of central interest to study synchronization
mechanisms and neuronal coupling at the microscopic level [49]; the associated formalism [60] can be
possibly considered for local studies or for relating our results with this important domain in connec-
tionism. Nevertheless, we remain mainly at the mesoscopic level of modeling, the level of the neuronal
population, and consequently interested in the formalism developed for dynamic neural fields [53], that
demonstrated a richness of behavior [54] adapted to the kind of phenomena we wish to manipulate at
this level of description. Our group has a long experience in the study and adaptation of the properties of
neural fields [52, 51] and their use for observing the emergence of typical cortical properties [46]. In the
envisioned development of more complex architectures and interplay between structures, the explor-
ation of mathematical properties such as stability and boundedness and the observation of emerging
phenomena is one important objective. This objective is also associated with that of capitalizing our
experience and promoting good practices in our software production.

In summary, we think that this systemic approach also brings to computational neuroscience new
case studies where heterogenous and adaptive models with various time scales and parameters have to
be considered jointly to obtain a mastered substratum of computation. This is particularly critical for
large scale deployments.

3.3 Machine Learning

The adaptive properties of the nervous system are certainly among its most fascinating characteristics,
with a high impact on our cognitive functions. Accordingly, machine learning is a domain [47] that
aims at giving such characteristics to artificial systems, using a mathematical framework (probabilities,
statistics, data analysis, etc.). Some of its most famous algorithms are directly inspired from neuroscience,
at different levels. Connectionist learning algorithms implement, in various neuronal architectures,
weight update rules, generally derived from the hebbian rule, performing non supervised (e.g. Kohonen
self-organizing maps), supervised (e.g.
layered perceptrons) or associative (e.g. Hopfield recurrent
network) learning. Other algorithms, not necessarily connectionist, perform other kinds of learning, like
reinforcement learning. Machine learning is a very mature domain today and all these algorithms have
been extensively studied, at both the theoretical and practical levels, with much success. They have also
been related to many functions (in the living and artificial domains) like discrimination, categorisation,
sensorimotor coordination, planning, etc. and several neuronal structures have been proposed as the
substratum for these kinds of learning [45, 40]. Nevertheless, we believe that, as for previous models,
machine learning algorithms remain isolated tools, whereas our systemic approach can bring original
views on these problems.

At the cognitive level, most of the problems we face do not rely on only one kind of learning and
require instead skills that have to be learned in preliminary steps. That is the reason why cognitive
architectures are often referred to as systems of memory, communicating and sharing information
for problem solving. Instead of the classical view in machine learning of a flat architecture, a more
complex network of modules must be considered here, as it is the case in the domain of deep learning.
In addition, our systemic approach brings the question of incrementally building such a system, with a
clear inspiration from developmental sciences. In this perspective, modules can generate internal signals
corresponding to internal goals, predictions, error signals, able to supervise the learning of other modules
(possibly endowed with a different learning rule), supposed to become autonomous after an instructing
period. A typical example is that of episodic learning (in the hippocampus), storing declarative memory

Project MNEMOSYNE

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about a collection of past episods and supervising the training of a procedural memory in the cortex.

At the behavioral level, as mentionned above, our systemic approach underlines the fundamental
links between the adaptive system and the internal and external world. The internal world includes
proprioception and interoception, giving information about the body and its needs for integrity and
other fundamental programs. The external world includes physical laws that have to be learned and
possibly intelligent agents for more complex interactions. Both involve sensors and actuators that are the
interfaces with these worlds and close the loops. Within this rich picture, machine learning generally
selects one situation that defines useful sensors and actuators and a corpus with properly segmented
data and time, and builds a specific architecture and its corresponding criteria to be satisfied. In our
approach however, the first question to be raised is to discover what is the goal, where attention must
be focused on and which previous skills must be exploited, with the help of a dynamic architecture and
possibly other partners. In this domain, the behavioral and the developmental sciences, observing how
and along which stages an agent learns, are of great help to bring some structure to this high dimensional
problem.

At the implementation level, this analysis opens many fundamental challenges, hardly considered in
machine learning : stability must be preserved despite on-line continuous learning; criteria to be satisfied
often refer to behavioral and global measurements but they must be translated to control the local circuit
level; in an incremental or developmental approach, how will the development of new functions preserve
the integrity and stability of others? In addition, this continous re-arrangement is supposed to involve
several kinds of learning, at different time scales (from msec to years in humans) and to interfer with
other phenomena like variability and meta-plasticity.

In summary, our main objective in machine learning is to propose on-line learning systems, where
several modes of learning have to collaborate and where the protocoles of training are realistic. We
promote here a really autonomous learning, where the agent must select by itself internal resources
(and build them if not available) to evolve at the best in an unknown world, without the help of any
deus-ex-machina to define parameters, build corpus and define training sessions, as it is generally the
case in machine learning. To that end, autonomous robotics (cf. § 3.4) is a perfect testbed.

3.4 Autonomous Robotics

Autonomous robots are not only convenient platforms to implement our algorithms; the choice of such
platforms is also motivated by theories in cognitive science and neuroscience indicating that cognition
emerges from interactions of the body in direct loops with the world (embodiment of cognition [41]). In
addition to real robotic platforms, software implementations of autonomous robotic systems including
components dedicated to their body and their environment will be also possibly exploited, considering
that they are also a tool for studying conditions for a real autonomous learning.

A real autonomy can be obtained only if the robot is able to define its goal by itself, without the
specification of any high level and abstract cost function or rewarding state. To ensure such a capability,
we propose to endow the robot with an artificial physiology, corresponding to perceive some kind of pain
and pleasure. It may consequently discriminate internal and external goals (or situations to be avoided).
This will mimick circuits related to fundamental needs (e.g. hunger and thirst) and to the preservation of
bodily integrity. An important objective is to show that more abstract planning capabilities can arise from
these basic goals.

A real autonomy with an on-line continuous learning as described in § 3.3 will be made possible by
the elaboration of protocols of learning, as it is the case, in animal conditioning, for experimental studies
where performance on a task can be obtained only after a shaping in increasingly complex tasks. Similarly,
developmental sciences can teach us about the ordered elaboration of skills and their association in more
complex schemes. An important challenge here is to translate these hints at the level of the cerebral
architecture.

As a whole, autonomous robotics permits to assess the consistency of our models in realistic condition
of use and offers to our colleagues in behavioral sciences an object of study and comparison, regarding
behavioral dynamics emerging from interactions with the environment, also observable at the neuronal
level.

In summary, our main contribution in autonomous robotics is to make autonomy possible, by various
means corresponding to endow robots with an artificial physiology, to give instructions in a natural

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and incremental way and to prioritize the synergy between reactive and robust schemes over complex
planning structures.

4 Application domains

4.1 Overview

Modeling the brain to emulate cognitive functions offers direct and indirect application domains. Our
models are designed to be confronted to the reality of life sciences and to make predictions in neuros-
cience and in the medical domain. Our models also have an impact in digital sciences; their performances
can be questioned in informatics, their algorithms can be compared with models in machine learning
and artificial intelligence, their behavior can be analysed in human-robot interaction. But since what
they produce is related to human thinking and behavior, applications will be also possible in various
domains of social sciences and humanities.

4.2 Applications in life sciences

One of the most original specificity of our team is that it is part of a laboratory in Neuroscience (with a
large spectrum of activity from the molecule to the behavior), focused on neurodegenerative diseases
and consequently working in tight collaboration with the medical domain. Beyond data and signal
analysis where our expertise in machine learning may be possibly useful, our interactions are mainly
centered on the exploitation of our models. They will be classically regarded as a way to validate biological
assumptions and to generate new hypotheses to be investigated in the living. Our macroscopic models
and their implementation in autonomous robots will allow an analysis at the behavioral level and will
propose a systemic framework, the interpretation of which will meet aetiological analysis in the medical
domain and interpretation of intelligent behavior in cognitive neuroscience and related domains like for
example educational science.

The study of neurodegenerative diseases is targeted because they match the phenomena we model.
Particularly, the Parkinson disease results from the death of dopaminergic cells in the basal ganglia, one
of the main systems that we are modeling. The Alzheimer disease also results from the loss of neurons, in
several cortical and extracortical regions. The variety of these regions, together with large mnesic and
cognitive deficits, require a systemic view of the cerebral architecture and associated functions, very
consistent with our approach.

4.3 Application in digital sciences

Of course, digital sciences are also impacted by our researches, at several levels. At a global level, we will
propose new control architectures aimed at providing a higher degree of autonomy to robots, as well
as machine learning algorithms working in more realistic environment. More specifically, our focus on
some cognitive functions in closed loop with a real environment will address currently open problems.
This is obviously the case for planning and decision making; this is particularly the case for the domain of
affective computing, since motivational characteristics arising from the design of an artificial physiology
allow to consider not only cold rational cognition but also hot emotional cognition. The association of
both kinds of cognition is undoubtly an innovative way to create more realistic intelligent systems but
also to elaborate more natural interfaces between these systems and human users.

At last, we think that our activities in well-founded distributed computations and high performance
computing are not just intended to help us design large scale systems. We also think that we are working
here at the core of informatics and, accordingly, that we could transfer some fundamental results in this
domain.

4.4 Applications in human sciences

Because we model specific aspects of cognition such as learning, language and decision, our models
could be directly analysed from the perspective of educational sciences, linguistics, economy, philosophy
and ethics.

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Futhermore, our implication in science outreach actions, including computer science teaching in
secondary and primary school, with the will to analyse and evaluate the outcomes of these actions, is
at the origin of building a link between our research in computational learning and human learning,
providing not only tools but also new modeling paradigms.

5 Social and environmental responsibility

5.1 Footprint of research activities

As part of the Institute of Neurodegenerative Diseases that developed a strong commitment to the
environment, we take our share in the reduction of our carbon footprint by deciding to reduce our
commuting footprint and the number of yearly travels to conference.

5.2 Impact of research results

We’re engaged in the EcoMob regional project in collaboration with the University of Bordeaux and the
University of La Rochelle to study and model the behavior of individuals during their daily trips to and
from work places. In this context and based on our previous work on decision making, our team is
interested in elucidating how habits are formed and more importantly, how can they be changed.

6 Highlights of the year

6.1 Organization

N.P. Rougier co-funded the national network of reproducible research (with S.Cohen-Boulakia, F.Lemoine
and A.Legrand) and co-organized the kick-off conference "Recherche Reproductible: état des lieux" at
Institut Pasteur.

6.2 Events

• Organisation of the first Open Science workshop at Bordeaux Neurocampus

• Organization of the International workshop “Software, Pillar of Open Science

• Organisation of the fist Hack1Robo Hackathon at Cap Sciences

• Organisation of the Tutorial on Sustainable Deep Learning for Time-series: Reservoir Computing

7 New software, platforms, open data

7.1 New software

7.1.1 ReservoirPy

Name: Reservoir computing with Python

Keywords: Recurrent network, Artificial intelligence, Reservoir Computing, Multi-label classification,
Timeseries Prediction, Time Series, Machine learning, Classification, Offline Learning, Online
Learning, Nonlinear system, Audio classification, Trajectory Generation, Trajectory Modeling,
Neural networks

Scientific Description: Reservoirs Computing is based on random Recurrent Neural Networks (RNNs).
ESNs are a particular kind of networks with or without leaking neurons. The computing principle
can be seen as a temporal SVM (Support Vector Machine): random projections are used to make
dimensionality expansion of the inputs. The input stream is projected to a random recurrent layer

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and a linear output layer (called "read-out") is modified by learning. This training is often done
offline, but can also be done in an online fashion.

Compared to other RNNs, the input layer and the recurrent layer (called "reservoir") do not need to
be trained. For other RNNs, the structure of the recurrent layer evolves in most cases by gradient
descent algorithms like Backpropagation-Through-Time, which is not biologically plausible and
is adapted iteratively to be able to hold a representaion of the input sequence. In contrast, the
random weights of the ESN’s reservoir are not trained, but are often adapted to possess the "Echo
State Property" (ESP) or at least suitable dynamics to generalize. The reservoir activities include
non-linear transformations of the inputs that are then exploited by a linear layer. The states of the
reservoir can be mapped to the output layer by a computationally cheap linear regression. The
weights of the input and recurrent layer can be scaled depending on the task at hand: these are
considered as hyperparameters (i.e. parameters which are not learned) along with the leaking rate
(or time constant) of neurons and the random matrix densities.

Functional Description: ReservoirPy enables the fast and efficient training of artificial recurrent neural

networks.

This library provides implementations and tools for the Reservoir Computing paradigm: a way of
training Recurrent Neural Networks without training all the weights, by using random projections.
ReservoirPy provides an implementation only relying on general scientific librairies like Numpy
and Scipy, in order to be more versatile than specific frameworks (e.g. TensorFlow, PyTorch) and
provide more flexibilty to build custom architectures. It includes useful and advanced features
to train reservoirs. ReservoirPy especially focuses on the Echo State Networks flavour, based on
average firing rate neurons with tanh (hyperbolic tangent) activation function.

Reservoirs Computing is based on random Recurrent Neural Networks (RNNs). The computing
principle can be seen as a temporal SVM (Support Vector Machine): random projections are used
to make dimensionality expansion of the inputs towards a non-linear high-dimensional space.
The input stream is projected to a random recurrent layer and a (often) linear output layer (called
"read-out") is modified by learning. This training is often done offline, but can also be done in an
online fashion.

Compared to other RNNs, the input layer and the recurrent layer (called "reservoir") do not need to
be trained. For other RNNs, the structure of the recurrent layer are often modified by gradient des-
cent algorithms like Backpropagation-Through-Time (BPTT). This more classical kind of learning
is not biologically plausible and often needs to see the training data several time (i.e. for several
epochs), whereas with Reservoir Computing training data are used once usually. In contrast, the
random weights of the ESN’s reservoir are not trained, but are often adapted to possess the "Echo
State Property" (ESP) or at least suitable dynamics to generalize. In addition, sparse matrices are
often used for these random matrices. Overall, this greatly speeds up the learning process and
enables online learning, which is an advantage in many applications.

The reservoir activities include non-linear transformations of the inputs that are then exploited by
a linear layer. The states of the reservoir can be mapped to the output layer by a computationally
cheap linear regression. The weights of the input and recurrent layer can be scaled depending on
the task at hand: these are considered as hyperparameters (i.e. parameters which are not learned)
along with the leaking rate (or time constant) of neurons.

URL: https://github.com/reservoirpy/reservoirpy

Publications: hal-03699931, hal-02595026, hal-03533731, hal-03203318, hal-03482372, hal-03203374,

hal-03761440, tel-03946773, hal-03628290, hal-03780006, hal-03945994

Contact: Xavier Hinaut

Participants: Xavier Hinaut, Nathan Trouvain, Nicolas Rougier

Project MNEMOSYNE

7.1.2 Neurosmart

Name: Neurosmart

11

Keywords: Science outreach, Web Application, JavaScript, JSon, Cognitive sciences, Neurosciences

Functional Description: The software consists of a web interface allowing to run the scenario of a
presentation of cognitive functions while visualizing the brain areas involved. It’s made of : - a
3D brain model - an interactive visualization module of this brain model with low computation
consumption - a command language in JSON syntax to drive the display of the model and interactive
multi-media content, which can be extended - an "indulgent" JSON syntax analyzer, i.e., allowing
to build the data structure with a minimum of control characters, all is programmed in Javascript
to run in any browser.

Release Contributions: Initial version

URL: https://gitlab.inria.fr/mnemosyne/neurosmart

Contact: Thierry Viéville

Partners: Fondation Blaise Pascal, EchoScience

7.1.3 AIDElibs

Name: Artificial Intelligence Devoted to Education

Keywords: Cognitive sciences, Neurosciences, Educational Science, C++, JavaScript, Python, Connected

object, Automatic Learning, Learning

Scientific Description: We want to explore to what extent approaches or techniques from cognitive
neuroscience related to machine learning and symbolic tools to represent knowledge, could help
to better formalize human learning as studied in education sciences. . To this end, we are devel-
oping a research code for measuring learning analytics during activities with tangible objects and
middleware between the major tools and algorithms used in this exploratory action of research.

Functional Description: This library includes

- the preliminary implementation of metrizable symbolic data structure allowing performing
symbolic derivations using numerical embedding, in an explicitly (thus easily explainable) way,
targeting reinforcement symbolic learning or open-ended creative complex problem-solving.

- a set of C/C++ routines for basic calculations, with the portions of code executed on connected
objects which allow measurement of learning traces, and the control of experiments,

- C/C ++ or Javascript tools to interface the different software modules used, and a Python wrapper
to develop above these functionalities.

Release Contributions: Initial version

URL: https://gitlab.inria.fr/line/aide-group/aide

Contact: Thierry Viéville

Participants: Chloe Mercier, Axel Palaude, Lola Denet, Éric Pascual

Partners: Laboratoire LINE, Université Côte d’Azur, PoBot

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Inria Annual Report 2023

8 New results

8.1 Overview

This year we have addressed several important questions related to our scientific positioning. Central
to this positioning, we have studied and modeled bio-inspired learning mechanisms and collaborative
mnesic functions (cf. § 8.2). We have also studied higher cognitive functions, related to cognitive control
(cf. § 8.3) and have also considered how important characteristics can be associated to this framework,
like symbolic abstract knowledge (cf. § 8.4), and oscillations (cf. § 8.5). Endly, we have also pursued our
work on language processing in birds and robots (cf. § 8.6).

8.2 Learning mechanisms and collaborative mnesic functions

Within the development of the ReservoirPy library, we have released various versions from 0.3.6 to
0.3.10. We presented Reservoir Computing principles and the ReservoirPy library at AI4industry 2023
workshop (Jan23, Bordeaux), at ECML-PKDD "Tutorial on Sustainable Deep Learning for Time-series"
(Sept23, Turin, Italy), at University of Californie Los Angeles invited by A. Warlaumont (Nov23, USA),
at "5ème Rencontres Chercheur·euse·s et Ingénieur·e·s" at Institut Henri Poincaré invited by Phimeca
company (Nov23, Paris) and also for internal (Mnemosyne Green Days, Oct23, Lacanau) and project
meetings (DFKI-Inria NEARBY project, Dec23, virtual). ReservoirPy was used by two projects at the
Hack1robo hackathon (Jun23, Bordeaux). We made a coding "sprint" at PyCon conference (Feb23,
Bordeaux) [17]. A collaboration with an original application to COVID prediction is in progress regarding
the interface to the library in R language that was jointly developed with the SISTM team and called
reservoirR.

In a collaboration with C. Moulin-Frier et al. (Inria-Flowers team) we explored a new way to combine
Reservoir Computing with Reinforcement Learning (RL). The general aim was to model to adaptive abilit-
ies of animals to their environments. It explores the interplay between evolutionary and developmental
processes using meta reinforcement learning. In the study [26] we evolved reservoirs, focusing on their
hyperparameters rather than weight values. These reservoirs are then used within a Reinforcement Learn-
ing framework to learn behavioral policies. The study tests the model in various simulated environments
(MuJoCo), examining its ability to handle tasks with partial observability, learn locomotion, and generalize
behaviors to new tasks. The results indicate that evolving reservoirs can enhance reinforcement learning
in diverse challenging tasks, in particular using reservoirs enable to accelerate the training convergence
of the PPO (Proximal Policy Optimisation) RL algorithm.

8.3 Cognitive control

In cognitive control, the working memory in the prefrontal cortex and the episodic memory in the
hippocampus play a major role in the definition of flexible contextual rules that can replace the dominant
behavior. This year, we have concluded two important doctoral works related to this topic: Snigha Dagar
has proposed a bio-informed model of the prefrontal cortex, able to learn and manipulate abstract and
concrete rules and has defended her work in April [21]. Hugo Chateau-Laurent will defend his thesis in
February next year and is considering the role of the hippocampus in episodic memory and in cognitive
control.

8.4 Integrating abstract symbolic knowledge

Within the AIDE AEx (cf. § 10.3.4), we carried on introducing the idea of a symbolic description of a
complex human learning task, in order to contribute to better understanding how we learn, in the very
precise framework of a task, named #CreaCube, related to initiation to computational thinking presented
as an open-ended problem, which involves solving a problem and appealing to creativity [31], and also to
participate in the experimental design and analysis [27, 34]. We also proposed to map an ontology onto a
SPA-based architecture with a preliminary partial implementation into spiking neural networks in order
to provide an effective link between symbolic presentation of information and biologically plausible
numerical implementation. We also still work on making explicit how a reinforcement learning paradigm

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can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an
ontology, for instance with applications to robotics. This work is embedded in a strong collaboration with
education science collaborators [18] working on computational thinking initiation and computer science
tools in education with a multi-disciplinary vision of cognitive function modeling.

8.5 Integrating oscillations

This year, we carried on studying the neural oscillations involved in cognition.

As part of the PhD project of Nikolaos Vardalakis, we kept on developing and studying our detailed
model of the hippocampal formation and its interactions with the medial septum (modeled as Kuramoto
oscillators). In particular, we showed how electrical stimulation can help restore theta-nested gamma
oscillations, characteristics of memory encoding, and how the timing of this stimulation (relative to the
phase of the theta rhythm) is critical to the restoration of gamma activity in the case of impaired theta
phase reset between the hippocampus and the medial septum.

We started a project of modeling the prefrontal cortex (PFC) with an MSc student by reproducing
an existing computational model of this area, which we aim to couple with our hippocampus model in
order to have a global model of the brain areas involved in memory and the oscillations supporting their
interactions. We are currently preparing a publication in the journal "ReScience C" about the replication
of the computational model of the PFC, while the larger PFC-hippocampus project has been submitted
to the ANR JCJC call (AAPG 2024).

At the microscopic scale, as part of the PhD project of Maeva Andriantsoamberomanga, we are
currently investigating the effects of extracellular electrical stimulation on hippocampal pyramidal
cells, and in particular we are studying the influence of electrode placement on the recruitment of
action potentials using very detailed multicompartmental neuron models capturing the geometry of
hippocampal neurons.

Finally, we studied a different cognitive process, namely attention, and showed in ([7]) that the
frequency of neural oscillations in a computational model of the fronto-parietal network is critical to
obtain high sensitivity of target detection in a visual attention task.

8.6 Language processing

We pursue our research on the understanding of how children acquire language through noisy supervision
and model how their brain could process language with the little information available. We take the
perspective of a learning agent by focusing on robotic corpora, in order to integrate the "Grounding
Problem" (a question also important for other topics of the team cf 8.4).

In particular, the Reservoir Computing paradigm enables to have a diversity of computations available
"right at the start of learning". This is like if the child or agent would have computation (nearly) for
since the beginning to bootstrap the developmental learning. In this perspective, it seems obvious that
it can enable a child or an agent to learn more quickly than with a classical learning algorithm (has we
have shown previously comparing reservoirs and LSTM). This year, we demonstrated this within a new
perspective with the study of Leger et al. [26] by combining this paradigm with meta-reinforcement
learning 8.2.

Moreover, we have pushed forward the studies related to "brain encoding" for brain imaging data
(fMRI, MEG, etc.) of participants listening to or reading stories (said to be more ecological conditions than
previous imagery experiments). From the activations of a language model, processing the same stimuli as
the participant, we predict the imaging data (fMRI/MEG) from participant, synchronising points in time.
There is a vast literature on linguistic brain encoding for functional MRI (fMRI) related to syntactic and
semantic representations. Magnetoencephalography (MEG), with higher temporal resolution than fMRI,
enables us to look more precisely at the timing of linguistic feature processing. Inspired by previous fMRI
studies, we studied [13, 33] MEG brain encoding using basic syntactic and semantic features, with various
context lengths and directions (past vs. future), for a dataset of 8 subjects listening to stories. We find that
BERT representations predict MEG significantly but not other syntactic features or word embeddings
(e.g. GloVe), allowing us to encode MEG in a distributed way across auditory and language regions in
time. In particular, past context is crucial in obtaining significant results. In following experiment [32],
we investigate if speech models outperform language models during speech-evoked brain activity, if

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Inria Annual Report 2023

LM and SM combine new word representation with previous context, and what are time of information
is shared between them. Moreover, we made a review paper [29]. In this survey, first discuss popular
representations of language, vision and speech stimuli, and present a summary of neuroscience datasets.
Further, we review popular deep learning based encoding and decoding architectures and note their
benefits and limitations. Finally, we will conclude with a brief summary and discussion about future
trends.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

X. Hinaut had a contract with the Lyre lab of Suez company (Pessac) to supervise a master student on a
study using reservoir computing.

10 Partnerships and cooperations

10.1 International initiatives

Stanford-France

Title: E-I RNN imbalance in psychiatric disorders

Duration: 2022 ->

Coordinator: Vinod Menon (menon@stanford.edu)

Partners:

• SCSNL, Stanford (USA)

Inria contact: Xavier Hinaut

Summary: Context. This grant allowed us to start a new collaboration between Vinod Menon’s lab at
Stanford and Mnemosyne at Inria, Bordeaux (including Xavier Hinaut and Nathan Trouvain). The
collaboration explores novel modeling approaches to investigate how neural excitability/inhibitory
(E/I) imbalance can cause atypical sensory processing of salient information observed in psychi-
atric disorders, and investigate the underlying mechanisms associated with typical and atypical
processing. Scientific accomplishments. 1. MMN-like response from adaptation: We explored
the surprisal response generated by different models using either predictive coding or adaptation
mechanisms, with the objective of generating surprisal responses and mismatch negativity (MMN)
observed in humans in response to rare and salient deviant vs frequent salient stimuli. We observed
similar results for two adaptation mechanisms: (1) adaptation caused by short-term synaptic
depression, and (2) adaptation in adaptative exponential integrated-and-fire neurons (AdEx). 2.
Effect of E/I balance on MMN-like response: We explored the effect of E/I imbalance on the sur-
prisal response and how imbalance can account for the abnormal surprise response observed in
psychiatric disorders. We observed in our models that increased E/I ratio slowed down adaptation
(Figure 1) leading to reduced differences between response to standard and deviant stimuli (i.e.,
suppressed surprisal responses, (Figure 2) which potentially explained phenomena observed in
the auditory cortex of patients with Autism Spectrum Disorder (ASD). 3. Propagation of MMN-like
response along cortical hierarchy: Next, using the Reservoirpy library, we explored the effect of
hierarchical structure on the delayed and time-sustained amplification of the surprisal response
during its propagation from sensory to frontal areas, which plausibly accounts for how the sensory
MMN (mismatch negativity) response gives rise to a stronger, more sustained, and delayed P300
response. We do observe delayed amplification of the surprisal response along the hierarchy from
sensory to frontal neural networks, provided strong and dense enough inter-regional connectivity
(Figure 3). We are still working together through the preliminary results in order to clarify how the
behavioral relevance of salient stimuli modulates this delayed amplification.

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10.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an

Inria International Program

MENG PO

Title: Memory ENGineering for Problem sOlving

Duration: 2020 ->

Coordinator: Shan Yu (shan.yu@nlpr.ia.ac.cn)

Partners:

• Brainnetome Center and National Laboratory of Pattern Recognition (Chine)

Inria contact: Frederic Alexandre

Summary: Artificial Intelligence (AI) has been built on the opposition between symbolic problem solving
that should be addressed by explicit models of planning, and numerical learning that should be
obtained by neural networks. But it is clear that in ecological conditions, our cognition has to mix
both capabilities and this is nicely carried out by our brains. Our behavior is sometimes described
as a simple dichotomy between Goal-Directed (explicit deliberation and knowledge manipulation
for planning) and habitual (automatic Stimulus-Response association) approaches. Recent results
rather report more general strategies, including the hybrid combinations of both. Importantly,
they highlight key mechanisms, corresponding to detect explicitly contexts in which the strategy
should be modified and to adapt simple Stimulus-Response associations to these contexts. We
propose here to associate our experiences to develop a more general framework for adapting neural
networks to problem solving, thus augmenting their usability in AI and the understanding of brain
reasoning mechanisms. On the Chinese side, connectionist models like deep neural networks
are adapted to avoid so-called catastrophic forgetting and to facilitate context-based information
processing. On the French side, models in computational neuroscience explore the capacity of
neuronal structures like the hippocampus to categorize contexts and investigate the role of the
prefrontal cortex, known to modulate behavioral activity depending on the context. The main
impact of this associate team is in the better understanding of brain circuits and their relation
to higher cognitive functions associated to problem solving. This might also have an impact in
the medical domain, through the possibility of studying lesioned systems and their relations to
dysfunctions of the brain. In addition, the new algorithms and network architecture for deep
learning generated in this project promise a wide range of applications in complex and dynamic
environments.

10.1.2 Inria associate team not involved in an IIL or an international program

SARASWATI

Title: Sequential motor skills: a dual system view

Duration: 2020 ->

Coordinator: Raju Surampudi BAPI (srinivasa.chakravarthy@gmail.com)

Partners:

• Indian Institute of Technology Hyderabad Hyderabad (Inde)

Inria contact: Nicolas Rougier

Summary: Action-outcome (A-O) and stimulus-response (S-R) processes, two forms of instrumental
conditioning, are important components of behavior. The former evaluates the benefit of an
action in order to choose the best one among those available (action selection), while the latter is
responsible for automatic behavior (routines), eliciting a response as soon as a known stimulus
is presented , independently of the hedonic value of the stimulus. Action selection can be easily

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Inria Annual Report 2023

characterized by using a simple operant conditioning setup, such as a two-armed bandit task,
where an animal must choose between two options of different value, the value being probability,
magnitude or quality of reward. After some trial and error, a wide variety of vertebrates are able
to select the best option . After intensive training, which depends on the species and the task and
whether the same values are used throughout the series of the experiments, the animal will tend
to become insensitive to change and persist in selecting the formerly best option . Most of the
studies on action selection and habits/routines agree on a slow and incremental transfer from
the action-outcome to the stimulus-response system such that after extensive training, the S-R
system takes control of behavior and the animal becomes insensitive to reward devaluation. Oddly
enough, very little is known on the exact mechanism underlying such transfer and there exists many
different hypothesis. One difficult question that immediately arises is when and how the brain
switches from a flexible action selection system to a more static one. Therefore, this collaborative
project aims at answering this question on both the theoretical and experimental side.

10.2 International research visitors

10.2.1 Visits to international teams

Research stays abroad

Frederic Alexandre

Visited institution: Brainnetome Center and National Laboratory of Pattern Recognition, CASIA

Country: China

Dates: December 4-9

Context of the visit: Associate Team Meng Po

Mobility program/type of mobility: Research stay

Xavier Hinaut and Nathan Trouvain

Visited institution: Stanford

Country: USA

Dates: November 6-13

Context of the visit: Stanford-France grant with SCSNL, Stanford

Mobility program/type of mobility: Research stay

Xavier Hinaut and Nathan Trouvain

Visited institution: UCLA

Country: USA

Dates: November 14-16

Context of the visit: Collaboration with Anne Warlaumont

Mobility program/type of mobility: Research stay

Project MNEMOSYNE

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10.3 National initiatives

10.3.1 ANR SOMA (PRCI)

Participants: Nicolas Rougier, Remya Sankar.

This project is a convergence point between past research approaches toward new computational
paradigms (adaptive reconfigurable architecture, cellular computing, computational neuroscience, and
neuromorphic hardware):

1. SOMA is an adaptive reconfigurable architecture to the extent that it will dynamically re-organize

both its computation and its communication by adapting itself to the data to process.

2. SOMA is based on cellular computing since it targets a massively parallel, distributed and decent-

ralized neuromorphic architecture.

3. SOMA is based on computational neuroscience since its self-organization capabilities are inspired

from neural mechanisms.

4. SOMA is a neuromorphic hardware system since its organization emerges from the interactions

between neural maps transposed into hardware from brain observation.

This project represents a significant step toward the definition of a true fine-grained distributed, adaptive
and decentralized neural computation framework. Using self-organized neural populations onto a
cellular machine where local routing resources are not separated from computational resources, it will
ensure natural scalability and adaptability as well as a better performance/power consumption tradeoff
compared to other conventional embedded solutions.

10.3.2 ANR DeepPool (JCJC)

Participants:

Xavier Hinaut, Nathan Trouvain, Subba Oota.

Language involves several abstraction levels of hierarchy. Most models focus on a particular level
of abstraction making them unable to model bottom-up and top-down processes. Moreover, we do
not know how the brain grounds symbols to perceptions and how these symbols emerge throughout
development. Experimental evidence suggests that perception and action shape one-another (e.g. motor
areas activated during speech perception) but the precise mechanisms involved in this action-perception
shaping at various levels of abstraction are still largely unknown. The PI proposes to create a new
generation of neural-based computational models of language processing and production: i.e. to (1)
use biologically plausible learning mechanisms; (2) create novel sensorimotor mechanisms to account
for action-perception shaping; (3) build hierarchical models from sensorimotor to sentence level; (4)
embody such models in robots in order to ground semantics.

The project will last four years (2022-2025). We regularly discuss with our colleague from the University

of Bordeaux (Gaël Jobard).

10.3.3 Exploratory action BrainGPT

Participants:

Xavier Hinaut, Yannis Bendi-Ouis.

In the wake of the emergence of large-scale language models such as ChatGPT, the BrainGPT project is
at the forefront of research in Artificial Intelligence and Computational Neuroscience. While these models
are remarkably efficient, they do not reflect how our brain processes and learns language. BrainGPT takes

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Inria Annual Report 2023

up the challenge by focusing on the development of models more faithful to human cognitive functioning,
inspired by data from brain activity during listening or reading. The ambition is to create more efficient
models, less reliant on intensive computations and massive volumes of data. BrainGPT will open new
perspectives on our understanding of language and cognition.

The project will last four years (2023-2027).

10.3.4 Exploratory action AIDE

Participants:

Frédéric Alexandre, Axel Palaude, Chloé Mercier, Thierry Viéville.

The modelling and assessment of computational thinking (CT) skills is a challenge that has a major
impact on how learning activities are integrated into the curricula of OECD countries, particularly in
terms of equal opportunities. The Artificial Intelligence Devoted to Education (AIDE) Inria exploratory
action (AEx)aims to help address this challenge in an innovative way by modelling computational thinking
through a neuro-inspired cognitive model, allowing analysis of the learner engaged in learning activities.
It’s an exploratory subject, finishing this year. We are taking the scientific risk of looking at things
differently, and had the chance to generate a few interesting outcomes. For example, instead of using the
so-called artificial intelligence mechanisms to try to make "assistants", i.e., algorithms to better learn,
we start focusing on how formalisms from the field of "artificial intelligence" (numerical and symbolic)
contribute to better understand how we learn. But it is also a research with applications. Our hope is to
contribute to the reduction of educational inequalities and improve school perseverance, focusing on
transversal competencies, also called 21st century competencies which include computer thinking. More
details on our activities here and a public presentation here.

10.4 Regional initiatives

10.4.1 Observatory of surveillance in democracy

Participants:

Frédéric Alexandre, Nicolas Rougier.

The University of Bordeaux has labeled one of our activities as an interdisciplinary and exploratory
research project. In collaboration with university partners in the field of law, the aim of this project is
to understand the changes in society imposed by the development of digital surveillance technologies
in a democratic context and to organize seminars and general public conferences to disseminate this
information.

10.4.2 Hyperhum@in

Participants: Nicolas Rougier.

The Hyperhum@in research program brings together a core group of researchers in HSS and life
sciences committed to questioning exploratory engineering projects “at the frontiers of the human”. The
second part, entitled “brain-machine: analogy, model, identity”, proposes to take a combined look at
Artificial Intelligence and cognitive neuroscience which have today become inseparable in their mutual
quest for intelligibility of the functioning of the human brain.

10.4.3 RT-HippoNeuroStim

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Participant:

Amélie Aussel.

The University of Bordeaux has labeled this project as an interdisciplinary and exploratory research
project.

The RT-HippoNeuroStim project aims at translating the hippocampal model previously developed
by A. Aussel, together with Fabien Wagner (IMN), onto the new neuromorphic computing architecture
developed by the team of Timothée Levi at the IMS. This architecture is based on Field Programmable
Gate Arrays (FPGA) and is much more efficient than current simulation software. We will leverage this
platform to simulate the activity of the hippocampus in real time, which will greatly accelerate research
on hippocampal neurostimulation.

10.4.4 Ecomob

Participants:

Frédéric Alexandre, Snigdha Dagar, Nicolas Rougier.

Project gathering researchers from: University of La Rochelle (Cerege lab in social sciences and L3I
lab in computer science); University of Bordeaux (IRGO lab in organisation management); Town and
suburbs of La Rochelle.

The goal of this project was to study and model user urban mobility behaviours in an eco-responsibility
context. Our team was in charge of studying models of decision in such complex contexts, in interaction
with teams in social sciences aiming at influencing user behaviours.

The project ended this year with a workshop organized in La Rochelle on October 26th, with scientific
talks in the morning and open sessions in the afternoon, to disseminate our results to a wider audience,
including public and private stakeholders.

10.4.5 PsyPhINe

Participants: Nicolas Rougier.

Project gathering researchers from: MSH Lorraine (USR3261), InterPsy (EA 4432), APEMAC, EPSaM

(EA4360), Archives Henri-Poincaré (UMR7117), Loria (UMR7503) and Mnemosyne.

PsyPhiNe is a pluridisciplinary and exploratory project between philosophers, psychologists, neur-
oscientists and computer scientists. The goal of the project is to explore cognition and behavior from
different perspectives. The project aims at exploring the idea of assignments of intelligence or intention-
ality, assuming that our intersubjectivity and our natural tendency to anthropomorphize play a central
role: we project onto others parts of our own cognition. To test these hypotheses, we ran a series of
experiments with human subject confronted to amotorized lamp that can or cannot interact with them
while they’re doing a specific task.

10.4.6 Regional and local Research Networks

We are members of three Regional Research networks, devoted to Artificial Intelligence, Robotics and
Computational Education.

We are members of two Networks of Research of the University of Bordeaux: PHDS (Public Health

Data Science) and RobSys (Robustness of Autonomous Systems).

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Inria Annual Report 2023

11 Dissemination

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

General chair, scientific chair F. Alexandre was in charge of the scientific organization of the one-week
workshop AI for Industry AI4I’23 including 400 attendees on january 16-20, with teaching in the morning
and hands-on experiments on industrial applications in the afternoon.

F. Hyseni, N.P. Rougier, C. Mercier and N. Trouvain co-organized the first Open Science workshop at

Bordeaux Neurocampus.

N.P. Rougier co-organized the International workshop “Software, Pillar of Open Science”.
N.P. Rougier co-organized Recherche Reproductible: état des lieux at Institut Pasteur and co-funded

the "Réseau français de la recherche reproductible"

X. Hinaut co-organized the ECML-PKDD "Tutorial on Sustainable Deep Learning for Time-series"

(Sept23, Turin, Italy).

X. Hinaut and N. Trouvain organized a coding "sprint" at PyCon conference (Feb23, Bordeaux) [17].
X. Hinaut co-organised the first edition of Hack1robo hackathon.

11.1.2 Scientific events: selection

Member of the conference program committees F. Alexandre: ACAIN23, DATAQUITAINE23, ICANN23;

X. Hinaut: Drôles d’objets 2023;
N.P. Rougier: CogSci23, ICANN23, Drôles d’objets 2023;
A. Aussel has been elected as a member of the program committee for the Organization for Computa-
tional Neurosciences (OCNS) for three years, and as such has helped select keynote and oral talks and
reviewed abstract submissions for the CNS conference in July 2023 in Leipzig.

Reviewer F. Alexandre: reviewer for ICANN23, TAIMA23;

X. Hinaut: reviewer for SFN23, CogSci23, Drôles d’Objets;
N.P.Rougier for ICANN23, CogSci23, Bench 2023;

11.1.3 Journal

X. Hinaut: reviewer for Neural Networks;

Member of the editorial boards F. Alexandre is Academic Editor for PLOS ONE; Review Editor for
Frontiers in Neurorobotics; member of the editorial board of Cognitive Neurodynamics.

N.P.Rougier is Editor-in-Chief for ReScience C and ReScience X, associate editor for PeerJ Computer

Science, Review Editor for Frontiers in Robotics, Frontiers in Decision Making.

Reviewer - reviewing activities A. Aussel has been a reviewer for the journal PNAS.

11.1.4 Invited talks

F. Alexandre: talk about Cognitive Control to the interdisciplinary seminar in Cognitive Informatics of the
University of Québec in Montréal; Webinar to the European Digital Innovation Hub DIHNAMIC about
ChatGPT. Talks given to companies (Dassault Systems, LVMH) and Socio-Economic actors (Ministry of
Defence) about Artificial Intelligence.

N.P.Rougier has been invited to the University of Lancaster , colloquium of the Mathematics Institute
of the University of Potsdam, SOFT Days (Bordeaux), European College of Sport Science conference
(Paris), Hacking Cognition (Paris), Huawei Technical Summit (Helsinki), Inria (Nancy).

X. Hinaut was invited to give a talk to the interdisciplinary seminar in Cognitive Informatics of the
University of Québec in Montréal by S. Harnad (Jan23, Canada, remotly); X. Hinaut was invited to give a
tutorial on Reservoir Computing at AI4industry workshop (Jan23, Bordeaux, FR). X. Hinaut was invited

Project MNEMOSYNE

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to give a talk at IIIT Hyderabad (Jan23, India). X. Hinaut gave a tutorial on ReservoirPy at the ECML-
PKDD "Tutorial on Sustainable Deep Learning for Time-series" (Sept23, Turin, Italy). X. Hinaut was
invited to give a talk at SCSNL, Stanford (Nov23, Palo Alto, USA). X. Hinaut was invited at University of
California Los Angeles by A. Warlaumont (Nov23, LA, USA) both to give a scientific talk and a tutorial on
Reservoir Computing and ReservoirPy. P. Bernard (and X. Hinaut for preparation) were invited to present
Reservoir Computing and ReservoirPy "5ème Rencontres Chercheur·euse·s et Ingénieur·e·s" at Institut
Henri Poincaré invited by Phimeca company (Nov23, Paris, FR).

11.1.5 Scientific expertise

F. Alexandre was auditioned on December 12th at the French National Assembly by the OPECST (Office
Parlementaire d’Evaluation des Choix Scientifiques et Technologiques), about Artificial Intelligence; he
was also an expert for the FRQNT (Fonds de Recherche du Québec Nature et Technologies) and for the
ANID (Agencia Nacional de Investigacion y Desarrollo) in Chile.

N.P. Rougier is member of the national network of Open Science experts, member of the software
college for the national committee for Open Science, Open Science expert for SwissUniversities and AI
expert for Sorbonne university;

11.1.6 Research administration

F. Alexandre is member of the steering committee of Inria Bordeaux Sud-Ouest Project Committee;
member of the Inria International Chairs committee; corresponding member for Inria Bordeaux Sud-
Ouest of the Inria Operational Committee for the assesment of Legal and Ethical risks; He was also
member of the Program Committee of the yearly Inria Scientific days (JSI’23);

N.P. Rougier is the corresponding member for Inria Bordeaux Sud-Ouest on scientific edition, head of

the Computational Neuroscience team at Institute of Neurodegenerative Diseases.

X.Hinaut is member of the “Committee for Technological Development”(CDT), the "Committeefor
Research Jobs" (CER) of Inria Bordeaux Sud-Ouest, and addressee of the PlaFRIM high-performance
computing cluster. He is also chair of IEEE Task Forces (TF) about: "Reservoir Computing" (co-chair),
"Cognitive and Developmental Systems Technical Committee": "Language and Cognition" (main chair)
and is also member of IEEE TF "Action and Perception". He is co-Head of the "Apprentissage et Neur-
osciences pour la Robotique" (GT8) CNRS Robotics Working Group. He manages a WP in the PHDS
Impulsion Bordeaux network.

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

Many courses are given in french universities and schools of engineers at different levels (LMD) by most
team members, in computer science, in applied mathematics, in neuroscience and in cognitive science.

• T. Viéville is teaching computational thinking in the Msc #CreaSmartEdtech "Digital Expertise" and

is co-organizing this Master of Science, animating both online and blended sessions.

• C. Mercier teached the Education Computing, including Artificial Intelligence and Ontologies
initiation, in the Msc #CreaSmartEdtech as an online interactive course, including student follow-
up, 6 hours.

• S. R. Oota teached at the Cogsci-2022 Full-day Tutorial: Deep Learning for Brain Encoding and

Decoding

• F. Alexandre and T. Viéville have been involved in the animation and online coaching of the "Intelli-
gence Artificielle Intelligente" citizen formation, via the creation of a MOOC, with more than 40,000
participants, allowing everyone to master these disruptive technologies by better understanding
ground notions [19, 20, 25, 30].

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Inria Annual Report 2023

• T. Viéville have been involved, as project driver, in the production of the "Numérique et Science
Informatique", on-line and interactive teaching platform, with two MOOC and a forum, aiming at
offering a French-speaking formation regarding the NSI and SNT teaching, with more than 5000
participants after a few weeks of experimental production. See accompanied community-based
self-training of computer science and technology secondary school teachers [8, 24].

• N. Rougier co-created (with Arthur Leblois and Slim Karkar) the computational neuroscience
module for the international master program in neuroscience (University of Bordeaux), where A.
Aussel also teached.

• N.Rougier helped in the design of the MOOC Intelligence artificielle pour et par les enseignants

• F. Alexandre gave an invited talk about ChatGPT to the Graduate Program Numerics; he is also
teaching Machine Learning and Artificial Intelligence to Masters of the University of Bordeaux and
to engineering schools of Bordeaux INP.

• A. Aussel is involved in teaching at the ENSEIRB engineering school (Graph Theory) and in the
international master program in neuroscience of the University of Bordeaux. She has supervised
student projects at the workshop “AI 4 Industry”.

• X. Hinaut is involved in teaching (Machine Learning, Modelling, Neural Networks, Time Series
Processing, Reservoir Computing, ...), supervising BSc and MSc interns, and supervising student
projects in Bordeaux area: student project at workshop “AI 4 Industry”, MSc Eng. Bordeaux INP
engineering schools, MSc Cognitive Science, University of Bordeaux, FR. He is also in the monitoring
comittee of several PhD students: G. Hamon (Inria), Manel Rakez (Inserm), Tristan Karch (Inria).

• X. Hinaut was in the PhD comittee of Tristan Karch (Inria, Bordeaux), and was reviewer in the PhD

comittee of Marc-Antoine Georges (Gipsa lab, INP, Grenoble)

11.2.2 Supervision

• A. Aussel is involved in the supervision of two PhD students, Nikolaos Vardalakis and Maeva

Andriantsoamberomanga, and has been involved in the supervision of one MSc intern.

• X. Hinaut was involved in the supervistion of PhD students (S.R. Oota, N. Trouvain, K. Ba, Y. Bendi-
Ouis), MSc students (A. Frounier, C. Léger, P. Bernard, L. Bernachot), BSc student (V. Gardies).

11.3 Popularization

11.3.1 Articles and contents

F. Alexandre: two article for The Conversation, about word embedding and about ChatGPT; One article in
Le Figaro Newspaper about Artificial Intelligence; Contribution to the online Blog Binaire, of newspaper
Le Monde about the interdisciplinary book.

11.3.2 Education

F. Alexandre: two talks given at the regional Maison de la Science to introduce Artificial Intelligence and
ChatGPT to 25 high-school teachers. Training of high-school teachers in mathematics of the Department
Les Landes about ChatGPT, on Oct 11th in Mont de Marsan.

X. Hinaut gave a talk on songbirds at the interdisciplinary seminar of Inria, Bordeaux. X. Hinaut
(helped by F. Alexandre for preparation) gave a talk on AI done at the interdisciplinary seminar of Inria,
Bordeaux.

H. Chateau-Laurent and X. Hinaut made an interactive performances twice in the year. First, during
the "Semaine du Cerveau" (Mar23) they made a conference to more than hundred high-school students
arguing that science and art could intertwine; doing scientific presentations, musical performances and
some student could interact with an optimisation tool (HyperOpt) [42] to optimise musical patterns
during the final musical performance. Then, they did a more advanced interaction with an adult public

Project MNEMOSYNE

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at the "Drôles d’Objets" conference (Nancy, May23). They first invited the public to interact with a visual
interface throught a MIDI keyboard to visualize the latent space of a Generative Adversarial Network
producing birdsong sounds. Once the participants selected four canary syllables, they could select the
rhythm at which they should be played. This was done through an intuitive interface easing the use of
Euclidian rhythms [57].

X. Hinaut with colleagues from Inria and Bordeaux Univeristy organised the first edition of Hack1robo
hackathon. Hack1Robo focusing on areas such as AI, robotics, and cognitive sciences. Held from
June 2-4, 2023, at the Cap Sciences FabLab, the event aimed to mediate and disseminate research
knowledge, making technology accessible. Open to anyone with relevant knowledge or skills, the 2023
edition welcomed 27 participants. It sought to spark interest and potential vocations in students from
the Bordeaux academic ecosystem, linking them with Inria’s and the University’s research labs. Two
promising projects from the 2023 edition were chosen for the Hackatech hackathon in November 2023,
aiming to develop commercializable technology.

11.3.3 Interventions

• A. Aussel was interviewed for the "Une minute avec..." program from Inria, which was then released
online during the Semaine du Numérique. She took part in various popularization initiatives: the
"Un scientifique, une classe, Chiche !" program, the "Circuit scientifique Bordelais", the "Semaine
du Cerveau", and also presented her work to visiting students from the ENS Lyon engineering
school.

• N.P. Rougier gave a talk at TEDx Quartier Latin (Paris) on embodied cognition

• N.P. Rougier is vice-president of the Hypermondes festival (Mérignac) that mix science and fiction

for the general public (8,000 attendees) and co-organized the third edition.

12 Scientific production

12.1 Major publications

[1] F. Alexandre. ‘A global framework for a systemic view of brain modeling’. In: Brain Informatics 8.1
(16th Feb. 2021), p. 22. DOI: 10.1186/s40708-021-00126-4. URL: https://hal.inria.fr/hal
-03143843.

[2] A. Nioche, N. P. Rougier, M. Deffains, S. Bourgeois-Gironde, S. Ballesta and T. Boraud. ‘The adaptive
value of probability distortion and risk-seeking in macaques’ decision-making’. In: Philosophical
Transactions of the Royal Society B: Biological Sciences (Jan. 2021). DOI: 10.1098/rstb.2019.0668.
URL: https://hal.inria.fr/hal-03005035.

[3] L. Pedrelli and X. Hinaut. ‘Hierarchical-Task Reservoir for Online Semantic Analysis from Continu-
ous Speech’. In: IEEE Transactions on Neural Networks and Learning Systems (27th Sept. 2021). DOI:
10.1109/TNNLS.2021.3095140. URL: https://hal.inria.fr/hal-03031413.

[4] N. P. Rougier. Scientific Visualization: Python + Matplotlib. 15th Nov. 2021. URL: https://hal.inr

ia.fr/hal-03427242.

[5] N. P. Rougier and G. I. Detorakis. ‘Randomized Self Organizing Map’. In: Neural Computation (2021).

URL: https://hal.inria.fr/hal-03017448.

[6] R. Sankar, N. P. Rougier and A. Leblois. ‘Computational benefits of structural plasticity, illustrated
in songbirds’. In: Neuroscience & Biobehavioral Reviews (2021). URL: https://hal.archives-ou
vertes.fr/hal-03416314.

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Inria Annual Report 2023

12.2 Publications of the year

International journals

[7] A. Aussel, I. C. Fiebelkorn, S. Kastner, N. J. Kopell and B. R. Pittman-Polletta. ‘Interacting rhythms
enhance sensitivity of target detection in a fronto-parietal computational model of visual attention’.
In: eLife 12 (2023), e67684. DOI: 10.7554/eLife.67684. URL: https://hal.science/hal-0415
0575.

[8]

S. Bachy, F. Chambon, P. Corieri, O. Goletti, S. Hoarau, V. Komis, T. Massart, K. Mens, G. Parriaux, C.
Poulmaire, M. Romero, M. Rafalska and T. Viéville. ‘Création d’une Communauté d’Apprentissage
de l’Informatique’. In: Adjectif : analyses et recherches sur les TICE (Nov. 2023). URL: https://hal
.science/hal-04350397.

[9] D. Birre, T. Feuillet, R. Lagalis, J. Milian, F. Alexandre, D. Sheeren, R. Serrano-Notivoli, M. Vignal
and M. Y. Bader. ‘A new method for quantifying treeline-ecotone change based on multiple spatial
pattern dimensions’. In: Landscape Ecology 38 (6th Jan. 2023), p. 18. DOI: 10.1007/s10980-022-
01589-4. URL: https://hal.inrae.fr/hal-03942369.

[10] N. Vardalakis, A. Aussel, N. P. Rougier and F. B. Wagner. ‘A dynamical computational model of theta
generation in hippocampal circuits to study theta-gamma oscillations during neurostimulation’.
In: eLife (2024). URL: https://inria.hal.science/hal-04383365.

International peer-reviewed conferences

[11] S. Reddy Oota, V. Agarwal, M. Marreddy, M. Gupta and R. S. Bapi. ‘Speech Taskonomy: Which
Speech Tasks are the most Predictive of fMRI Brain Activity?’ In: Proc. INTERSPEECH 2023. INTER-
SPEECH 2023 - 24th INTERSPEECH Conference. Dublin, Ireland, 20th Aug. 2023, pp. 5167–5171.
DOI: 10.21437/Interspeech.2023-121. URL: https://hal.science/hal-04131475.

[12] S. Reddy Oota, M. Gupta and M. Toneva. ‘Joint processing of linguistic properties in brains and
language models’. In: 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
New Orleans, United States, 10th Dec. 2023. URL: https://inria.hal.science/hal-04400635.

[13] S. Reddy Oota, N. Trouvain, F. Alexandre and X. Hinaut. ‘MEG Encoding using Word Context
Semantics in Listening Stories’. In: INTERSPEECH 2023 - 24th INTERSPEECH Conference. Dublin,
Ireland, 20th Aug. 2023. URL: https://hal.science/hal-04148324.

Conferences without proceedings

[14] F. Hyseni, N. P. Rougier and A. Leblois. ‘Attractor dynamics drive flexible timing in birdsong’. In:
ICANN 2023 - 32nd International Conference on Artificial Neural Networks. Heraklion, Greece,
26th Sept. 2023. URL: https://hal.science/hal-04168450.

[15] F. Hyseni, N. P. Rougier and A. Leblois. ‘Comparative study of the synfire chain and ring attractor
model for timing in the premotor nucleus in male Zebra Finches’. In: ESANN 2023 proceedings,
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine
Learning. Bruges (Belgium) and online event, 4-6 October 2023, i6doc.com publ., ISBN 978-2-87587-
088-9. Available from h t t p : // w w w . i 6 d o c . c o m / e n / . . ESANN 2023 - The 31 European
Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Bruges/ Hybrid, Belgium: Ciaco - i6doc.com, 26th Sept. 2023, pp. 647–652. DOI: 10.14428/esann
/2023.ES2023-120. URL: https://hal.science/hal-04218404.

[16]

S. Reddy Oota, M. Marreddy, M. Gupta and R. S. Bapi. ‘How does the brain process syntactic struc-
ture while listening?’ In: ACL 2023 - The 61st Annual Meeting of the Association for Computational
Linguistics. Toronto, Canada, 9th July 2023. URL: https://hal.science/hal-04131510.

[17] N. Trouvain, D. Das and X. Hinaut. ‘ReservoirPy Sprint: Amélioration de ReservoirPy, un outil simple
de Reservoir Computing’. In: PyConFR. Bordeaux, France, 16th Feb. 2023. URL: https://hal.sci
ence/hal-04401731.

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Scientific books

25

[18] M. Romero, L. Heiser, A. Lepage, A. Gagnebien, A. Bonjour, A. Lagarrigue, A. Palaude, C. Boulord,
C.-A. Gagneur, C. Mercier, C. Caucheteux, D. Guidoni-Stoltz, F. Tressols, J. Henry, F. Alexandre, J.-F.
Céci, J. Camponovo, L. Fouché, J.-F. Métral, L.-B. Hodgkins, M.-H. Comte, M. Durampart, P. Corieri,
P. Olry, P. Reboul, P. Bonfils, S. B. Amor, S. Collin, S. Ciavaldini-Cartaut, T. Viéville, V. Batifol and
Y.-A. L. Borgne. Teaching and learning in the age of artificial intelligence. Vol. Livre blanc. Canopé,
1st Mar. 2023. URL: https://hal.science/hal-04013223.

Scientific book chapters

[19] F. Alexandre, M.-H. Comte, A. Lagarrigue, C. Mercier, A. Palaude, M. Romero and T. Viéville. ‘Un
MOOC pour initier a l’IA : « Intelligence Artificielle avec Intelligence »’. In: Enseigner et apprendre à
l’ère de l’intelligence artificielle. 1st Mar. 2023, p. 96. URL: https://inria.hal.science/hal-04
037791.

[20] F. Alexandre, M.-H. Comte, A. Lagarrigue and T. Viéville. ‘L’IA pour mieux apprendre et apprehender
L’IA’. In: Enseigner et apprendre à l’ère de l’intelligence artificielle. 1st Mar. 2023, p. 96. URL: https:
//inria.hal.science/hal-04037828.

Doctoral dissertations and habilitation theses

[21] S. Dagar. ‘Computational modeling of cognitive control for rule-guided behavior’. Université de

Bordeaux, 21st Apr. 2023. URL: https://theses.hal.science/tel-04301585.

[22] F. Hyseni. ‘Dynamique temporelle dans la représentation neuronale des tâches sensorimotrices :
étude du réseau de contrôle du chant chez les oiseaux’. PhD thesis. Université de Bordeaux, Dec.
2023. URL: https://www.theses.fr/2023BORD0351.

Reports & preprints

[23] P. Bernard, B. Hate and M. Laval. Symboling : utiliser des structures symboliques dotées d’une
métrique. RR-9499. Inria & Labri, Univ. Bordeaux, 27th Feb. 2023, p. 18. URL: https://inria.hal
.science/hal-04006574.

[24] M.-H. Comte, S. Djeballah, M. Fourny, S. Hoarau, A. Juton, M. Khaneboubi, A. Lagarrigue, T. Massart,
C. Poulmaire, V. Prince, S. Renouf, T. Viéville and J.-M. Vincent. A French-speaking training platform
for teachers, dedicated to learning computer science, in secondary school. RR-9514. Inria, 26th June
2023, p. 15. URL: https://inria.hal.science/hal-04140155.

[25] L. Heiser and M. Romero. Learner-centred perspectives in artificial intelligence education. Université

Côte d’Azur, 1st Mar. 2023, p. 13. URL: https://hal.science/hal-04114236.

[26] C. Léger, G. Hamon, E. Nisioti, X. Hinaut and C. Moulin-Frier. Evolving Reservoirs for Meta Rein-

forcement Learning. 2023. URL: https://inria.hal.science/hal-04354303.

[27] A. Palaude and T. Viéville. DBA and K-means clustering : Explainability problems for research on
behaviors and strategies. RR-9522. Inria & Labri, Université Bordeaux, 4th Dec. 2023, p. 17. URL:
https://inria.hal.science/hal-04254844.

[28] W. Radji, C. Léger and L. Bardisbanian. Early Empirical Results on Reinforcement Symbolic Learning.
RR-9509. Inria & Labri, Univ. Bordeaux, 23rd May 2023, p. 9. URL: https://inria.hal.science
/hal-04103795.

[29] S. Reddy Oota, M. Gupta, R. S. Bapi, G. Jobard, F. Alexandre and X. Hinaut. Deep Neural Networks
and Brain Alignment: Brain Encoding and Decoding (Survey). 14th July 2023. URL: https://hal.s
cience/hal-04162064.

[30] M. Romero. Data management plan of the Special Interest Group Scol_IA Renewal of digital practices
and creative uses of digital and AI. Université Côte d’Azur, 13th Mar. 2023, p. 15. URL: https://hal
.science/hal-04123600.

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Inria Annual Report 2023

Other scientific publications

[31] M. H. Anik and M. Romero. ‘Exploring the Scope of Practicing Basic Science Process Skills in the
Creative Problem-SolvingExperiences through Modular Cube Robotics Technologies in Students
across VariousEducational Levels’. In: Hybrid research symposium within the CreaComp research
programme “Co-creative problem-solving and technology enhanced learning from a sociocultural
perspective”. Nice, France, 17th Oct. 2023. URL: https://hal.science/hal-04248910.

[32] S. R. Oota, N. Trouvain, G. Jobard, F. Alexandre and X. Hinaut. ‘MEG Encoding using Neural
Language & Speech Models and Shared Context Semantics in Listening Stories’. In: Society for the
Neurobiology of Language (SNL 2023). Marseille, France, 24th Oct. 2023. URL: https://hal.scie
nce/hal-04401769.

[33] S. Reddy Oota, N. Trouvain, F. Alexandre and X. Hinaut. ‘Past Word Context Enables Better MEG
Encoding Predictions than Current Word in Listening Stories’. In: NeuroFrance 2023. Lyon, France,
24th May 2023. URL: https://inria.hal.science/hal-04154794.

[34] M. Romero and F. Alexandre. ‘Towards computational learning science: modeling creative problem
solving in child-robot interaction through behavioral learning analytics’. In: ISLS 2023 - Annual
Meeting of the International Society of the Learning Sciences. Montréal, Canada, 10th June 2023.
URL: https://hal.science/hal-04124474.

12.3 Other

Scientific popularization

[35] F. Alexandre. ‘Comment fonctionne ChatGPT ? Décrypter son nom pour comprendre les modèles
de langage’. In: The Conversation France (4th June 2023). URL: https://inria.hal.science/ha
l-04156226.

[36] F. Alexandre. ‘De Cambridge Analytica à ChatGPT, comprendre comment l’IA donne un sens aux
mots’. In: The Conversation France (19th May 2023). URL: https://inria.hal.science/hal-04
156230.

[37] H. Chateau-Laurent and X. Hinaut. ‘Sample my Canary’. In: 2023 Drôles d’Objets - Un nouvel art

de faire. Nancy, France, 15th May 2023. URL: https://inria.hal.science/hal-04154799.

[38] A. Palaude, C. Mercier and M. Romero. ‘Représenter l’apprentissage humain dans des problèmes
ouverts’. In: Dataquitaine 2023 - IA, Recherche Opérationnelle & Data Science. Bordeaux, France,
2nd Mar. 2023. URL: https://inria.hal.science/hal-04028721.

Educational activities

[39] M. Romero, B. Sylvie, P. Eloïse, I. Guillaume, T. Jessy, K. Raoul, H. Pierre, D. Caroline, T. Donia,
L. Alexandre and P. Azeneth. ‘Une approche historico-culturelle de l’activité (CHAT) pour analyser
les enjeux du numérique en éducation’. Doctoral. Canada, 16th June 2023. URL: https://hal.sc
ience/hal-04140492.

12.4 Cited publications

[40] F. Alexandre. ‘Biological Inspiration for Multiple Memories Implementation and Cooperation’. In:
International Conference on Computational Intelligence. Ed. by V. Kvasnicka, P. Sincak, J. Vascak
and R. Mesiar. 2000.

[41] D. H. Ballard, M. M. Hayhoe, P. K. Pook and R. P. N. Rao. ‘Deictic codes for the embodiment of
cognition’. In: Behavioral and Brain Sciences 20.04 (1997), pp. 723–742. URL: http://dx.doi.org
/10.1017/S0140525X97001611.

[42]

J. Bergstra, D. Yamins, D. D. Cox et al. ‘Hyperopt: A python library for optimizing the hyperpara-
meters of machine learning algorithms’. In: Proceedings of the 12th Python in science conference.
Vol. 13. Citeseer. 2013, p. 20.

Project MNEMOSYNE

27

[43] D. Mitra. ‘Asynchronous relaxations for the numerical solution of differential equations by parallel

processors’. In: SIAM J. Sci. Stat. Comput. 8.1 (1987), pp. 43–58.

[44] D.P. Bertsekas and J.N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Athena

Scientific, 1997.

[45] K. Doya. ‘What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?’

In: Neural Networks 12 (1999), pp. 961–974.

[46]

J. Fix, N. P. Rougier and F. Alexandre. ‘A dynamic neural field approach to the covert and overt
deployment of spatial attention’. In: Cognitive Computation 3.1 (2011), pp. 279–293. DOI: 10.1007
/s12559-010-9083-y. URL: http://hal.inria.fr/inria-00536374/en.

[47] T. Mitchell. Machine Learning. Mac Graw-Hill Press, 1997.

[48] P. Dayan and L.F. Abbott. Theoretical Neuroscience : Computational and Mathematical Modeling of

Neural Systems. MIT Press, 2001.

[49] R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J. Bower, M. Diesmann, A. Morrison, P. H.
Goodman, F. Harris, M. Zirpe, T. Natschläger, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lansner,
O. Rochel, T. Viéville, E. Muller, A.P. Davison, S. E. Boustani and A. Destexhe. ‘Simulation of networks
of spiking neurons: a review of tools and strategies’. In: Journal of Computational Neuroscience
23.3 (2007), pp. 349–398.

[50] R.C. O’Reilly and Y. Munakata. Computational Explorations in Cognitive Neuroscience: Understand-

ing the Mind by Simulating the Brain. Cambridge, MA, USA: MIT Press, 2000.

[51] N. P. Rougier. ‘Dynamic Neural Field with Local Inhibition’. In: Biological Cybernetics 94.3 (2006),

pp. 169–179.

[52] N. P. Rougier and A. Hutt. ‘Synchronous and Asynchronous Evaluation of Dynamic Neural Fields’.

In: J. Diff. Eq. Appl. (2009).

[53] S. Amari. ‘Dynamic of pattern formation in lateral-inhibition type neural fields’. In: Biological

Cybernetics 27 (1977), pp. 77–88.

[54]

S. Coombes. ‘Waves, bumps and patterns in neural field theories’. In: Biol. Cybern. 93 (2005), pp. 91–
108.

[55] L. Squire. ‘Memory systems of the brain: a brief history and current perspective’. In: Neurobiol.

Learn. Mem. 82 (2004), pp. 171–177.

[56] W. Taouali, T. Viéville, N. P. Rougier and F. Alexandre. ‘No clock to rule them all’. In: Journal of

Physiology 105.1-3 (2011), pp. 83–90.

[57] G. Toussaint. ‘The Euclidean algorithm generates traditional musical rhythms’. In: Renaissance

Banff: Mathematics, Music, Art, Culture. 2005, pp. 47–56.

[58] T. Trappenberg. Fundamentals of Computational Neuroscience. Oxford University Press, 2002.

[59] T. Viéville. ‘An unbiased implementation of regularization mechanisms’. In: Image and Vision
Computing 23.11 (2005), pp. 981–998. URL: http://authors.elsevier.com/sd/article/S026
2885605000909.

[60] W. Gerstner and W.M. Kistler. Spiking Neuron Models: Single Neurons, Populations, Plasticity.

Cambridge University Press: Cambridge University Press, 2002.
<scratchpad>
- Main topic: Research Centre at the University of Bordeaux
- Key points:
  - Project MNEMOSYNE: a research project focusing on computational neuroscience and medicine
  - Integrative and cognitive neuroscience: modeling the brain as a system of active memories
  - Computational neuroscience: developing methods from computer science to explore brain function
  - Machine learning: applying adaptive properties of the nervous system to artificial systems
  - Autonomous robotics: using robots to test autonomous learning systems
- Structure: The document is organized into sections, including project details, research program, application domains, social and environmental responsibility, and more
- Potential artifacts to ignore: links, HTML tags, web-related elements
</scratchpad>

<final_summary>
The MNEMOSYNE project is a research centre at the University of Bordeaux, focusing on computational neuroscience and medicine. The project aims to model the brain as a system of active memories and develop methods from computer science to explore brain function. It explores the application of machine learning to adaptive properties of the nervous system and uses autonomous robotics to test autonomous learning systems. The project also addresses social and environmental responsibility, including reducing carbon footprint and promoting scientific activities. Overall, MNEMOSYNE seeks to advance our understanding of the brain and its functions, with potential applications in medicine, education, and beyond.
The MNEMOSYNE project is a research centre at the University of Bordeaux, focusing on computational neuroscience and medicine. The project aims to model the brain as a system of active memories and develop methods from computer science to explore brain function. It explores the application of machine learning to adaptive properties of the nervous system and uses autonomous robotics to test autonomous learning systems. The project also addresses social and environmental responsibility, including reducing carbon footprint and promoting scientific activities. Overall, MNEMOSYNE seeks to advance our understanding of the brain and its functions, with potential applications in medicine, education, and beyond.