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33 results on '"Frédéric Alexandre"'

1. Reinforcement Symbolic Learning

Author

Chloé Mercier, Thierry Viéville, Frédéric Alexandre, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Innovation et Numérique pour l'Education (LINE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA), AEx AIDE, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, and Université Nice Sophia Antipolis (... - 2019) (UNS)

Subjects
Models for Learning Sciences, Ontology Edit Distances, Computer science, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, [SHS.EDU]Humanities and Social Sciences/Education, Perspective (graphical), 0102 computer and information sciences, Ontology (information science), 01 natural sciences, Task (project management), Symbolic learning, Reinforcement Symbolic Learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, 010201 computation theory & mathematics, Reinforcement learning, Artificial intelligence, Reinforcement, Representation (mathematics), business, Set (psychology), 030217 neurology & neurosurgery
Abstract

International audience; Complex problem solving involves representing structured knowledge, reasoning and learning, all at once. In this prospective study, we make explicit how a reinforcement learning paradigm can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an ontology. This preliminary paper is only a set of ideas while feasibility verification is still a perspective of this work.

Published
2021

2. A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning

Author

Surampudi Bapi Raju, Jérémie Naudé, Frédéric Alexandre, Pramod Kaushik, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), International Institute of Information Technology [Hyperabad] (IIIT-H), Sorbonne Université (SU), Neurosciences Paris Seine (NPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), We would like to acknowledge the following grants which have been a major support to this research:- Indo-French CEFIPRA Grant for the project Basal Ganglia at Large(No.DST-INRIA2013-02/Basal Ganglia dated 13-09-2014)- Internships programme at INRIA, 6 month Internship with Team Mnemosyne at INRIA Bordeaux - Sud-Ouest, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Neuroscience Paris Seine (NPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Naudé, Jérémie, Institut de Génomique Fonctionnelle (IGF), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
Dissociation (neuropsychology), Mean squared prediction error, Dopamine, [SDV]Life Sciences [q-bio], Cognitive Neuroscience, Conditioning, Classical, Experimental and Cognitive Psychology, 03 medical and health sciences, Behavioral Neuroscience, 0302 clinical medicine, Reward, mental disorders, medicine, GABAergic Neurons, gamma-Aminobutyric Acid, 030304 developmental biology, Mathematics, 0303 health sciences, Dopaminergic Neurons, musculoskeletal, neural, and ocular physiology, Ventral striatum, Ventral Tegmental Area, Classical conditioning, Ventral tegmental area, [SDV] Life Sciences [q-bio], medicine.anatomical_structure, nervous system, GABAergic, Neuroscience, 030217 neurology & neurosurgery, psychological phenomena and processes, medicine.drug
Abstract

Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.

Published
2022
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3. A Structure of Restricted Boltzmann Machine for Modeling System Dynamics

Author

Olivier Bach, Frédéric Alexandre, Denis Penninckx, Alain Hugget, Guillaume Padiolleau, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), IEEE, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest

Subjects
Computer Science::Machine Learning, Restricted Boltzmann machine, Artificial neural network, Unsupervised Deep Learning, business.industry, Stochastic process, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], System dynamics, 03 medical and health sciences, 0302 clinical medicine, State Representation Learning, 0202 electrical engineering, electronic engineering, information engineering, Unsupervised learning, Reinforcement learning, 020201 artificial intelligence & image processing, Artificial intelligence, business, Representation (mathematics), Feature learning, Factored Restricted Boltzmann Machine, 030217 neurology & neurosurgery
Abstract

International audience; This paper presents a new approach for learning transition function in state representation learning (SRL) for control. While state-of-the-art methods use different deterministic neural networks to learn forward and inverse state transition functions independently with auto-supervised learning, we introduce a bidirectional stochastic model to learn both transition functions. We aim at using the uncertainty of the model on its predictions as an intrinsic motivation for exploration to enhance the representation learning. More, using the same model to learn both transition functions allows sharing the parameters, which can reduce their number and should increase the embedding quality of the representation. We use a factored restricted Boltzmann machine (fRBM) based model, enhanced with dedicated structure for learning system dynamics and transitions with shared parameters. The presented work focuses on building the structure of the bidirectional transition model for unsupervised learning. Our fRBM structure is directly inspired from physics interactions between inputs and outputs in reinforcement learning framework. We compare different training algorithms for learning the model that must be able to predict observable random variables to be used in SRL framework. Our structure is not restricted to any type of observable, nevertheless in this paper we focus on learning dynamics from the OpenAI Gym environment Swinging Pendulum. We show that the proposed structure is able to learn bidirectional transition function and performs well in prediction task.

Published
2020
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4. When Artificial Intelligence and Computational Neuroscience meet

Author

Philippe Gaussier, Benoît Girard, Peter Ford Dominey, Nicolas P. Rougier, Frédéric Alexandre, Mehdi Khamassi, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Architectures et modèles d'Adptation et de la cognition (AMAC), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Springer, Gaussier, Philippe, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, and CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Computer science, media_common.quotation_subject, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computational intelligence, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Perception, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Set (psychology), 030304 developmental biology, media_common, 0303 health sciences, Computational neuroscience, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], [SCCO.NEUR] Cognitive science/Neuroscience, Intelligent decision support system, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Cognition, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Salient, Robot, Artificial intelligence, business, 030217 neurology & neurosurgery, [SDV.NEU.SC] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences
Abstract

Computational Intelligence and Artificial Intelligence are both aiming at building machines and softwares capable of intelligent behavior. They are consequently prone to interactions, even if the latter is not necessarily interested in understanding how cognition emerges from the brain substrate. In this chapter, we enumerate, describe and discuss the most important fields of interactions. Some are methodological and are concerned with information representation, processing and learning. At the functional level, the focus is set on major cognitive functions like perception, navigation, decision making and language. Among the salient characteristics of the critical contributions of Computational Neuroscience to the development of intelligent systems, its systemic view of the cerebral functioning is particularly precious to model highly multimodal cognitive functions like decision making and language and to design cognitive architectures for the autonomous behavior of robots.

Published
2020

5. Pattern Separation in the Hippocampus: Distinct Circuits under Different Conditions

Author

Frédéric Alexandre, Randa Kassab, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest

Subjects
0301 basic medicine, Histology, Computer science, Process (engineering), media_common.quotation_subject, Emotions, Models, Neurological, Population, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Hippocampus, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Memory, Robustness (computer science), Biological neural network, Animals, Humans, Computer Simulation, Dentate gyrus, [INFO]Computer Science [cs], education, Function (engineering), Episodic memory, media_common, Neurons, Motivation, education.field_of_study, Recall, business.industry, General Neuroscience, Computational model, [SCCO.NEUR]Cognitive science/Neuroscience, Pattern recognition, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 030104 developmental biology, Pattern separation, Artificial intelligence, Anatomy, Neural coding, business, 030217 neurology & neurosurgery
Abstract

International audience; Pattern separation is a fundamental hippocampal process thought to be critical for distinguishing similar episodic memories, and has long been recognized as a natural function of the dentate gyrus (DG) supporting autoassociative learning in CA3. Understanding how neural circuits within the DG-CA3 network mediate this process has received much interest, yet the exact mechanisms behind remain elusive. Here we argue for the case that sparse coding is necessary but not sufficient to ensure efficient separation and, alternatively, propose a possible interaction of distinct circuits which, nevertheless, act in synergy to produce a unitary function of pattern separation. The proposed circuits involve different functional granule-cell populations, a primary population mediates sparsification and provides recurrent excitation to the other populations which are related to additional pattern separation mechanisms with higher degrees of robustness against interference in CA3. A variety of top-down and bottom-up factors, such as motivation, emotion, and pattern similarity, controls the selection of circuitry depending on circumstances. According to this framework, a computational model is implemented and tested against model variants in a series of numerical simulations and biological experiments. The results demonstrate that the model combines fast learning, robust pattern separation and high storage capacity. It also accounts for the controversy around the involvement of the DG during memory recall, explains other puzzling findings, and makes predictions that can inform future investigations.

Published
2018
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6. A Survival Task for the Design and the Assessment of an Autonomous Agent

Author

Bhargav Teja Nallapu and Frédéric Alexandre

Subjects
Statement (computer science), Computational neuroscience, Computer science, Association (object-oriented programming), 05 social sciences, Autonomous agent, Context (language use), Cognition, computer.software_genre, Task (project management), 03 medical and health sciences, Intelligent agent, 0302 clinical medicine, Human–computer interaction, 0501 psychology and cognitive sciences, 050102 behavioral science & comparative psychology, computer, 030217 neurology & neurosurgery
Abstract

Learning to survive in a complex environment is a more relevant task for the design of intelligent autonomous machines than complex problem solving. To establish this statement, we emphasize that autonomy requires emotional- and motivational-like characteristics that are much more straightforward to define in the context of survival tasks than in problem solving which is classically considered with intelligent agents. We also propose that using a simulation platform is a good preliminary step before the design of real machines because it allows to consider another fundamental challenge, related to the association of these emotional and motivational characteristics to higher cognitive functions. These considerations are illustrated by current simulations that we are carrying out with a bio-inspired neuronal model of the cerebral architecture of primates.

Published
2018
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7. A biologically inspired neuronal model of reward prediction error computation

Author

Surampudi Bapi Raju, Pramod Kaushik, Maxime Carrere, Frédéric Alexandre, International Institute of Information Technology, Hyderabad [Hyderabad] (IIIT-H), Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université d'Hyderabad, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
0301 basic medicine, business.industry, Computer science, Computation, Mean squared prediction error, Classical conditioning, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Reinforcement learning, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, psychological phenomena and processes
Abstract

International audience; The neurocomputational model described here proposes that two dimensions involved in computation of reward prediction errors i.e magnitude and time could be computed separately and later combined unlike traditional reinforcement learning models. The model is built on biological evidences and is able to reproduce various aspects of classical conditioning, namely, the progressive cancellation of the predicted reward, the predictive firing from conditioned stimuli, and delineation of early rewards by showing firing for sooner early rewards and not for early rewards that occur with a longer latency in accordance with biological data.

Published
2017

8. Beyond Machine Learning: Autonomous Learning

Author

Frédéric Alexandre, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest

Subjects
Computational Neuroscience, Active learning (machine learning), business.industry, Computer science, Intelligent decision support system, Multi-task learning, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, Autonomous Learning, Robot learning, Task (project management), Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Computational learning theory, Inductive transfer, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Instance-based learning, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

International audience; Recently, Machine Learning has achieved impressive results, surpassing human performances, but these powerful algorithms are still unable to define their goals by themselves or to adapt when the task changes. In short, they are not autonomous. In this paper, we explain why autonomy is an important criterion for really powerful learning algorithms. We propose a number of characteristics that make humans more autonomous than machines when they learn. Humans have a system of memories where one memory can compensate or train another memory if needed. They are able to detect uncertainties and adapt accordingly. They are able to define their goals by themselves, from internal and external cues and are capable of self-evaluation to adapt their learning behavior. We also suggest that introducing these characteristics in the domain of Machine Learning is a critical challenge for future intelligent systems.

Published
2016
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9. Modeling the sensory roles of noradrenaline in action selection

Author

Maxime Carrere, Frédéric Alexandre, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Alexandre, Frédéric

Subjects
0301 basic medicine, Sensory processing, Computer science, Brain activity and meditation, business.industry, medicine.medical_treatment, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Sensory system, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Action selection, Neuromodulation (medicine), 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Action (philosophy), medicine, Autonomous learning, Artificial intelligence, Decision-making, business, Neuroscience, 030217 neurology & neurosurgery
Abstract

International audience; Noradrenaline participates in the neuromodulation of brain activity to modify the trade-off between exploration and exploitation when sensory contingencies have changed. Accordingly, attentional models of noradrenaline acting on sensory representations have been proposed. In this paper , we explore another possible action of this neuromodulator in the decision making process and report simulation results that illustrate that its role is concerned with different aspects of sensory processing. This is made possible by the extension of a classical model of action selection, to render it able to detect and to adapt to sudden changes in sensory contingencies, which is a major characteristic of autonomous learning.

Published
2016

10. A System-Level Model of Noradrenergic Function

Author

Maxime Carrere, Frédéric Alexandre, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
Computer science, media_common.quotation_subject, System level model, Activation function, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], decision making, Task (project management), 03 medical and health sciences, 0302 clinical medicine, Neuromodulation, Modulation (music), medicine, 0501 psychology and cognitive sciences, 050102 behavioral science & comparative psychology, Function (engineering), media_common, Mechanism (biology), business.industry, 05 social sciences, bio-inspiration, Neuromodulation (medicine), medicine.anatomical_structure, Artificial intelligence, business, Neuroscience, 030217 neurology & neurosurgery
Abstract

International audience; Neuromodulation is an interesting way to display different modes of functioning in a complex network. The effect of Noradrenaline has often been related to the exploration/exploitation trade-off and implemented in models by modulation of the gain of activation function. In this paper, we show that this mechanism is not sufficient for system-level networks and propose another way to implement it, exploiting reported inhibition of a striatal region by Noradrenaline. We describe here the corresponding model and report its performances in a reversal task.

Published
2016
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11. Modeling Neuromodulation as a Framework to Integrate Uncertainty in General Cognitive Architectures

Author

Frédéric Alexandre, Maxime Carrere, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest

Subjects
0301 basic medicine, Cognitive science, Accurate estimation, Computer science, business.industry, Neuromodulation, Cognition, Cognitive architecture, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], computer.software_genre, bio-inspiration, Field (computer science), Neuromodulation (medicine), decision making, 03 medical and health sciences, Intelligent agent, 030104 developmental biology, 0302 clinical medicine, Artificial general intelligence, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

International audience; One of the most critical properties of a versatile intelligent agent is its capacity to adapt autonomously to any change in the environment without overly complexifying its cognitive architecture. In this paper, we propose that understanding the role of neuromodulation in the brain is of central interest for this purpose. More precisely, we propose that an accurate estimation of the nature of uncertainty present in the environment is performed by specific brain regions and broadcast throughout the cerebral network by neuromodulators, resulting in appropriate changes in cerebral functioning and learning modes. Better understanding the principles of these mechanisms in the brain might tremendously inspire the field of Artificial General Intelligence. The original contribution of this paper is to relate the four major neuromodulators to four fundamental dimensions of uncertainty.

Published
2016
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12. No clock to rule them all

Author

Wahiba Taouali, Thierry Viéville, Frédéric Alexandre, Nicolas P. Rougier, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Scheme (programming language), Dynamic field, Central clock, Theoretical computer science, Dynamic neural fields, Computer science, Models, Neurological, Stability (learning theory), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], USable, Whole systems, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Physiology (medical), 0202 electrical engineering, electronic engineering, information engineering, Computer Simulation, computer.programming_language, Asynchronous computation, Artificial neural network, General Neuroscience, Numerical analysis, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], 020201 artificial intelligence & image processing, Numerical methods, Neural Networks, Computer, computer, Neural networks, Algorithms, 030217 neurology & neurosurgery
Abstract

International audience; This article introduces general concepts and definitions related to the notion of asynchronous computation in the framework of artificial neural networks. Using the dynamic field theory as an illustrative example, we explain why one may want to perform such asynchronous computation and how one can implement it since this computational scheme draws several consequences on both the trajectories and the stability of the whole system. After giving an unequivocal definition of asynchronous computation, we present a few practically usable methods and quantitative bounds that can guarantee most of the mesoscopic properties of the system.

Published
2011
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13. Spatio-temporal biologically inspired models for clean and noisy speech recognition

Author

Laurent Boougrain, Zouhour Neji Ben Salem, Frédéric Alexandre, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Speech perception, Computer science, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.1: Signal processing, Cognitive Neuroscience, Speech recognition, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Spatio-temporal processing, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning/I.2.6.2: Connectionism and neural nets, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, business.industry, SIGNAL (programming language), Biologically inspired models, Computer Science Applications, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Natural language processing
Abstract

International audience; Speech perception and recognition using biologically inspired models is a challenging issue not well explored yet. The paper presents two spatio-temporal biologically inspired methods for the preprocessing and learning of speech signal based on self-organization map (SOM). The experimental results are very encouraging and provide a framework that could be more studied to enhance speech perception and recognition technology.

Published
2007
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14. From biological to numerical experiments in systemic neuroscience: a simulation platform

Author

Nicolas Denoyelle, Florian Pouget, Thierry Viéville, Frédéric Alexandre, Maxime Carrere, Topology-Aware System-Scale Data Management for High-Performance Computing (TADAAM), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Mnemonic Synergy (Mnemosyne), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), A.R. Londral, P. Encarnação, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computational Neuroscience, 0303 health sciences, Computational neuroscience, Computer science, business.industry, Representation (systemics), Virtual Reality, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Cognition, Benchmarking, Virtual reality, External Data Representation, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Artificial intelligence, Neural coding, business, Neuroscience, 030217 neurology & neurosurgery, Simulation, 030304 developmental biology, Complement (set theory)
Abstract

International audience; Studying and modeling the brain as a whole is a real challenge. For such systemic models (in contrast to models of one brain area or aspect), there is a real need for new tools designed to perform complex numerical experiments, beyond usual tools distributed in the computer science and neuroscience communities. Here, we describe an effective solution, freely available on line and already in use, to validate such models of the brain functions. We explain why this is the best choice, as a complement to robotic setup, and what are the general requirements for such a benchmarking platform. In this experimental setup, the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital interoceptive cues, complex survival behaviors can be experimented. We also discuss here algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier. The key point is to possibly alternate the use of symbolic representation and of complementary and usual neural coding. As a consequence, algorithmic principles have to be considered at higher abstract level, beyond a given data representation, which is an interesting challenge.

Published
2015

15. VirtualEnaction: A Platform for Systemic Neuroscience Simulation

Author

Nicolas Denoyelle, Frédéric Alexandre, Thierry Viéville, Florian Pouget, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)

Subjects
Computational Neuroscience, 0303 health sciences, Computational neuroscience, Computer science, Virtual Reality, Environment controlled, [SCCO.COMP]Cognitive science/Computer science, Cognition, Virtual reality, Effective solution, 03 medical and health sciences, 0302 clinical medicine, Line (geometry), Neuroscience, 030217 neurology & neurosurgery, Simulation, 030304 developmental biology
Abstract

International audience; Considering the experimental study of systemic models of the brain as a whole (in contrast to models of one brain area or aspect), there is a real need for tools designed to realistically simulate these models and to experiment them. We explain here why a robotic setup is not necessarily the best choice, and what are the general requirements for such a bench-marking platform. A step further, we describe an effective solution, freely available on line and already in use to validate functional models of the brain. This solution is a digital platform where the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital somesthetic cues, complex survival behaviors can be experimented. The platform is also complemented with algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier.; Dans le domaine de l'étude expérimentale des modèles systémiques du cerveau pris dans son ensemble (par opposition aux modèles de la zone du cerveau ou une image), il y a un réel besoin d'outils conçus pour simuler de manière réaliste ces modèles et les expérimenter. Nous expliquons ici pourquoi une installation robotique n'est pas nécessairement le meilleur choix, et quelles sont les exigences générales d'une telle plateforme en terme de benchmarking. Nous décrivons alors une solution efficace, disponible gratuitement en ligne et déjà utilisées pour valider les modèles fonctionnels du cerveau. Cette solution est une plate-forme numérique où un "brainy-bot" implémente le modèle étudié et permet son intégration dans un environnement de survie contrôlé, simplifié, mais réaliste. Des entrées visuelles, tactiles et olfactive, un corps très simplifié, un bras et une commandande mootrice des yeux, en plus de la somesthésie des variavles vitales sont disponibles. De ce fait, des comportements de survie complexes peuvent être expérimentées. La plate-forme est également complétée par des modules algorithmiques de simulation de fonctions cognitives de haut niveau, facilitant le travail de construction de comportement biologiquement plausibles.

Published
2014

16. Postsynaptic dysfunction is associated with spatial and object recognition memory loss in a natural model of Alzheimer’s disease

Author

Frédéric Alexandre, Madhuchhanda Mandal, Alvaro O. Ardiles, Adrian G. Palacios, Alfredo Kirkwood, Cheril Tapia-Rojas, Nibaldo C. Inestrosa, Centro Interdisciplinario de Neurociencia de Valparaiso (CINV), Universidad de Valparaiso [Chile], Centro de Envejecimiento y Regeneracion, Pontificia Universidad Católica de Chile (UC), Mind/Brain institute and Department of Neurosciences, Johns Hopkins University (JHU), Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Centro de Regulación Celular y Patología Joaquín V. Luco, Departamento de Biología Celular y Molecular (CRCP), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Genetically modified mouse, Aging, Time Factors, Models, Neurological, Hippocampus, tau Proteins, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Models, Biological, 03 medical and health sciences, 0302 clinical medicine, Postsynaptic potential, Alzheimer Disease, Memory, Neuroplasticity, Animals, Senile plaques, Phosphorylation, Octodon, Maze Learning, 030304 developmental biology, 0303 health sciences, Memory Disorders, Multidisciplinary, Amyloid beta-Peptides, Neuronal Plasticity, biology, Memoria, Biological Sciences, biology.organism_classification, Disease Models, Animal, Pattern Recognition, Physiological, Synaptic plasticity, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychology, Neuroscience, 030217 neurology & neurosurgery
Abstract

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder associated with progressive memory loss, severe dementia, and hallmark neuropathological markers, such as deposition of amyloid-β (Aβ) peptides in senile plaques and accumulation of hyperphosphorylated tau proteins in neurofibrillary tangles. Recent evidence obtained from transgenic mouse models suggests that soluble, nonfibrillar Aβ oligomers may induce synaptic failure early in AD. Despite their undoubted value, these transgenic models rely on genetic manipulations that represent the inherited and familial, but not the most abundant, sporadic form of AD. A nontransgenic animal model that still develops hallmarks of AD would be an important step toward understanding how sporadic AD is initiated. Here we show that starting between 12 and 36 mo of age, the rodent Octodon degus naturally develops neuropathological signs of AD, such as accumulation of Aβ oligomers and phosphorylated tau proteins. Moreover, age-related changes in Aβ oligomers and tau phosphorylation levels are correlated with decreases in spatial and object recognition memory, postsynaptic function, and synaptic plasticity. These findings validate O. degus as a suitable natural model for studying how sporadic AD may be initiated.

Published
2012
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17. Visual Target Selection Emerges from a Bio-inspired Network Topology

Author

Nicolas P. Rougier, Frédéric Alexandre, Wahiba Taouali, Taouali, Wahiba, J. Kacprzyk, Neuromimetic intelligence (CORTEX), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
genetic structures, Process (engineering), Computer science, Visual Atten- tion, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Network topology, 050105 experimental psychology, Superior Colliculus, 03 medical and health sciences, 0302 clinical medicine, 0501 psychology and cognitive sciences, Computer vision, Selection (genetic algorithm), [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Biological data, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], business.industry, Orientation (computer vision), Superior colliculus, 05 social sciences, Robotics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Ocular Saccades, Saccadic masking, Dynamic Neural Field, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, 030217 neurology & neurosurgery
Abstract

International audience; The orientation of sensors toward regions of interest of the environment is an important motor activity, monitored by ancient struc- tures of the brainstem. Particularly, the superior colliculus is known to be deeply involved in visual saccadic behavior. Target selection relies on various hints including exogenous information about the nature and the position of candidate targets and endogenous information about current motivations. We present a model of the collicular structure based on bio- logical data, the speci city of which is related to the homogeneity of the underlying substratum of computation. This makes it more suitable to process massive visual ows on a distributed architecture, as it could be requested in a realistic task in autonomous robotics. The present model is restricted to the exogenous part of the visual pathway, from the retina to the superior colliculus. A realistic behavior for the selection of exogenous targets is reported here

Published
2012

18. Reinforcement learning and dimensionality reduction: A model in computational neuroscience

Author

Nishal P. Shah and Frédéric Alexandre

Subjects
Computational neuroscience, Computer science, business.industry, Dimensionality reduction, 05 social sciences, Analogy, Action selection, 050105 experimental psychology, Reduction (complexity), 03 medical and health sciences, 0302 clinical medicine, Basal ganglia, Principal component analysis, Reinforcement learning, 0501 psychology and cognitive sciences, Artificial intelligence, business, 030217 neurology & neurosurgery, Curse of dimensionality
Abstract

Basal Ganglia, a group of sub-cortical neuronal nuclei in the brain, are commonly described as the neuronal substratum to Reinforcement Learning. Since the seminal work by Schultz [1], a huge amount of work has been done to deepen that analogy, from functional and anatomic points of view. Nevertheless, a noteworthy architectural hint has been hardly explored: the outstanding reduction of dimensionality from the input to the output of the basal ganglia. Bar-Gad et al. [2] have suggested that this transformation could correspond to a Principal Component Analysis but did not explore the full functional consequences of this hypothesis. In this paper, we propose to study this mechanism within a model more realistic from a computational neuroscience point of view. Particularly, we show its feasibility when the loop is closed, in the framework of Action Selection.

Published
2011
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19. Can Self-Organization Emerge through Dynamic Neural Fields Computation?

Author

Frédéric Alexandre, Lucian Alecu, Hervé Frezza-Buet, SUPELEC-Campus Metz, Ecole Supérieure d'Electricité - SUPELEC (FRANCE), Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, Computation, [SCCO.COMP]Cognitive science/Computer science, Neural fields, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Field (computer science), 03 medical and health sciences, 0302 clinical medicine, Self organisation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, cognitive systems, Layer (object-oriented design), Flexibility (engineering), Self-organization, Quantitative Biology::Neurons and Cognition, business.industry, Perspective (graphical), self-organization, Human-Computer Interaction, 020201 artificial intelligence & image processing, dynamic neural fields, Artificial intelligence, business, 030217 neurology & neurosurgery, Software
Abstract

International audience; In this paper, dynamic neural fields are used to develop key features of a cortically-inspired computational module. Under the perspective of designing computational systems that can exhibit the flexibility and genericity of the cortical substrate, using neural field as the competition layer for self-organizing modules has to be considered. However, despite the fact that they serve as a biologically-inspired model, applying dynamic neural fields to drive self-organization is not straightforward. In order to address that issue, an original method for evaluating neural field equations is proposed, based on statistical measurements of the field behavior in some scenarios. Limitations of classical neural field equations are then quantified, and an original field equation is proposed to overcome these difficulties. The performance of the proposed field model is discussed in comparison with some previously considered models, leading to the promotion of the proposed model as a suitable mean for processing competition in cortex-like computation for cognitive systems.

Published
2011
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20. A dynamic neural field approach to the covert and overt deployment of spatial attention

Author

Frédéric Alexandre, Jérémy Fix, Nicolas P. Rougier, SUPELEC-Campus Metz, Ecole Supérieure d'Electricité - SUPELEC (FRANCE), Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Visual search, dynamic neural fields, Cognitive Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, 02 engineering and technology, Saccadic masking, Computer Science Applications, 03 medical and health sciences, Gaze-contingency paradigm, eye movements, 0302 clinical medicine, visual attention, Saccadic suppression of image displacement, Covert, Saccade, Fixation (visual), 0202 electrical engineering, electronic engineering, information engineering, emergence, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Psychology, N2pc, 030217 neurology & neurosurgery, Cognitive psychology
Abstract

International audience; Abstract The visual exploration of a scene involves the in- terplay of several competing processes (for example to se- lect the next saccade or to keep fixation) and the integration of bottom-up (e.g. contrast) and top-down information (the target of a visual search task). Identifying the neural mech- anisms involved in these processes and in the integration of these information remains a challenging question. Visual attention refers to all these processes, both when the eyes remain fixed (covert attention) and when they are moving (overt attention). Popular computational models of visual attention consider that the visual information remains fixed when attention is deployed while the primates are executing around three saccadic eye movements per second, changing abruptly this information. We present in this paper a model relying on neural fields, a paradigm for distributed, asyn- chronous and numerical computations and show that covert and overt attention can emerge from such a substratum. We identify and propose a possible interaction of four elemen- tary mechanisms for selecting the next locus of attention, memorizing the previously attended locations, anticipating the consequences of eye movements and integrating bottom- up and top-down information in order to perform a visual search task with saccadic eye movements.

Published
2011
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21. Effects of a Modulatory Feedback upon the BCM learning rule

Author

Frédéric Alexandre, Thomas Girod, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
business.industry, General Neuroscience, lcsh:QP351-495, Feed forward, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, lcsh:RC321-571, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, lcsh:Neurophysiology and neuropsychology, Learning rule, Path (graph theory), 030212 general & internal medicine, Artificial intelligence, business, Psychology, computer, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, 030217 neurology & neurosurgery
Abstract

International audience; By applying a modulatory feedback upon the BCM learning rule, we show that it is possible to introduce a bias in the learning process, thus promoting a certain kind of selectivity on the feedforward path.

Published
2009

22. Cortical basis of communication: local computation, coordination, attention

Author

Frédéric Alexandre, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Eye Movements, Computer science, Cognitive Neuroscience, media_common.quotation_subject, Models, Neurological, Computational neurosciences, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 050105 experimental psychology, Feedback, 03 medical and health sciences, 0302 clinical medicine, Cognition, Thalamus, Artificial Intelligence, Perception, Neural Pathways, medicine, Visual attention, Humans, 0501 psychology and cognitive sciences, Attention, media_common, Cognitive science, Cerebral Cortex, Neurons, Computational neuroscience, Artificial neural network, Communication, 05 social sciences, Representation (systemics), Eye movement, Information flow, Model of the cortex, medicine.anatomical_structure, Cerebral cortex, Coordination, Visual Perception, Neural Networks, Computer, 030217 neurology & neurosurgery, Algorithms
Abstract

International audience; Human communication emerges from cortical processing, known to be implemented on a regular repetitive neuronal substratum. The supposed genericity of cortical processing has elicited a series of modeling works in computational neuroscience that underline the information flows driven by the cortical circuitry. In the minimalist framework underlying the current theories for the embodiment of cognition, such a generic cortical processing is exploited for the coordination of poles of representation, as it is reported in this paper for the case of visual attention. Interestingly, this case emphasize how abstract internal referents are built to conform to memory requirements. This paper proposes that these referents are the basis for communication in humans, which is firstly a coordination and an attentional procedure with regard to their congeners.

Published
2009

23. From physiological principles to computational models of the cortex

Author

Frédéric Alexandre, Jérémy Fix, Nicolas P. Rougier, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)

Subjects
Exploit, Computer science, Models, Neurological, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Robustness (computer science), Physiology (medical), Animals, Humans, 0501 psychology and cognitive sciences, Computer Simulation, Visual search, Cerebral Cortex, Neurons, Computational model, Biological data, Computational neuroscience, business.industry, General Neuroscience, 05 social sciences, Computational Biology, Grid, Asynchronous communication, Evoked Potentials, Visual, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

Understanding the brain goes through the assimilation of an increasing amount of biological data going from single cell recording to brain imaging studies and behavioral analysis. The description of cognition at these three levels provides us with a grid of analysis that can be exploited for the design of computational models. Beyond data related to specific tasks to be emulated by models, each of these levels also lays emphasis on principles of computation that must be obeyed to really implement biologically inspired computations. Similarly, the advantages of such a joint approach are twofold: computational models are a powerful tool to experiment brain theories and assess them on the implementation of realistic tasks, such as visual search tasks. They are also a way to explore and exploit an original formalism of asynchronous, distributed and adaptive computations with such precious properties as self-organization, emergence, robustness and more generally abilities to cope with an intelligent interaction with the world. In this article, we first discuss three levels at which a cortical circuit might be observed to provide a modeler with sufficient information to design a computational model and illustrate this principle with an application to the control of visual attention.

Published
2007
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24. Comparison between two spatio-temporal organization maps for speech recognition

Author

Frédéric Alexandre, Laurent Bougrain, Zouhour Neji Ben Salem, Centre de Recherche Réseau Image SysTème Architecture et MuLtimédia (CRISTAL), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Self-organizing map, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, Artificial neural network, Computer science, Speech recognition, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Field (computer science), Domain (software engineering), Temporal database, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE, 03 medical and health sciences, 0302 clinical medicine, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Representation (mathematics), 030217 neurology & neurosurgery
Abstract

http://www.springerlink.com/content/w1q3r7r14857/; In this paper, we compare two models biologically inspired and gathering spatio-temporal data coding, representation and processing. These models are based on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of spatio-temporal data coding. The first one is based on the domain of complex numbers, while the second is based on the ISI (Inter Spike Interval). STOM is experimented in the field of speech recognition in order to evaluate its perform-ance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.

Published
2006
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25. A Distributed Model of Spatial Visual Attention

Author

Julien Vitay, Nicolas P. Rougier, Frédéric Alexandre, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), S. Wermter, G. Palm and M. Elshaw, and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Artificial neural network, Computer science, business.industry, 05 social sciences, Central nervous system, Sensory system, Robotics, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 050105 experimental psychology, Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, Salient, medicine, Key (cryptography), [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 0501 psychology and cognitive sciences, Computer vision, Artificial intelligence, Biomimetics, business, Massively parallel, 030217 neurology & neurosurgery
Abstract

International audience; Although biomimetic autonomous robotics relies on the massively parallel architecture of the brain, a key issue for designers is to temporally organize behaviour. The distributed representation of the sensory information has to be coherently processed to generate relevant actions. In the visuomotor domain, we propose here a model of visual exploration of a scene by the means of localized computations in neural populations whose architecture allows the emergence of a coherent behaviour of sequential scanning of salient stimuli. It has been implemented on a real robotic platform exploring a moving and noisy scene including several identical targets.

Published
2005
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26. Towards Word Semantics from Multi-modal Acoustico-Motor Integration: Application of the Bijama Model to the Setting of Action-Dependant Phonetic Representations

Author

Olivier Ménard, Frédéric Alexandre, Hervé Frezza-Buet, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Stefan Wermter and Günther Palm and Mark Elshaw

Subjects
Artificial neural network, Computer science, Process (engineering), business.industry, Association (object-oriented programming), Phonetics, 02 engineering and technology, Semantics, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, Modal, Learning rule, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, continuum neural field theory self-organized maps multi-modal, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Word (computer architecture), Natural language processing
Abstract

This paper presents a computational self-organizing model of multi-modal information, inspired from cortical maps. It shows how the organization in a map can be influenced by the same process occurring in other maps. We illustrate this approach on a phonetic - motor association, that shows that the organization of words can integrate motor constraints, as observed in humans.

Published
2005
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27. A model of contextual effect on reproduced extents in recall tasks: the issue of the imputed motion hypothesis

Author

Arnaud Tonnelier, Jean-Christophe Sarrazin, Frédéric Alexandre, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Adult, Male, General Computer Science, Models, Neurological, Complex system, Temporal context, Motion Perception, Neuropsychological Tests, 050105 experimental psychology, Motion (physics), 03 medical and health sciences, 0302 clinical medicine, Cognition, Memory, Econometrics, Humans, 0501 psychology and cognitive sciences, Mathematics, Observer Variation, Spatial contextual awareness, Spacetime, Recall, 05 social sciences, Kappa effect, Space Perception, Time Perception, Female, 030217 neurology & neurosurgery, Kappa, Photic Stimulation, Biotechnology
Abstract

In this article the fundamental question of space and time dependencies in the reproduction of spatial or temporal extents is studied. The functional dependence of spatial responses on the temporal context and the corresponding dependence of temporal responses on spatial context are reported as the tau and kappa effects, respectively. A common explanation suggested that the participant imputes motion to discontinuous displays. Using a mathematical model we explore the imputed velocity hypothesis and provide a globally fit model that addresses the question of sequences modelling. Our model accounts for observed data in the tau experiment. The accuracy of the model is improved introducing a new hypothesis based on small velocity variations. On the other hand, results show that the imputed velocity hypothesis fails to reproduce the kappa effect. This result definitively shows that both effects are not symmetric.

Published
2004
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28. Extraction d'invariants et prise en compte de la variabilité dans les réseaux de neurones artificiels

Author

Frédéric Alexandre, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and J. Lautrey

Subjects
connexionnisme, variability, cognitive science, 05 social sciences, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], sciences cognitives, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, variabilité, 0501 psychology and cognitive sciences, invariants, connectionism, artificial neural networks, réseaux de neurones artificiels, 030217 neurology & neurosurgery
Abstract

Contribution à un ouvrage; Le connexionnisme a pour but l'étude et le développement de modèles informatiques de réseaux de neurones artificiels (RNA) que nous présenterons ici. Nous montrerons que ces modèles permettent de réaliser des opérations intéressantes de traitement statistique de l'information pouvant participer à des fonctions de décision et nous discuterons le lien entre ces opérations et l'extraction d'invariants. Nous verrons par ailleurs pourquoi ces modèles sont souvent associés à des propriétés de robustesse, dans la mesure où ils offrent encore de bonnes performances même dans des situations où l'information à traiter est dégradée ou incomplète ainsi que dans le cas où on leur demande d'effectuer une décision par généralisation, à partir d'un ensemble restreint de cas similaires. Nous nous demanderons s'il s'agit ici d'une définition satisfaisante de la variabilité.

Published
2002

29. Biological Inspiration for Multiple Memories Implementation and Cooperation

Author

Frédéric Alexandre, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), P. Sincak, J. Vascak, and V. Kvasnicka & R. Mesiar

Subjects
Cognitive science, Biological inspiration, connexionnisme, learning, Computer science, 05 social sciences, Autonomous agent, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], biological inspiration, inspiration biologique, 050105 experimental psychology, Memorization, apprentissage, memory, 03 medical and health sciences, 0302 clinical medicine, Connectionism, mémoire, 0501 psychology and cognitive sciences, connectionism, artificial neural networks, réseaux de neurones artificiels, 030217 neurology & neurosurgery
Abstract

Colloque avec actes et comité de lecture. internationale.; International audience; Biological inspiration has led to the design of many connectionist models and mechanisms. Among them, memorization mechanisms are of particular importance to endow biologically inspired systems with efficient and consistent adaptive abilities. In this paper, we report recent modelling works of this kind, implementing procedural, episodic and working memories and making them cooperate for autonomous agent navigation.

Published
2000

30. Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing

Author

Frédéric Alexandre, Nicolas Rougier, Hervé Frezza-Buet, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), none, and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Dynamic time warping, traitement de séquences, Computer science, media_common.quotation_subject, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], connexionism, 02 engineering and technology, temporal mechanisms, 03 medical and health sciences, 0302 clinical medicine, Connectionism, sequence processing, Perception, Learning rule, 0202 electrical engineering, electronic engineering, information engineering, Pattern matching, Dimension (data warehouse), media_common, Data processing, business.industry, Task (computing), cortex, connectionnisme, 020201 artificial intelligence & image processing, Artificial intelligence, business, mécanismes temporels, 030217 neurology & neurosurgery
Abstract

Contribution à un ouvrage.; Whereas classical connectionist models can hardly cope with difficult dynamic tasks with a strong temporal factor, many temporal mechanisms inspired with neurobiological data has been proposed in the past and yield efficient time processing properties. The goal of this chapter is to show that, beyond these isolated mechanisms, their integration in a more general architectural and functional framework can potentiate their power and make them usable for non trivial behavioral tasks. We propose a cerebral framework, from the neuronal to the behavioral level, and give some applicative illustrations that underline the encouraging results obtained today.

Published
2000

31. Modeling prefrontal functions for robot navigation

Author

Frédéric Alexandre, Hervé Frezza-Buet, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)

Subjects
0303 health sciences, Quantitative Biology::Neurons and Cognition, Goal orientation, Computer science, business.industry, frontal cortex, neural planning, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Mobile robot, Computer Science::Robotics, 03 medical and health sciences, planification neuronale, 0302 clinical medicine, medicine.anatomical_structure, Action (philosophy), cortex frontal, Cortex (anatomy), medicine, Robot, Artificial intelligence, Motion planning, Prefrontal cortex, business, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Colloque avec actes et comité de lecture.; This paper presents a model of cooperation between representations of correlation in the associative cortex and goal oriented representations of action in the prefrontal cortex. The model is applied to robot navigation and gives a framework for addressing planning with numerical neural techniques.

Published
1999

32. fMRI of the dominant inferior parietal lobule

Author

René Anxionnat, Marc Braun, Thierry Jonveaux, Serge Bracard, Luc Picard, Frédéric Alexandre, Karine Angoi-Duprez, Jérome Louis, Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
medicine.medical_specialty, genetic structures, Cognitive Neuroscience, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Audiology, Literal and figurative language, behavioral disciplines and activities, 050105 experimental psychology, 03 medical and health sciences, experimentation, 0302 clinical medicine, Visual memory, medicine, 0501 psychology and cognitive sciences, In patient, Endovascular treatment, 10. No inequality, Recall, 05 social sciences, Inferior parietal lobule, hemisphere dominant, nervous system, Neurology, dominant hemisphere, Psychology, 030217 neurology & neurosurgery, psychological phenomena and processes, Dominant hemisphere, Cognitive psychology, IRM, MRI
Abstract

Colloque avec actes et comité de lecture.; Figurative and non figurative recall memory tasks highlight the angular and supramarginal gyri bilaterally and the dominant Broca's area. In patients with dominant temporal AVM or seizures foci, visual memory tasks are usefull to identify fonctional areas prior to endovascular treatment or surgery.

Published
1998

33. Impact of the Konio pathway in the thalamocortical visual system: a modeling study

Author

Frédéric Alexandre, Carlos Carvajal, Thierry Viéville, Mnemonic Synergy (Mnemosyne), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria), BMC, Ed., Institut des Maladies Neurodégénératives [Bordeaux] (IMN), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0303 health sciences, Computer science, Event (computing), [SCCO.NEUR]Cognitive science/Neuroscience, General Neuroscience, Computation, Feed forward, Information processing, [SCCO.COMP]Cognitive science/Computer science, DUAL (cognitive architecture), [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 03 medical and health sciences, Cellular and Molecular Neuroscience, Koniocellular cell, 0302 clinical medicine, Parvocellular cell, Asynchronous communication, Poster Presentation, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neuroscience, ComputingMilieux_MISCELLANEOUS, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

International audience; In the early visual system, regarding the detection of a visual event, motion information is pre-processed in the Magnocellular pathway, while it has been shown that the Koniocellular pathway [1] also plays an important role, providing a global analysis about such a kind of information processing. However, the functional interplay between these two parallel pathways remains partially understood. Previous works have attacked this question by studying the signals produced by the corresponding ganglion cells [1] and their elaboration at further steps [2], rather than proposing to model the underlying mechanisms at a mesoscopic level, i.e., focusing on the functional aspects of such dual processing. Neurobiological studies dedicated to the thalamocortical stage of the early visual system provide knowledge on particular characteristics of the system, namely: 1) the variety of cell types in the retina [2], inducing different pathways, 2) the variety of thalamocortical projections through focused vs diffuse efferences to the cortex [3], from core vs matrix (specific vs non-specific) thalamic nuclei, and 3) the variety of kinds of connectivity between thalamic, cortical and collicular areas (i.e., feedforward, feedback, shortcuts, driver and modulator information flows [4]). To figure out the impact of these multi-scale characteristics, we propose here a systemic approach at the structure level. To this end, we have developed a reduced bio-inspired distributed asynchronous model of the primitive mammal visual system, considering only motion event detection. This computational model is fed with natural image sequences, and is implemented as a large size distributed calculation [5] with thousands of computation units per structure. Thanks to the dual analysis integrating local and larger image cues, we test the system for the detection of specific dynamical patterns (which could be interpreted as, e.g., threats or targets). Our simulations aim at showing that these multi-scale interactions help improving the speed and/or quality of such critical tasks, including target selection and tracking. We expect this approach to propose an innovative answer to the interplay issues quoted here, generalizable to other visuo-motor functions. This also provides a platform that could be used as a testbed for new hypotheses. Further information flows to be included could correspond to the Parvocellular pathway (and its related functions). Other structures such as the pulvinar, or higher cortical areas, would also allow us to explore even more developed mechanisms and behaviors [3,6].

Published
2013
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