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1. Decision making under uncertainty: a quasimetric approach.

Author

Steve N'Guyen, Clément Moulin-Frier, and Jacques Droulez

Subjects
Medicine, Science
Abstract

We propose a new approach for solving a class of discrete decision making problems under uncertainty with positive cost. This issue concerns multiple and diverse fields such as engineering, economics, artificial intelligence, cognitive science and many others. Basically, an agent has to choose a single or series of actions from a set of options, without knowing for sure their consequences. Schematically, two main approaches have been followed: either the agent learns which option is the correct one to choose in a given situation by trial and error, or the agent already has some knowledge on the possible consequences of his decisions; this knowledge being generally expressed as a conditional probability distribution. In the latter case, several optimal or suboptimal methods have been proposed to exploit this uncertain knowledge in various contexts. In this work, we propose following a different approach, based on the geometric intuition of distance. More precisely, we define a goal independent quasimetric structure on the state space, taking into account both cost function and transition probability. We then compare precision and computation time with classical approaches.

Published
2013
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2. Bayesian time-domain multiple sound source localization for a stochastic machine

Author

Emmanuel Mazer, Laurent Girin, Marvin Faix, Raphael Frisch, Jacques Droulez, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CRISSP (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Signal processing, Computer science, Bayesian probability, 020206 networking & telecommunications, Statistical model, specific hardware, 02 engineering and technology, Acoustic source localization, Bayesian stochastic machine, time-domain processing, Bayesian inference, Robustness (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Multiple sound source localization, Time domain, Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract

We propose a time-domain multiple sound source localization (SSL) method based on Bayesian inference. This method is specifically designed to run on the stochastic machines (SM) that we are currently developing to perform efficient low-level sensor signal processing with ultra-low power consumption. The proposed SSL method is divided into two main parts. First, a probabilistic model is run on 50 very short time frames (3. 75ms each) of multichannel recorded signals. Second, the results obtained on the different frames are fused to obtain a final localization map. Using the system in a supervised way allows to extract estimated source locations by selecting as many maxima as there are sources in the room. We explain how this method is implemented on a SM. Experiments are presented to illustrate the performance and robustness of the resulting system.

Published
2019
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3. A Bayesian stochastic machine for sound source localization

Author

Laurent Girin, Raphaël Laurent, Pierre Bessière, Laurent Fesquet, Raphael Frisch, Emmanuel Mazer, Augustin Lux, Jacques Droulez, Marvin Faix, Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interpretation and Modelling of Images and Videos (PERCEPTION ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CRISSP (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interpretation and Modelling of Images and Videos (PERCEPTION), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), and ANR-11-LABX-0025-01,PERSYVAL-lab,Systèmes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Stochastic computing, Computer science, Stochastic process, Posterior probability, Bayesian probability, 02 engineering and technology, Acoustic source localization, Bayesian inference, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, 0305 other medical science, Likelihood function, Algorithm
Abstract

International audience; Compared to conventional processors, stochastic computing architectures have strong potential to speed up computation time and to reduce power consumption. We present such an architecture, called Bayesian Machine (BM), dedicated to solving Bayesian inference problems. Given a set of noisy signals provided by low-level sensors, a BM estimates the posterior probability distribution of an unknown target information. In the present study, a BM is used to solve a sound source localization (SSL) problem: the BM computes the probability distribution of the position of a sound source given acoustic signals captured by a set of microphones. Assuming free field wave propagation (no reverberations), we express the SSL problem as the maximization of a likelihood function fed with audio features provided by the time-frequency (TF) analysis of the captured audio waves. The proposed BM uses bitwise parallel sampling to fuse the resulting multi-channel information. As the number of channels to fuse is large, the standard BM architecture encounters the so-called " time dilution problem " (long delays are necessary to obtain valid samples). We tackle this problem by using max-normalization of the distributions combined with a periodic re-sampling of the bit streams after processing a reasonably small subset of evidences. Finally, we compare the localization performance of the proposed machine with the results obtained using a standard version of the machine. The re-sampling leads to an impressive acceleration factor of 10³ in the computation.

Published
2017

4. Approximation Enhancement for Stochastic Bayesian Inference

Author

Pierre Bessière, Joseph S. Friedman, Jacques Droulez, Jorge Lobo, Damien Querlioz, Institut d'électronique fondamentale (IEF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and University of Coimbra

Subjects
Stochastic computing, Computer science, business.industry, Applied Mathematics, Inference, [SCCO.COMP]Cognitive science/Computer science, 020206 networking & telecommunications, 02 engineering and technology, 021001 nanoscience & nanotechnology, Bayesian inference, Machine learning, computer.software_genre, Theoretical Computer Science, Bayesian statistics, Approximate inference, Artificial Intelligence, Frequentist inference, 0202 electrical engineering, electronic engineering, information engineering, Statistical inference, Fiducial inference, Artificial intelligence, 0210 nano-technology, business, computer, Software
Abstract

International audience; Advancements in autonomous robotic systems have been impeded by the lack of a specialized computational hardware that makes real-time decisions based on sensory inputs. We have developed a novel circuit structure that efficiently approximates naïve Bayesian inference with simple Muller C-elements. Using a stochastic computing paradigm, this system enablesreal-time approximate decision-making with an area-energy-delay product nearly one billiontimes smaller than a conventional general-purpose computer. In this paper, we propose severaltechniques to improve the approximation of Bayesian inference by reducing stochastic bitstream autocorrelation. We also evaluate the effectiveness of these techniques for various naïve inference tasks and discuss hardware considerations, concluding that these circuits enable approximate Bayesian inferences while retaining orders-of-magnitude hardware advantagescompared to conventional general-purpose computers.

Published
2017
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5. Bayesian Sensor Fusion with Fast and Low Power Stochastic Circuits

Author

Raphaël Laurent, Jorge Lobo, Jacques Droulez, Pierre Bessière, M. Awais Aslam, Emmanuel Mazer, Alexandre Coninx, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], and University of Coimbra

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, 020208 electrical & electronic engineering, Bayesian probability, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Energy consumption, Sensor fusion, Bayesian inference, 020202 computer hardware & architecture, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Hardware Architecture, Computer engineering, Probabilistic logic network, Logic gate, 0202 electrical engineering, electronic engineering, information engineering, Applications of artificial intelligence
Abstract

International audience; —As the physical limits of Moore's law are being reached, a research effort is launched to achieve further performance improvements by exploring computation paradigms departing from standard approaches. The BAMBI project (Bottom-up Approaches to Machines dedicated to Bayesian Inference) aims at developing hardware dedicated to probabilistic computation , which extends logic computation realised by boolean gates in current computer chips. Such probabilistic computing devices would allow to solve faster and at a lower energy cost a wide range of Artificial Intelligence applications, especially when decisions need to be taken from incomplete data in an uncertain environment. This paper describes an architecture where very simple operators compute on a time coding of probability values as stochastic signals. Simulation tests and a reconfigurable logic hardware implementation demonstrated the feasibility and performances of the proposed inference machine. Hardware results show this architecture can quickly solve Bayesian sensor fusion problems and is very efficient in terms of energy consumption.

Published
2016

6. Quick and energy-efficient bayesian computing of binocular disparity using stochastic digital signals

Author

Jacques Droulez, Alexandre Coninx, Pierre Bessière, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), European Project: 618024,EC:FP7:ICT,FP7-ICT-2013-C,BAMBI(2014), Coninx, Alexandre, and Bottom-up Approaches to Machines dedicated to Bayesian Inference - BAMBI - - EC:FP7:ICT2014-01-01 - 2016-12-31 - 618024 - VALID

Subjects
FOS: Computer and information sciences, 0209 industrial biotechnology, Theoretical computer science, Computer Science - Artificial Intelligence, Computer science, Computer Vision and Pattern Recognition (cs.CV), Bayesian probability, Bayesian inference, Computer Science - Computer Vision and Pattern Recognition, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Theoretical Computer Science, 020901 industrial engineering & automation, [SCCO.COMP] Cognitive science/Computer science, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], energy efficiency, Stochastic computing, business.industry, Applied Mathematics, Probabilistic logic, Robotics, sensorimotor processing, Artificial Intelligence (cs.AI), Probability distribution, Binocular disparity, binocular disparity, stochastic computing, 020201 artificial intelligence & image processing, Distributed memory, Artificial intelligence, hardware implementation, business, Software
Abstract

Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics., Preprint of article submitted for publication in International Journal of Approximate Reasoning and accepted pending minor revisions

Published
2016
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7. Design of Stochastic Machines Dedicated to Approximate Bayesian inferences

Author

Jacques Droulez, Pierre Bessière, Raphaël Laurent, Marvin Faix, Emmanuel Mazer, PERVASIVE INTERACTION (PERVASIVE INTERACTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), European Project: 618024,EC:FP7:ICT,FP7-ICT-2013-C,BAMBI(2014), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, Bayesian probability, Inference, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, 01 natural sciences, Bayesian Programming, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], symbols.namesake, Joint probability distribution, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Stochastic computing, 010308 nuclear & particles physics, 020206 networking & telecommunications, Computer Science Applications, Human-Computer Interaction, Bayesian statistics, symbols, stochastic computing, Stochastic optimization, Information Systems, Gibbs sampling, Von Neumann architecture
Abstract

International audience; We present an architecture and a compilation toolchain for stochastic machines dedicated to Bayesian inferences. These machines are not Von Neumann and code information with stochastic bitstreams instead of using floating point representations. They only rely on stochastic arithmetic and on Gibbs sampling to perform approximate inferences. They use banks of binary random generators which capture the prior knowledge on which the inference is built. The output of the machine is devised to continuously sample the joint probability distribution of interest. While the method is explained on a simple example, we show that our machine computes a good approximation of the solution to a problem intractable in exact inference.

Published
2016
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8. Bayesian Inference With Muller C-Elements

Author

Pierre Bessière, Damien Querlioz, Jacques Droulez, Joseph S. Friedman, L. E. Calvet, Institut d'électronique fondamentale (IEF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, and Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010302 applied physics, Computer science, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, 02 engineering and technology, 021001 nanoscience & nanotechnology, Bayesian inference, Machine learning, computer.software_genre, 01 natural sciences, Bayes' theorem, Naive Bayes classifier, Frequentist inference, Component (UML), 0103 physical sciences, Feature (machine learning), Influence diagram, Leverage (statistics), [INFO]Computer Science [cs], Artificial intelligence, Electrical and Electronic Engineering, 0210 nano-technology, business, computer
Abstract

International audience; Bayesian inference is a powerful approach for integrating independent conflicting information for decision-making. Though an important component of robotic, biological, and other sensory-motors systems, general-purpose computers perform Bayesian inference with limited efficiency. Here we show that Bayesian inference can be efficiently performed with stochastic signals, which are particularly adapted to novel low power nano-devices that exhibit faults and device variations. A simple Muller C-element directly implements Bayes' rule. Complex inferences are performed by C-element trees, which compute the probability of an event based on multiple independent sources of evidence. A naïve Bayesian spam filter circuit is demonstrated as a pedagogical application, and design techniques for improving circuit functionality are described. Limitations of this structure are discussed in terms of signal autocorrelation. The stochastic inference structure is exceptionally robust to faults, an essential feature of decision circuits, and can therefore leverage the increased efficiency of emerging nanodevices. This hardware implementation of Bayesian inference is extremely area and power efficient, with an area-energy-delay product several orders of magnitude less than the conventional floating point implementation. These results open a pathway for a direct stochastic hardware implementation of Bayesian inference, enabling a new class of embedded decision circuits for robotics and medical applications.

Published
2016
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9. Robust Fusion of Trackers using on-line Drift Prediction

Author

Jacques Droulez, Stéphane Herbin, Isabelle Leang, Benoît Girard, ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, and Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Fusion, BitTorrent tracker, business.industry, Computer science, [SCCO.NEUR]Cognitive science/Neuroscience, Process (computing), 020207 software engineering, Context (language use), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Video tracking, 0202 electrical engineering, electronic engineering, information engineering, Fuse (electrical), [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Computer vision, Noise (video), Artificial intelligence, business, Set (psychology), ComputingMilieux_MISCELLANEOUS
Abstract

Visual object tracking is a standard function of computer vision that has been the source of numerous propositions. This diversity of approaches leads to the idea of trying to fuse them and take advantage of the strengths of each of them while controlling the noise they may introduce in some circumstances. The work presented here describes a generic framework for the combination of trackers, where fusion may occur at different levels of the processing chain. The fusion process is governed by the online detection of abnormal behavior either from specific features provided by each tracker, or from out of consensus detection. The fusion of three trackers exploiting complementary designs and features is evaluated on 72 fusion schemes. Thorough experiments on 12 standard video sequences and on a new set of 13 videos addressing typical difficulties faced by vision systems used in the demanding context of driving assistance, show that using fusion improves greatly the performance of each individual tracker, and reduces by a factor two the probability of drifting.

Published
2015
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10. How the Learning Path and the Very Structure of a Multifloored Environment Influence Human Spatial Memory

Author

Jacques Droulez, Guillaume Thibault, Daniel Bennequin, Laurent Dollé, Alain Berthoz, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), and Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)

Subjects
Horizontal and vertical, business.industry, Computer science, representation, General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, Perspective (graphical), Experimental and Cognitive Psychology, Spatial cognition, Memorization, Psychiatry and Mental health, Clinical Psychology, Mode (computer interface), spatial cognition, Human–computer interaction, Path (graph theory), Psychology (miscellaneous), Artificial intelligence, business, Representation (mathematics), Protocol (object-oriented programming), Applied Psychology, Research Article, navigation and spatial memory
Abstract

International audience; Few studies have explored how humans memorize landmarks in complex multifloored buildings. They have observed that participants memorize an environment either by floors or by vertical columns, influenced by the learning path. However, the influence of the building's actual structure is not yet known. In order to investigate this influence, we conducted an experiment using an object-in-place protocol in a cylindrical building to contrast with previous experiments which used rectilinear environments. Two groups of 15 participants were taken on a tour with a first person perspective through a virtual cylindrical three-floored building. They followed either a route discovering floors one at a time, or a route discovering columns (by simulated lifts across floors). They then underwent a series of trials, in which they viewed a camera movement reproducing either a segment of the learning path (familiar trials), or performing a shortcut relative to the learning trajectory (novel trials). We observed that regardless of the learning path, participants better memorized the building by floors, and only participants who had discovered the building by columns also memorized it by columns. This expands on previous results obtained in a rectilinear building, where the learning path favoured the memory of its horizontal and vertical layout. Taken together, these results suggest that both learning mode and an environment's structure influence the spatial memory of complex multifloored buildings.

Published
2015
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11. Robust sequence storage in bistable oscillators

Author

David Colliaux, Pierre Bessière, Jacques Droulez, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), FET, and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010302 applied physics, Physics, Circuit noise, Bistability, Stochastic process, Differential equation, Computation, 02 engineering and technology, 021001 nanoscience & nanotechnology, Topology, 01 natural sciences, Stochastic differential equation, Control theory, Robustness (computer science), 0103 physical sciences, Noise tolerance, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, 0210 nano-technology
Abstract

The versatility of nanodevices dynamics may allow original architectures for computation but care should be taken to handle fluctuations arising at this scale. In the network of oscillatory units which we propose, bistability with down-quiescent and up-oscillatory states enable tolerance to noise in storage and retrieval dynamics for patterns or sequences. We illustrate this by simulations of the stochastic differential equations for a network with connectivity corresponding to stored patterns.

Published
2014

12. How does horizontal and vertical navigation influence spatial memory of multifloored environments?

Author

Manuel Vidal, Alain Berthoz, Achille Pasqualotto, Jacques Droulez, Guillaume Thibault, EDF (EDF), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), and Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)

Subjects
Adult, Male, Linguistics and Language, Horizontal and vertical, Computer science, Experimental and Cognitive Psychology, Column (database), 050105 experimental psychology, Language and Linguistics, Memorization, Navigation and spatial memory, User-Computer Interface, Young Adult, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Perceptual learning, Orientation, Architecture, Humans, Learning, 0501 psychology and cognitive sciences, Computer vision, Set (psychology), Communication, business.industry, 05 social sciences, Brain, Recognition, Psychology, Vertical plane, Spatial cognition, Middle Aged, 16. Peace & justice, Sensory Systems, Space Perception, Mental Recall, [SCCO.PSYC]Cognitive science/Psychology, Trajectory, Female, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract

International audience; Although a number of studies have been devoted to 2-D navigation, relatively little is known about how the brain encodes and recalls navigation in complex multifloored environments. Previous studies have proposed that humans preferentially memorize buildings by a set of horizontal 2-D representations. Yet this might stem from the fact that environments were also explored by floors. Here, we have investigated the effect of spatial learning on memory of a virtual multi-floored building. Two groups of 28 participants watched a computer movie that showed either a route along floors one at a time or travel between floors by simulated lifts, consisting in both cases of a 2-D trajectory in the vertical plane. To test recognition, the participants viewed a camera movement that either replicated a segment of the learning route (familiar segment) or did not (novel segment—i.e., shortcuts). Overall, floor recognition was not reliably superior to column recognition , but learning along a floor route produced a better spatial memory performance than did learning along a column route. Moreover, the participants processed familiar segments more accurately than novel ones, not only after floor learning, but crucially, also after column learning, suggesting a key role of the observation mode on the exploitation of spatial memory.

Published
2013
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13. Motion from the past. A new method to infer vestibular capacities of extinct species

Author

Philippe Janvier, Ronan Allain, Jacques Droulez, Daniel Bennequin, Romain David, Alain Berthoz, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Centre de recherche sur la Paléobiodiversité et les Paléoenvironnements (CR2P), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Chimie Et Interdisciplinarité : Synthèse, Analyse, Modélisation (CEISAM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Institut de Mathématiques de Jussieu (IMJ), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), and Université de Nantes (UN)-Université de Nantes (UN)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, Angular acceleration, genetic structures, Motion (geometry), Rotation, 010603 evolutionary biology, 01 natural sciences, 03 medical and health sciences, Acceleration, Orientation (geometry), otorhinolaryngologic diseases, medicine, Computer vision, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Vestibular system, 0303 health sciences, Semicircular canal, business.industry, General Engineering, Anatomy, medicine.anatomical_structure, Head (vessel), sense organs, Artificial intelligence, business, [SDU.STU.PG]Sciences of the Universe [physics]/Earth Sciences/Paleontology, Geology
Abstract

The vestibular system detects head movement in space and maintains visual and postural stability. The semicircular canal system is responsible for registering head rotation. How it responds to head rotation is determined by the rotational axis and the angular acceleration of the head, as well as the sensitivity and orientation of each semicircular canal. The morphological parameters of the semicircular canals are supposed to allow an optimal detection of head rotations induced by some behaviours, especially locomotor. We propose a new method of semicircular canal analysis, based on the computation of central streamlines of virtually reconstructed labyrinths. This method allows us to ascertain the functional structure of the semicircular canal system and to infer its capacity to detect particular head rotations, induced by particular behaviours. In addition, this method is well-suited for datasets provided by any kind of serial sectioning methods, from MRI to μCT scanning and even mechanical serial sectioning, of extant and extinct taxa.

Published
2011
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14. Implementation of a probabilistic inference in a photoreceptor cell

Author

Pierre Bessière, Jacques Droulez, Audrey Houillon, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
0303 health sciences, Computer science, business.industry, Cognitive Neuroscience, Neuroscience (miscellaneous), [SCCO.COMP]Cognitive science/Computer science, Pattern recognition, Probabilistic inference, Photoreceptor cell, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, medicine.anatomical_structure, Developmental Neuroscience, Probabilistic CTL, medicine, Artificial intelligence, business, 030217 neurology & neurosurgery, 030304 developmental biology
Published
2009

15. Processing 3D form and 3D motion: respective contributions of attention-based and stimulus-driven activity

Author

V. Cornilleau-Pérès, Jacques Droulez, Anne-Lise Paradis, Jean-Baptiste Poline, Institut d'imagerie neurofonctionnelle (IIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Ecole Nationale Supérieure des Télécommunications (ENST)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École des hautes études en sciences sociales (EHESS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de neurosciences cognitives et d'imagerie cérébrale (LENA), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École des hautes études en sciences sociales (EHESS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Ecole Nationale Supérieure des Télécommunications (ENST)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), and Paradis, Anne-Lise

Subjects
Adult, Male, genetic structures, Neural substrate, Cognitive Neuroscience, media_common.quotation_subject, Motion Perception, Feature selection, Stimulus (physiology), 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Imaging, Three-Dimensional, Perception, Structure from motion, Humans, 0501 psychology and cognitive sciences, Attention, Visual Pathways, 10. No inequality, Stimulus-driven processing, media_common, Visual Cortex, Communication, Brain Mapping, 3D motion, business.industry, Dorsal pathway, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, 05 social sciences, Magnetic Resonance Imaging, Visual motion, 3D shape, Form Perception, Neurology, Neural processing, Evoked Potentials, Visual, Female, Selective attention, Psychology, business, Ventral pathway, Neuroscience, 030217 neurology & neurosurgery, Photic Stimulation, Motion system, MRI
Abstract

International audience; This study aims at segregating the neural substrate for the 3D-form and 3D-motion attributes in structure-from-motion perception, and at disentangling the stimulus-driven and endogenous-attention-driven processing of these attributes. Attention and stimulus were manipulated independently: participants had to detect the transitions of one attribute –form, 3D motion or colour– while the visual stimulus underwent successive transitions of all attributes. We compared the BOLD activity related to form and 3D motion in three conditions: stimulus-driven processing (unattended transitions), endogenous attentional selection (task) or both stimulus-driven processing and attentional selection (attended transitions). In all conditions, the form versus 3D-motion contrasts revealed a clear dorsal/ventral segregation. However, while the form-related activity is consistent with previously described shape-selective areas, the activity related to 3D motion does not encompass the usual “visual motion” areas, but rather corresponds to a high-level motion system, including IPL and STS areas. Second, we found a dissociation between the neural processing of unattended attributes and that involved in endogenous attentional selection. Areas selective for 3D-motion and form showed either increased activity at transitions of these respective attributes or decreased activity when subjects’ attention was directed to a competing attribute. We propose that both facilitatory and suppressive mechanisms of attribute selection are involved depending on the conditions driving this selection. Therefore, attentional selection is not limited to an increased activity in areas processing stimulus properties, and may unveil different functional localization from stimulus modulation.

Published
2008
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16. Stereoscopic depth perception in peripheral field and global processing of horizontal disparity gradient pattern

Author

Bjorn Drobe, Annie Monot, Jacques Droulez, Céline Devisme, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Centre de recherches sur la conservation des documents graphiques (CRCDG), and Ministère de la Culture et de la Communication (MCC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Adult, Male, Vision Disparity, Psychometrics, genetic structures, media_common.quotation_subject, education, Poison control, Stimulus (physiology), 050105 experimental psychology, horizontal disparity, 03 medical and health sciences, Stereoscopic depth, 0302 clinical medicine, fluids and secretions, surface perception, Foveal, Perception, parasitic diseases, Psychophysics, Humans, 0501 psychology and cognitive sciences, disparity gradient, media_common, Mathematics, Depth Perception, Vision, Binocular, Communication, eccentricity, business.industry, 05 social sciences, deformation, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, Geodesy, Sensory Systems, Peripheral, body regions, Ophthalmology, Stereopsis, perceived depth, Sensory Thresholds, Female, Visual Fields, business, Photic Stimulation, 030217 neurology & neurosurgery
Abstract

International audience; This study investigated how the visual system detects a surface deviation from planar, induced by, crossed or uncrossed, horizontal disparities continuously increasing with eccentricity. Binocular disparities increased linearly and concentrically, between two given eccentricities. The thresholds of deformation detection were gathered using a method in which observers halted a dynamic stimulus. The thresholds were substantially higher than those measured by the control experiment using a method of constant stimuli. Results, using the adjustment method, highlighted lower discrimination thresholds for uncrossed disparities than for crossed disparities. For all the directions of disparity, thresholds varied similarly as a function of eccentricity, however two observations could be pointed out: thresholds of peripheral start depended on disparity gradient and starting eccentricity; fovea start thresholds did not depend on disparity gradient alone. Data suggests that, in peripheral field, the visual system was more sensitive to uncrossed disparities than crossed disparities, relative to the frontoparallel plane. According to a verbal report from observers, the reference used for the perceptive judgment appears not to be the screen plane but rather the peripheral stimulus. Moreover, in the deformation detection of planar surfaces, horizontal disparities processing depends on the eccentric location of the disparities. It could be global for the peripheral locations and could be based more on depth contrast for the central locations.

Published
2008
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17. A unified probabilistic model of the perception of three-dimensional structure from optic flow

Author

Francis Colas, Pierre Bessière_aff n, Mark Wexler_aff n, Jacques Droulez_aff n, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Environnements et outils pour le Génie Logiciel Industriel (ADELE), Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
General Computer Science, Motion Perception, [SCCO.COMP]Cognitive science/Computer science, Rigidity (psychology), Bayesian inference, Models, Biological, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Discrimination, Psychological, Psychophysics, Structure from motion, Humans, 0501 psychology and cognitive sciences, Computer vision, Motion perception, Projection (set theory), Mathematics, Depth Perception, Models, Statistical, business.industry, Optical Illusions, 05 social sciences, Statistical model, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Inverse problem, Pattern Recognition, Visual, [SCCO.PSYC]Cognitive science/Psychology, 030221 ophthalmology & optometry, Bayesian programming, Artificial intelligence, business, Photic Stimulation, Biotechnology
Abstract

Human observers can perceive the three- dimensional (3-D) structure of their environment using various cues, an important one of which is optic flow. The motion of any point’s projection on the retina depends both on the point’s movement in space and on its distance from the eye. Therefore, retinal motion can be used to extract the 3-D structure of the environment and the shape of objects, in a process known as structure-from-motion (SFM). However, because many combinations of 3-D structure and motion can lead to the same optic flow, SFM is an ill-posed inverse problem. The rigidity hypothesis is a constraint supposed to formally solve the SFM problem and to account for human performance. Recently, however, a number of psychophysical results, with both moving and stationary human observers, have shown that the rigidity hypothesis alone cannot account for human performance in SFM tasks, but no model is known to account for the new results. Here, we construct a Bayesian model of SFM based mainly on one new hypothesis, that of stationarity, coupled with the rigidity hypothesis. The predictions of the model, calculated using a new and powerful methodology called Bayesian programming, account for a wide variety of experimental findings.

Published
2008
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18. A unified probabilistic model of the perception of three-dimensionnal structure from optic flow

Author

Francis Colas, Jacques Droulez, Mark Wexler, Pierre Bessière, and Laugier, Christian

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH]
Abstract

Human observers can perceive the threedimensional (3-D) structure of their environment using various cues, an important one of which is motion parallax. The motion of any points projection on the retina depends both on the points movement in space and on its distance from the eye. Therefore, retinal motion can be used to extract the 3-D structure of the environment and the shape of objects, in a process known as structurefrom- motion (sfm). However, because many combinations of 3-D structure and motion can lead to the same optic flow, sfm is an ill-posed inverse problem. The rigidity assumption is a constraint supposed to formally solve the sfm problem and to account for human performance. Recently, however, a number of psychophysical results, in both moving and stationary human observers, have shown that the rigidity assumption alone cannot account for sfm, but no model is known to account for the new results. Here, we construct a Bayesian model of sfm based on only one new assumption, that of stationarity, coupled with the assumption of rigidity. The predictions of the model, calculated using a new and powerful methodology called Bayesian programming, account for a wide variety of experimental findings.

Published
2006

19. Keeping track of objects while exploring an informationally impoverished environment: Local deictic versus global spatial strategies

Author

Nicolas J. Bullot, Jacques Droulez, and Zenon W. Pylyshyn

Subjects
Computer science, direction, [SCCO.COGDYNAMICS]Cognitive science/domain_scco.cogdynamics, [SCCO.COGPSY]Cognitive science/domain_scco.cogpsy, Deixis, spatial memory, Track (rail transport), [SCCO.REASONNING]Cognitive science/domain_scco.reasonning, Sensory Systems, Deitic reference, Ophthalmology, Human–computer interaction, [SHS.PHIL.PERCEPTION]Humanities and Social Sciences/Philosophy/domain_shs.phil.perception, location
Abstract

This study investigates a new experimental paradigm called the Modified Traveling Salesman Problem (Bullot & Droulez, submitted). This task requires subjects to visit once and only once n invisible targets in a 2D display, using a virtual vehicle controlled by the subject. Subjects can only see the directions of the targets from the current location of the vehicle, displayed by a set of oriented segments that can be viewed inside a circular window surrounding the vehicle. Two conditions were compared. In the “allocentric” condition, subjects see the vehicle move across the screen and change orientation under their command. The “egocentric” condition is similar except for how the information is provided: the position and orientation of the vehicle icon remains fixed at the center of the screen and only target directions, as indicated by the oriented segments, change as the subject “moves” the vehicle. The unexpected finding was that this task can be performed, in either condition, for up to 10 targets. We consider two possible strategies that might be used, a location-based strategy and a segment strategy. The location-based strategy relies on spatial memory and attempts to infer the locations of all the targets. The segment strategy is more local and focuses on the directional segments themselves, keeping track of the ones that represent already-visited targets. A number of observations suggest that the segment strategy was used, at least for larger numbers of targets. According to our hypothesis, keeping track of the segments requires one to use indexical reference for associating the segments with their status in the task - given by current status predicates Visited(x) or Not-visited(x) -, perhaps using visual indexes (Pylyshyn, 2001), deictic pointers (Ballard et al., 1997), or object files (Kahneman et al, 1992).

Published
2003

20. Self-motion and the perception of stationary objects

Author

Mark Wexler, Ivan Lamouret, Jacques Droulez, Francesco Panerai, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), and Wexler, Mark

Subjects
Computer science, media_common.quotation_subject, Models, Neurological, Motion Perception, Rigidity (psychology), 050105 experimental psychology, 03 medical and health sciences, Motion, Behavioral Neuroscience, 0302 clinical medicine, Form perception, Perceptual Cognitive Psychology, Perception, Orientation, Psychophysics, Structure from motion, Humans, 0501 psychology and cognitive sciences, Computer vision, Motion perception, media_common, Multidisciplinary, business.industry, 05 social sciences, Observer (special relativity), Form Perception, Head Movements, [SCCO.PSYC] Cognitive science/Psychology, [SCCO.PSYC]Cognitive science/Psychology, Artificial intelligence, business, Depth perception, 030217 neurology & neurosurgery
Abstract

One of the ways that we perceive shape is through seeing motion1,2,3. Visual motion may be actively generated (for example, in locomotion), or passively observed. In the study of the perception of three-dimensional structure from motion, the non-moving, passive observer in an environment of moving rigid objects has been used as a substitute1 for an active observer moving in an environment of stationary objects; this ‘rigidity hypothesis’ has played a central role in computational and experimental studies of structure from motion4,5. Here we show that this is not an adequate substitution because active and passive observers can perceive three-dimensional structure differently, despite experiencing the same visual stimulus: active observers' perception of three-dimensional structure depends on extraretinal information about their own movements. The visual system thus treats objects that are stationary (in an allocentric, earth-fixed reference frame) differently from objects that are merely rigid. These results show that action makes an important contribution to depth perception, and argue for a revision of the rigidity hypothesis to incorporate the special case of stationary objects.

Published
2001

21. Visual perception of motion and 3-D structure from motion: an fMRI study

Author

Anne-Lise Paradis, Alain Berthoz, P. F. Van De Moortele, E. Lobel, D. Le Bihan, Jean-Baptiste Poline, Valérie Cornilleau-Pérès, Jacques Droulez, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Laboratoire de Neurosciences Fonctionnelles et Pathologies (LNFP), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, IFR de Neuroimagerie Fonctionnelle (IFR 49), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Service Hospitalier Frédéric Joliot (SHFJ), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Male, Visual perception, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Motion Perception, MESH: Magnetic Resonance Imaging, 0302 clinical medicine, Form perception, Reference Values, MESH: Depth Perception, Parietal Lobe, Structure from motion, Brownian motion, MESH: Brain Mapping, Physics, Brain Mapping, MESH: Parietal Lobe, medicine.diagnostic_test, 05 social sciences, Mathematical analysis, MESH: Reference Values, Magnetic Resonance Imaging, MESH: Photic Stimulation, MESH: Motion Perception, Female, Occipital Lobe, MESH: Occipital Lobe, Adult, Cognitive Neuroscience, Curvature, 050105 experimental psychology, 03 medical and health sciences, Cellular and Molecular Neuroscience, medicine, Humans, 0501 psychology and cognitive sciences, MESH: Form Perception, Motion perception, Depth Perception, Communication, MESH: Humans, business.industry, MESH: Adult, MESH: Male, Form Perception, business, Functional magnetic resonance imaging, Parallax, MESH: Female, Photic Stimulation, 030217 neurology & neurosurgery
Abstract

International audience; Functional magnetic resonance imaging was used to study the cortical bases of 3-D structure perception from visual motion in human. Nine subjects underwent three experiments designed to locate the areas involved in (i) motion processing (random motion versus static dots), (ii) coherent motion processing (expansion/ contraction versus random motion) and (iii) 3-D shape from motion reconstruction (3-D surface oscillating in depth versus random motion). Two control experiments tested the specific influence of speed distribution and surface curvature on the activation results. All stimuli consisted of random dots so that motion parallax was the only cue available for 3-D shape perception. As expected, random motion compared with static dots induced strong activity in areas V1/V2, V5+ and the superior occipital gyrus (SOG; presumptive V3/V3A). V1/V2 and V5+ showed no activity increase when comparing coherent motion (expansion or 3-D surface) with random motion. Conversely, V3/V3A and the dorsal parieto-occipital junction were highlighted in both comparisons and showed gradually increased activity for random motion, coherent motion and a curved surface rotating in depth, which suggests their involvement in the coding of 3-D shape from motion. Also, the ventral aspect of the left occipito-temporal junction was found to be equally responsive to random and coherent motion stimuli, but showed a specific sensitivity to curved 3-D surfaces compared with plane surfaces. As this region is already known to be involved in the coding of static object shape, our results suggest that it might integrate various cues for the perception of 3-D shape.

Published
2000
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22. A 6-dof device to measure head movements in active vision experiments: geometric modeling and metric accuracy

Author

V. Cornilleau-Pérès, Jacques Droulez, Sylvain Hanneton, Francesco Panerai, Hanneton, Sylvain, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), and Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, Transducers, MESH: Head Movements, MESH: Research Support, Non-U.S. Gov't, 050105 experimental psychology, MESH: Calibration, 03 medical and health sciences, 0302 clinical medicine, MESH: Depth Perception, Position (vector), Computer Systems, Calibration, Humans, 0501 psychology and cognitive sciences, Computer vision, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], MESH: Models, Theoretical, Active vision, Depth Perception, MESH: Humans, Human head, business.industry, Orientation (computer vision), General Neuroscience, 05 social sciences, MESH: Computer Systems, Body movement, Equipment Design, Models, Theoretical, Head Movements, Head (vessel), [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, Geometric modeling, business, 030217 neurology & neurosurgery, MESH: Equipment Design, MESH: Transducers
Abstract

This work describes a technique for measuring human head movements in 3D space. Rotations and translations of the head are tracked using a light helmet fastened to a multi-joint mechanical structure. This apparatus has been designed to be used in a series of psycho-physiological experiments in the field of active vision, where position and orientation of the head need to be measured in real time with high accuracy, high reliability and minimal interference with subject movements. A geometric model is developed to recover the position information and its parameters are identified through a calibration procedure. The expected accuracy, derived on the basis of the pure geometric model and the sensor resolution, is compared with the real accuracy, obtained by performing repetitive measurements on a calibration fixture. The outcome of the comparison confirms the validity of the proposed solution which turns out to be effective in providing measurement of head position with an overall accuracy of 0.6 mm and sampling frequency above 1 kHz.

Published
1999

23. Does the brain use sliding variables for the control of movements?

Author

Alain Berthoz, Jean-Jacques E. Slotine, Sylvain Hanneton, Jacques Droulez, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Nonlinear System Laboratory (NSL), and Massachusetts Institute of Technology (MIT)

Subjects
Adult, General Computer Science, Computer science, Stability criterion, Movement, Models, Neurological, Poison control, Kinematics, MESH: Movement, MESH: Psychomotor Performance, MESH: Research Support, Non-U.S. Gov't, 050105 experimental psychology, Inverse dynamics, MESH: Linear Models, 03 medical and health sciences, MESH: Brain, 0302 clinical medicine, Control theory, MESH: Models, Neurological, Humans, 0501 psychology and cognitive sciences, MESH: Humans, Quantitative Biology::Neurons and Cognition, 05 social sciences, Feed forward, Brain, MESH: Adult, Control system, Linear Models, Trajectory, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychomotor Performance, 030217 neurology & neurosurgery, Biotechnology
Abstract

Delays in the transmission of sensory and motor information prevent errors from being instantaneously available to the central nervous system (CNS) and can reduce the stability of a closed-loop control strategy. On the other hand, the use of a pure feedforward control (inverse dynamics) requires a perfect knowledge of the dynamic behavior of the body and of manipulated objects. Sensory feedback is essential both to accommodate unexpected errors and events and to compensate for uncertainties about the dynamics of the body. Experimental observations concerning the control of posture, gaze and limbs have shown that the CNS certainly uses a combination of closed-loop and open-loop control. Feedforward components of movement, such as eye saccades, occur intermittently and present a stereotyped kinematic profile. In visuo-manual tracking tasks, hand movements exhibit velocity peaks that occur intermittently. When a delay or a slow dynamics are inserted in the visuo-manual control loop, intermittent step-and-hold movements appear clearly in the hand trajectory. In this study, we investigated strategies used by human subjects involved in the control of a particular dynamic system. We found strong evidence for substantial nonlinearities in the commands produced. The presence of step-and-hold movements seemed to be the major source of nonlinearities in the control loop. Furthermore, the stereotyped ballistic-like kinematics of these rapid and corrective movements suggests that they were produced in an open-loop way by the CNS. We analyzed the generation of ballistic movements in the light of sliding control theory assuming that they occurred when a sliding variable exceeded a constant threshold. In this framework, a sliding variable is defined as a composite variable (a combination of the instantaneous tracking error and its temporal derivatives) that fulfills a specific stability criterion. Based on this hypothesis and on the assumption of a constant reaction time, the tracking error and its derivatives should be correlated at a particular time lag before movement onset. A peak of correlation was found for a physiologically plausible reaction time, corresponding to a stable composite variable. The direction and amplitude of the ongoing stereotyped movements seemed also be adjusted in order to minimize this variable. These findings suggest that, during visually guided movements, human subjects attempt to minimize such a composite variable and not the instantaneous error. This minimization seems to be obtained by the execution of stereotyped corrective movements.

Published
1997
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24. Perception of three-dimensional shape from ego- and object-motion: Comparison between small- and large-field stimuli

Author

Jacques Droulez, C. C. A. M. Gielen, Tjeerd M. H. Dijkstra, and V. Cornilleau-Pérès

Subjects
Male, Active vision, Rotation, Movement, media_common.quotation_subject, Rigidity (psychology), Geometry, Curvature, Planar, Optics, Perception, Humans, Structure from motion, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Mathematics, media_common, Structure-from-motion, Depth Perception, business.industry, Regular polygon, Proprioception, Sensory Systems, Visual field, Form Perception, Ophthalmology, Three-dimensional shape, Sensory Thresholds, Visual Fields, business, Head, Human
Abstract

We compare the performance in the detection of the shape of concave, planar and convex surfaces for small-field (8 deg) and large-field (90 deg) stimuli. Shape is perceived from head translations, object translations and object rotations. We find large differences between small-field and large-field stimulation. For small-field stimulation performance is best for object rotation, intermediate for self-motion and worst for object translation. For large-field stimulation performance is similar across conditions. Few errors on the sign of the curvature are found for self-motion for both field sizes, indicating that self-motion information disambiguates the curvature sign. For object rotation with small-field stimulation, the concave-convex ambiguity is strong with many apparent deformations. In contrast, large-field curvature signs are always accurately reported, suggesting that the weight of the rigidity hypothesis depends on field size.

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25. Visual perception of planar orientation: dominance of static depth cues over motion cues

Author

Jacques Droulez, Bernard Bourdoncle, Christian Miege, Mark Wexler, Valérie Cornilleau-Pérès, and Emmanuel Marin

Subjects
Adult, Male, Visual perception, Rotation, Optical flow, Motion Perception, Geometry, Field of view, Optics, Vision, Monocular, Motion parallax, Orientation, Humans, Motion perception, Texture, Physics, Monocular, business.industry, Optic flow, Sensory Systems, Ophthalmology, Pattern Recognition, Visual, Depth perception, Female, Cues, business, Parallax, Monocular vision, Photic Stimulation
Abstract

We measured the ability to report the tilt (direction of maximal slope) of a plane under monocular viewing conditions, from static depth cues (square grid patterns) and motion parallax (small rotations of the plane about a frontoparallel axis). These two cues were presented separately, or simultaneously. In the latter case they specified tilts that were either collinear (coherent case) or orthogonal (conflict case). The field of view was small (8 degrees) or large (60 degrees). In small field, for motion parallax, the reported tilt depends strongly on the orientation of the plane relative to the rotation axis, being totally ambiguous when tilt is collinear with the rotation axis. In contrast, in large field, the reported tilt depends little on this variable, and is accurately specified by motion cues. In both cases static cues strongly dominated the tilt reports. Hence static grid patterns constitute robust tilt cues, which can dominate contradictory tilt indications from motion parallax, and should be considered as essential for the visual orientation during locomotion, or the immersion in virtual reality environments.

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26. Skyrmion Gas Manipulation for Probabilistic Computing

Author

Daniele Pinna, Pierre Bessière, Julie Grollier, F. Abreu Araujo, Damien Querlioz, Vincent Cros, Joo-Von Kim, Jacques Droulez, Institut d'électronique fondamentale (IEF), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Unité mixte de physique CNRS/Thales (UMPhy CNRS/THALES), Centre National de la Recherche Scientifique (CNRS)-THALES, Laboratoire de Physiologie de la Perception et de l'Action (LPPA), and Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, Computation, FOS: Physical sciences, General Physics and Astronomy, Motion (geometry), 02 engineering and technology, 01 natural sciences, Computational science, Mesoscale and Nanoscale Physics (cond-mat.mes-hall), 0103 physical sciences, 010306 general physics, ComputingMilieux_MISCELLANEOUS, [PHYS]Physics [physics], Stochastic computing, Condensed Matter - Mesoscale and Nanoscale Physics, Skyrmion, Disordered Systems and Neural Networks (cond-mat.dis-nn), Computational Physics (physics.comp-ph), Condensed Matter - Disordered Systems and Neural Networks, 021001 nanoscience & nanotechnology, Probabilistic computing, Character (mathematics), Logic gate, Scalability, 0210 nano-technology, Physics - Computational Physics
Abstract

The topologically protected magnetic spin configurations known as skyrmions offer promising applications due to their stability, mobility and localization. In this work, we emphasize how to leverage the thermally driven dynamics of an ensemble of such particles to perform computing tasks. We propose a device employing a skyrmion gas to reshuffle a random signal into an uncorrelated copy of itself. This is demonstrated by modelling the ensemble dynamics in a collective coordinate approach where skyrmion-skyrmion and skyrmion-boundary interactions are accounted for phenomenologically. Our numerical results are used to develop a proof-of-concept for an energy efficient ($\sim\mu\mathrm{W}$) device with a low area imprint ($\sim\mu\mathrm{m}^2$). Whereas its immediate application to stochastic computing circuit designs will be made apparent, we argue that its basic functionality, reminiscent of an integrate-and-fire neuron, qualifies it as a novel bio-inspired building block., Comment: 41 pages, 20 figures

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