The Cat Is On the Mat. Or Is It a Dog? Dynamic Competition in Perceptual Decision Making

Recent neurobiological findings suggest that the brain solves simple perceptual decision-making tasks by means of a dynamic competition in which evidence is accumulated in favor of the alternatives. However, it is unclear if and how the same process applies in more complex, real-world tasks, such as the categorization of ambiguous visual scenes and what elements are considered as evidence in this case. Furthermore, dynamic decision models typically consider evidence accumulation as a passive process disregarding the role of active perception strategies. In this paper, we adopt the principles of dynamic competition and active vision for the realization of a biologically- motivated computational model, which we test in a visual catego- rization task. Moreover, our system uses predictive power of the features as the main dimension for both evidence accumulation and the guidance of active vision. Comparison of human and synthetic data in a common experimental setup suggests that the proposed model captures essential aspects of how the brain solves perceptual ambiguities in time. Our results point to the importance of the proposed principles of dynamic competi- tion, parallel specification, and selection of multiple alternatives through prediction, as well as active guidance of perceptual strategies for perceptual decision-making and the resolution of perceptual ambiguities. These principles could apply to both the simple perceptual decision problems studied in neuroscience and the more complex ones addressed by vision research.Peer reviewe

Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms

International audienceThe Continuous Neural Field Theory introduces biologically-inspired competition mechanisms in computational models of perception and action. This paper deals with the use of Genetic Algorithms to optimize its parameters, as to guarantee the emergence of robust cognitive properties. Such properties include the tracking of initially salient stimuli despite strong noise and distracters. Interactions between the parameter values, input dynamics and accuracy of model, as well as their implications for Genetic Algorithms are discussed. The fitness function and set of scenarios used to evaluate the parameters through simulation must be carefully chosen. Experimental results reflect an ineluctable tradeoff between generality and performance

Sensorimotor Exploration/Exploitation with Coordinating Local Predictions

International audienceThis contribution aims to show how exploration and exploitation might be tightly intertwined when modeling sensorimotor behaviors with coordinated predictive local representations. In such a framework, learning equals to creating and selecting anticipations to adapt to the dynamics of the agent and its environment. Motor actions are undertaken based on the expected outcome of the anticipations, and anticipations reinforced when successfully matching the dynamics. Reaching goals is thus equivalent to navigating through the sensorimotor space by forming and following chains of coordinated predictions. Although the agent may constantly only try to exploit its knowledge, the presence of multiple dynamic goals, the lack of correct anticipations, interactional noise or external constraint will lead to further exploration and the generation of new task-independent representations

Coordination implicite d'interactions sensorimotrices comme fondement de la cognition

This thesis promotes a cognitive infrastructure able to model sensorimotor behaviors in animals and humans. The theoretical developments upon which this infrastructure is drawn is inspired by the philosophical interactivist framework and the enaction paradigm: any system is represented by a set of active processes, in constant interaction with their subjective environment, which includes influences between these processes. Any living organism or cognitive system is therefore fractal, decomposed in different levels of emergence based on the same principles. These principles are now widely spread but appeared progressively during species evolution. Assimilation, regulation, anticipation or coordination made it possible for concurrent processes fighting for limited resources to cooperate, develop and maintain through ages. This joint evolution of environmental conditions and internal structures led to nowadays organisms, able to adapt to a genetically unpredictable environment of growing complexity.A mathematical model using the formalism of complex systems is detailed, as well as its computer implementation. The agent's dynamics is modeled by an activity field under the continuous influence of internal anticipations and external sensations. The global behavior of the agent then results from the implicit and stable coordination of localized interactive processes. On this aspect, the model extends artificial neural networks and classical probabilistic models. This essential characteristic enables applications in various domains and a unification of all levels of cognition. A set of applications validates the model, extending from physiological need satisfaction to mechanical systems handling, through auditory and visual perception. Finally and in order to extend the model to more complex behaviors in the future, technical contributions on algorithm optimization and parallel implementations are developed.Cette thèse propose une infrastructure cognitive permettant de modéliser les comportements sensori-moteurs animaux et humains. La réflexion théorique ayant conduit à cette infrastructure s'inspire du cadre philosophique interactiviste et du paradigme de l'énaction : tout système est représenté par un ensemble de processus actifs, en interaction permanente avec leur environnement propre, ce qui inclut leur influence mutuelle. Tout organisme vivant ou système cognitif peut ainsi être décomposé de manière fractale, chaque niveau d'émergence reposant sur les mêmes principes. Ces principes aujourd'hui largement répandus sont apparus durant l'évolution des espèces vivantes. L'assimilation, la régulation, l'anticipation ou encore la coordination ont ainsi permis à des processus en concurrence pour des ressources limitées de coopérer, se développer et se maintenir à travers les âges. Cette évolution conjointe des conditions environnementales et des structures internes a conduit aux organismes modernes, capables de s'adapter à un environnement génétiquement imprévisible et d'une complexité croissante.Un modèle mathématique utilisant le formalisme des systèmes complexes est détaillé, ainsi que son implémentation informatique. La dynamique de l'agent y est modélisée par un champ d'activité sous l'influence permanente d'anticipations internes et de sensations externes. Le comportement global de l'agent résulte alors de la coordination implicite et stable de processus interactifs localisés. A ce niveau, le modèle étend et complète les réseaux de neurones artificiels et les modèles probabilistes classiques. Cette caractéristique essentielle permet d'appliquer le modèle à des domaines variés et d'unifier tous les niveaux de la cognition. Le modèle est validé par un ensemble d'applications s'étendant de la satisfaction de besoins physiologiques à la manipulation de systèmes mécaniques, en passant par la perception auditive et visuelle. Enfin, et afin de pouvoir étendre ce type de modèles à des problèmes plus complexes dans le futur, des contributions techniques touchant à l'optimisation et à la parallélisation des algorithmes sont développées

Distributed model for sensorimotor control: anticipatory coordination and lateral competition

International audienceWe here propose a two layer modular infrastructure and evaluate various algorithms for competition and spatiotemporal coordination in order to control artificial sensorimotor systems. This research is part of a broader project that aims at understanding the mechanisms and constraints necessary to the emergence of adaptive behaviors from acquired distributed representations. The proposed model takes inspiration from the cerebral cortex organization at a mesoscopic scale, but targets computationally efficient implementations

Active Neural Field model of goal directed eye-movements

International audienceFor primates (including humans), interacting with objects of interest in the environment often involves their foveation, many of them not being static (e.g. other animals, relative motion due to self-induced movement). Eye movements allow the active and continuous sampling of local information, exploiting the graded precision of visual signals (e.g., due to the types and distributions of photoreceptors). Foveating and tracking targets thus requires adapting to their motion. Indeed, considering the delays involved in the transmission of retinal signals to the eye muscles, a purely reactive schema could not account for the smooth pursuit movements which maintain the target within the central visual field. Internal models have been posited to represent the future position of the target (for instance extrapolating from past observations), in order to compensate for these delays. Yet, adaptation of the sensorimotor and neural activity may be sufficient to synchronize with the movement of the target, converging to encoding its location here-and-now, without explicitly resorting to any frame of reference (Goffart et al., 2017).Committing to a distributed dynamical systems approach, we relied on a computational implementation of neural fields to model an adaptation mechanism sufficient to select, focus and track rapidly moving targets. By coupling the generation of eye-movements with dynamic neural field models and a simple learning rule, we replicated neurophysiological results that demonstrated how the monkey adapts to repeatedly observed moving targets (Bourrelly et al., 2016; Quinton & Goffart, 2018), progressively reducing the number of catch-up saccades and increasing smooth pursuit velocity (yet not going beyond the here-and-now target location). We now focus on eye-movements observed in presence of two simultaneously moving centrifugal targets (Goffart, 2016), for which the reduction to a single trajectory with some predicted dynamics (e.g., target center) is even more inappropriate

A sparse implementation of dynamic competition in continuous neural fields

International audienceThis paper introduces a sparse implementation of the Continuum Neural Field Theory, promoting a trade-off in accuracy for higher computational efficiency and alleviated constraints on the underlying model. The sparse version reproduces the main properties of previous discrete 2D implementations, such as dynamic competition leading to localized focus activity or robustness to noise and distracters, with a much higher computational speed on standard computer architectures

Competition in high dimensional spaces using a sparse approximation of neural fields

International audienceThe Continuum Neural Field Theory implements competition within topologically organized neural networks with lateral inhibitory connections. However, due to the polynomial complexity of matrix-based implementations, updating dense representations of the activity becomes computationally intractable when an adaptive resolution or an arbitrary number of input dimensions is required. This paper proposes an alternative to self-organizing maps with a sparse implementation based on Gaussian mixture models, promoting a trade-off in redundancy for higher computational efficiency and alleviating constraints on the underlying substrate. This version reproduces the emergent attentional properties of the original equations, by directly applying them within a continuous approximation of a high dimensional neural field. The model is compatible with preprocessed sensory flows but can also be interfaced with artificial systems. This is particularly important for sensorimotor systems, where decisions and motor actions must be taken and updated in real-time. Preliminary tests are performed on a reactive color tracking application, using spatially distributed color features

Visual attention using spiking neural maps

International audienceVisual attention is a mechanism that biological systems have developed to reduce the large amount of visual information in order to efficiently perform tasks such as learning, recognition, tracking, etc. In this paper we describe a simple spiking neural network model that is able to detect, focus on and track a stimulus even in the presence of noise or distracters. Instead of using a regular rate-coding neuron model based on the continuum neural field theory (CNFT), we propose to use a time-based code by means of a network composed of leaky integrate-and-fire (LIF) neurons. The proposal is experimentally compared against the usual CNFT-based model