LPMP: A Bio-Inspired Model for Visual Localization in Challenging Environments.

Autonomous vehicles require precise and reliable self-localization to cope with dynamic environments. The field of visual place recognition (VPR) aims to solve this challenge by relying on the visual modality to recognize a place despite changes in the appearance of the perceived visual scene. In this paper, we propose to tackle the VPR problem following a neuro-cybernetic approach.

Time as the fourth dimension in the hippocampus.

Experiences of animal and human beings are structured by the continuity of space and time coupled with the uni-directionality of time. In addition to its pivotal position in spatial processing and navigation, the hippocampal system also plays a central, multiform role in several types of temporal processing. These include timing and sequence learning, at scales ranging from meso-scales of seconds to macro-scales of minutes, hours, days and beyond, encompassing the classical functions of short term memory, working memory, long term memory, and episodic memories (comprised of information about when, what, and where).

A model of path integration and representation of spatial context in the retrosplenial cortex.

Inspired by recent biological experiments, we simulate animals moving in different environments (open space, spiral mazes and on a treadmill) to test the performances of a simple model of the retrosplenial cortex (RSC) acting as a path integration (PI) and as a categorization mechanism. The connection between the hippocampus, RSC and the entorhinal cortex is revealed through a novel perspective. We suppose that the path integration is performed by the information coming from RSC.

Active vision: on the relevance of a bio-inspired approach for object detection.

Starting from biological systems, we review the interest of active perception for object recognition in an autonomous system. Foveated vision and control of the eye saccade introduce strong benefits related to the differentiation of a 'what' pathway recognizing some local parts in the image and a 'where' pathway related to moving the fovea in that part of the image. Experiments on a dataset illustrate the capability of our model to deal with complex visual scenes.

Brain-Inspired Coding of Robot Body Schema Through Visuo-Motor Integration of Touched Events.

Representing objects in space is difficult because sensorimotor events are anchored in different reference frames, which can be either eye-, arm-, or target-centered. In the brain, Gain-Field (GF) neurons in the parietal cortex are involved in computing the necessary spatial transformations for aligning the tactile, visual and proprioceptive signals. In reaching tasks, these GF neurons exploit a mechanism based on multiplicative interaction for binding simultaneously touched events from the hand with visual and proprioception information.

Merging information in the entorhinal cortex: what can we learn from robotics experiments and modeling?

Place recognition is a complex process involving idiothetic and allothetic information. In mammals, evidence suggests that visual information stemming from the temporal and parietal cortical areas ('what' and 'where' information) is merged at the level of the entorhinal cortex (EC) to build a compact code of a place. Local views extracted from specific feature points can provide information important for view cells (in primates) and place cells (in rodents) even when the environment changes dramatically.

Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task.

Emotions play a significant role in internal regulatory processes. In this paper, we advocate four key ideas. First, novelty detection can be grounded in the sensorimotor experience and allow higher order appraisal.

Iterative free-energy optimization for recurrent neural networks (INFERNO).

The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains.

Spatio-Temporal Tolerance of Visuo-Tactile Illusions in Artificial Skin by Recurrent Neural Network with Spike-Timing-Dependent Plasticity.

Perceptual illusions across multiple modalities, such as the rubber-hand illusion, show how dynamic the brain is at adapting its body image and at determining what is part of it (the self) and what is not (others). Several research studies showed that redundancy and contingency among sensory signals are essential for perception of the illusion and that a lag of 200-300 ms is the critical limit of the brain to represent one's own body. In an experimental setup with an artificial skin, we replicate the visuo-tactile illusion within artificial neural networks.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars.

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation.

From grid cells and visual place cells to multimodal place cell: a new robotic architecture.

In the present study, a new architecture for the generation of grid cells (GC) was implemented on a real robot. In order to test this model a simple place cell (PC) model merging visual PC activity and GC was developed. GC were first built from a simple "several to one" projection (similar to a modulo operation) performed on a neural field coding for path integration (PI).

Online learning and control of attraction basins for the development of sensorimotor control strategies.

Imitation and learning from humans require an adequate sensorimotor controller to learn and encode behaviors. We present the Dynamic Muscle Perception-Action(DM-PerAc) model to control a multiple degrees-of-freedom (DOF) robot arm. In the original PerAc model, path-following or place-reaching behaviors correspond to the sensorimotor attractors resulting from the dynamics of learned sensorimotor associations.

Exploiting the gain-modulation mechanism in parieto-motor neurons: application to visuomotor transformations and embodied simulation.

The so-called self-other correspondence problem in imitation demands to find the transformation that maps the motor dynamics of one partner to our own. This requires a general purpose sensorimotor mechanism that transforms an external fixation-point (partner's shoulder) reference frame to one's own body-centered reference frame. We propose that the mechanism of gain-modulation observed in parietal neurons may generally serve these types of transformations by binding the sensory signals across the modalities with radial basis functions (tensor products) on the one hand and by permitting the learning of contextual reference frames on the other hand.

Neural model for learning-to-learn of novel task sets in the motor domain.

During development, infants learn to differentiate their motor behaviors relative to various contexts by exploring and identifying the correct structures of causes and effects that they can perform; these structures of actions are called task sets or internal models. The ability to detect the structure of new actions, to learn them and to select on the fly the proper one given the current task set is one great leap in infants cognition. This behavior is an important component of the child's ability of learning-to-learn, a mechanism akin to the one of intrinsic motivation that is argued to drive cognitive development.

From self-assessment to frustration, a small step toward autonomy in robotic navigation.

Autonomy and self-improvement capabilities are still challenging in the fields of robotics and machine learning. Allowing a robot to autonomously navigate in wide and unknown environments not only requires a repertoire of robust strategies to cope with miscellaneous situations, but also needs mechanisms of self-assessment for guiding learning and for monitoring strategies. Monitoring strategies requires feedbacks on the behavior's quality, from a given fitness system in order to take correct decisions.

Modeling the minimal newborn's intersubjective mind: the visuotopic-somatotopic alignment hypothesis in the superior colliculus.

The question whether newborns possess inborn social skills is a long debate in developmental psychology. Fetal behavioral and anatomical observations show evidences for the control of eye movements and facial behaviors during the third trimester of pregnancy whereas specific sub-cortical areas, like the superior colliculus (SC) and the striatum appear to be functionally mature to support these behaviors. These observations suggest that the newborn is potentially mature for developing minimal social skills.

Neurobiologically inspired mobile robot navigation and planning.

After a short review of biologically inspired navigation architectures, mainly relying on modeling the hippocampal anatomy, or at least some of its functions, we present a navigation and planning model for mobile robots. This architecture is based on a model of the hippocampal and prefrontal interactions. In particular, the system relies on the definition of a new cell type "transition cells" that encompasses traditional "place cells".

From reflex to planning: Multimodal versatile complex systems in biorobotics.

As models of living beings acting in a real world biorobots undergo an accelerated "philogenic" complexification. The first efficient robots performed simple animal behaviours (e.g.

A test of the time estimation hypothesis of place cell goal-related activity.

Place cells are hippocampal pyramidal neurons that discharge strongly in relation to the rat's location in the environment. We recently reported that many place cells recorded from rats performing place or cue navigation tasks also discharged when they were at the goal location rather than in the primary firing field. Furthermore, subtle differences in discharge timing were found in the two navigation tasks, with activity occurring later in the place task compared to the cue task.