Laurent Rodriguez

• PhD
• Professor (Associate) at Université Côte d'Azur

Recent progress in the fields of AI and cognitive sciences opens up new challenges that were previously inaccessible to study. One of such modern tasks is recovering lost data of one modality by using the data from another one. A similar effect (called the McGurk Effect) has been found in the functioning of the human brain. Observing this effect, o...

Recent progress in the fields of AI and cognitive sciences opens up new challenges and problems that were previously inaccessible to study. One of such modern tasks is recovering lost data of one modality by using the data from another one. A similar effect (called the McGurk Effect) has been found in the functioning of the human brain. Observing t...

The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is inspired by the process of self-organization of cortical areas in the human brain from both afferent and lateral/internal connections. In this work, we develop a brain-i...

The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is inspired by the process of self-organization of cortical areas in the human brain from both afferent and lateral/internal connections. In this work, we develop an origin...

The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with complex datasets. We propose in this work to improve the SOM performance by using extracted features instead of raw data. We condu...

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association...

The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with complex datasets. We propose in this work to improve the SOM performance by using extracted features instead of raw data. We condu...

Cortical plasticity is one of the main features that enable our capability to learn and adapt in our environment. Indeed, the cerebral cortex has the ability to self-organize itself through two distinct forms of plasticity: the structural plasticity that creates (sprouting) or cuts (pruning) synaptic connections between neurons, and the synaptic pl...

The written and spoken digits database is not a new database but a constructed database from existing ones, in order to provide a ready-to-use database for multimodal fusion. The written digits database is the original MNIST handwritten digits database with no additional processing. It consists of 70000 images (60000 for training and 10000 for tes...

Machine learning has recently taken the leading role in machine vision through deep learning algorithms. It has brought the best results in object detection, recognition and tracking. Nevertheless, these systems are computationally expensive since they need to process the whole images from the camera for producing such results. Consequently, they r...

Self-organization is a powerful bio-inspired feature that has been poorly investigated in the context of hardware computing architectures. The neural foundations of brain self-organization enable structural plasticity, continuous learning, and tolerance to lesions but at the cost of massive lateral synaptic connections. A cellular formulation of th...

Unsupervised artificial neural networks are now considered as a likely alternative to classical computing models in many application domains. For example, recent neural models defined by neuro-scientists exhibit interesting properties for an execution in embedded and autonomous systems: distributed computing, unsupervised learning, self-adaptation,...

Neurobiological systems have often been a source of inspiration for computational science and engineering, but in the past their impact has also been limited by the understanding of biological models. Today, new technologies lead to an equilibrium situation where powerful and complex computers bring new biological knowledge of the brain behavior. A...

The advent of massively parallel many-core architectures on a chip can be considered as a good opportunity to rethink the classical computation model used for several decades and that now shows some limitations to follow both the potential and the usage of new technologies. In this paper, the way explored to study new solutions is directly inspired...

This paper describes a bio-inspired architectural approach to design highly adaptive and reconfigurable systems in the context of mobile robotics. The aim is to design the hardware architecture of an intelligent controller for a robot that exhibits several behaviors such as landscape learning, obstacle avoidance, path planning, sensory-motor contro...

This paper describes a bio-inspired architectural approach to design highly adaptive systems in the context of mobile robotics. The concerned robots evolve in an indoor unknown environment and then exhibit several behaviours such as landscape learning, obstacle avoidance, path planning, sensori-motor control. We aim at designing the intelligent emb...