The goals of this project are to decipher how the interplay between central and peripheral mechanisms controls locomotion in four legged animals (tetrapods) and to the delineate the reorganization of motor circuits linked to functional regeneration after spinal cord lesion. We will take advantage of the evolutionarily conserved traits of neural structures in vertebrates to address these two fundamental questions by using salamanders as model organisms. Salamanders are best suited to these aims for two main reasons: First, because they have an anatomically simplified nervous system, which yet possesses the main features of all tetrapods; second, because they have unique regeneration abilities among vertebrates and can functionally repair their spinal cord after full transection. Taking an interdisciplinary approach, we will investigate the dynamic interactions between the nervous system, the body, and its environment before and after spinal cord lesion. We will combine numerical models of locomotor neural circuits, robotics, and advanced functional analyses in genetically modified salamanders in a way that will allow us to test biological data in neuromechanical models (simulations and robots) and, conversely, to validate model-based predictions in animals. Through the concerted and tightly collaborative activities in our laboratories, implementing state of the art assays ranging from the molecular to the organism level, we expect to create a blueprint of tetrapod locomotion control: how appropriate movements are generated in response to various environmental or intrinsic stimuli, and how such function can be recovered after injury. The synergy between our groups of complementary expertise will boost scientific research at multiple levels, not only in the field of neuroscience but also in regeneration research, robotics, and numerical modeling.

Edge computing (EC) and the development of portable devices such as cell phones, autonomous robot or health tracking systems represent one of the big challenges for artificial intelligence (AI) deployment. These hardware systems present very tight constraints in terms of energy consumption and computing power that today’s AI strategies cannot cope with. While high power GPU are well adapted to deep neural network implementations that should strongly benefit to AI development, ultra-low power and robust computing with limited resources need to be proposed for EC applications. To this end, we propose to explore the hardware implementation of small-scale neural networks with limited complexity that could satisfy EC requirements. Notably, spiking neural networks present a real opportunity to this end since, they can combine low power operation and non-trivial computing functions as biological neural networks do. In fact, spiking neural networks (SNNs) of moderate size can reproduce important aspects that are not considered in state-of-the-art machine learning approaches: i) non-linear dynamical regime (i.e. synchronized, critic, driven by attractor dynamics, sequences of spikes) that might explain basic mechanisms in perception and ii) the fast computing that occurs in the brain even if neurons are slow. The UNICO project proposes to address the material implementation of such SNNs by integrating in a dedicated hardware, the key ingredients at work in such SNNs. In fact, we can anticipate that the physical implementation of such highly parallel systems will encounter strong limitations with conventional technologies. A real breakthrough for Information and Communication Technologies would be to capitalize on emerging nanotechnologies to implement efficiently these SNNs on an ultra-low power hardware. Here, state of the art analog resistive memory technologies, or memristive devices, will be developed and integrated in the Back End Of Line of CMOS for implementing analog SNNs. By gathering competences from material sciences, device engineering, neuromorphic engineering and machine learning, we will explore how such SNNs can be deployed on various computing tasks of interest for EC applications. The expected innovations at both the hardware and computing levels could benefit to a wide range of AI applications in the future.

Clostridioides difficile (CD) is the major cause of nosocomial infections associated with antibiotic therapy. The disruption of the colonic microbiota by antibiotics promotes colonization of the gut by CD. Many aspects of CD pathogenesis remain poorly understood. During the infection CD survives in phage-rich gut communities by relying on defense systems like CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) for adaptive prokaryotic immunity. Toxin-Antitoxin (TA) and abortive infection (Abi) systems also contribute to prophage maintenance, prevention of phage infection, and stress response. RNAs have emerged as key components of these defense systems. CRISPR RNAs in complex with Cas proteins interfere with phage infection by targeting foreign nucleic acid for destruction. In type I TA (TITA), the antitoxin is a small antisense RNA that neutralizes toxin mRNA by inhibiting its translation and promoting its degradation and in type III TA, antitoxin RNA form a complex with the toxin leading to protein sequestration. Prokaryotic defense systems cluster together forming defense islands and could be functionally linked. This project is built upon our preliminary data on CRISPR-Cas, TITA and Abi-like systems in CD frequently associated with prophages. Our goal is to decipher the interplay between these systems and their contribution to CD adaptation and interactions with phages. We will use an integrative strategy combining genomics, molecular biology, genetics, bioinformatics and animal models. We expect to identify the roles of RNA-based defense systems with associated protein machineries in CD contributing to its fitness inside the host and enlarge the study to cover evolutionary aspects of these mechanisms. These data will shed new light on the coordination of bacterial defense strategies and genome evolution, pertinent for the development of phage and genome editing, epidemiological monitoring and new therapeutic strategies.

Edge computing (EC) and the development of portable devices such as cell phones, autonomous robot or health tracking systems represent one of the big challenges for artificial intelligence (AI) deployment. These hardware systems present very tight constraints in terms of energy consumption and computing power that today’s AI strategies cannot cope with. While high power GPU are well adapted to deep neural network implementations that should strongly benefit to AI development, ultra-low power and robust computing with limited resources need to be proposed for EC applications. To this end, we propose to explore the hardware implementation of small-scale neural networks with limited complexity that could satisfy EC requirements. Notably, spiking neural networks present a real opportunity to this end since, they can combine low power operation and non-trivial computing functions as biological neural networks do. In fact, spiking neural networks (SNNs) of moderate size can reproduce important aspects that are not considered in state-of-the-art machine learning approaches: i) non-linear dynamical regime (i.e. synchronized, critic, driven by attractor dynamics, sequences of spikes) that might explain basic mechanisms in perception and ii) the fast computing that occurs in the brain even if neurons are slow. The UNICO project proposes to address the material implementation of such SNNs by integrating in a dedicated hardware, the key ingredients at work in such SNNs. In fact, we can anticipate that the physical implementation of such highly parallel systems will encounter strong limitations with conventional technologies. A real breakthrough for Information and Communication Technologies would be to capitalize on emerging nanotechnologies to implement efficiently these SNNs on an ultra-low power hardware. Here, state of the art analog resistive memory technologies, or memristive devices, will be developed and integrated in the Back End Of Line of CMOS for implementing analog SNNs. By gathering competences from material sciences, device engineering, neuromorphic engineering and machine learning, we will explore how such SNNs can be deployed on various computing tasks of interest for EC applications. The expected innovations at both the hardware and computing levels could benefit to a wide range of AI applications in the future.