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.

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.

Bladder cancer (BC) ranks as the tenth most prevalent cancer in the world (IARC, WHO), with a steady rise in its incidence and prevalence, and is accompanied by a high morbidity and mortality. Absence of reliable screening methods, invasiveness of diagnostic modalities, and high recurrence rates, makes BC one of the most challenging and expensive cancers to diagnose and treat. Cystoscopic examination is considered as the gold standard for BC assessment, but although the detection rate is high, the technique/equipment is expensive, invasive, not available worldwide, and most importantly uncomfortable and associated with risk of complications. Therefore, there is a critical need to develop a non-invasive, low cost, and sensitive method for the early detection and monitoring of BC. Each of our groups have developed promising, simple and low-cost urine-based diagnostic tests for the non-invasive early detection of BC through preclinical exploratory studies. In this project, we aim to investigate the robustness of these biomarkers in our reciprocal populations through a large international multicentric study (Canada, France, Germany), and determine the diagnostic accuracy of each test and combined ones. This would demonstrate the applicability of each of our tests to be used as universal non-invasive biomarkers for early detection and surveillance of BC in different populations, and determine whether a multi-analyte urine biomarker through a combination of these tests could increase the performance for the detection of primary or recurrent BC. For this purpose, we bring together an international group of experts who will collaborate to comprehensively assess the robustness of these biomarkers and their applicability worldwide in order to provide sufficient evidence towards the clinical implementation of these tests for the non-invasive detection of BC.