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Chez la salamandre comme chez les autres vertébrés, les circuits moteurs spinaux sont sous l’influence de systèmes spinaux et supra-spinaux. Parmi ces systèmes, le système sérotoninergique joue un rôle important. Dans la plupart des études, l’application de sérotonine exogène a révélé un effet modulateur sur les circuits locomoteurs spinaux. Cependant, les effets produits par une augmentation du relargage de sérotonine endogène (libérée au niveau des synapses sérotoninergiques) par un inhibiteur sélectif de la recapture de sérotonine (SSRI) sont moins connus. Chez les tétrapodes, les SSRIs déstabilisent l’activité des réseaux contrôlant les membres durant la locomotion fictive, mais leurs effets au niveau des réseaux axiaux (contrôlant le tronc), qui sont coordonnés avec les réseaux des membres pendant la locomotion, sont mal connus. En plus des systèmes neuromodulateurs, les réseaux moteurs spinaux sont sous l’influence de commandes issues des circuits supra-spinaux, parmi lesquels certains intègrent des entrées sensorielles et adaptent la commande motrice générée par la moelle épinière afin de réaliser des mouvements d’orientation pour approcher une proie ou fuir un prédateur. Chez la salamandre, les mouvements d’orientation basés sur l’information visuelle ont été étudiés au niveau comportemental, mais les circuits sous tendant ces mouvements ne sont pas élucidés. Le premier objectif de mes travaux était d’étudier les effets de l’augmentation de la concentration de sérotonine endogène sur l’activité locomotrice fictive générée par le réseau axial spinal chez la salamandre. Mes travaux montrent que l’application d’un SSRI déstabilise l’activité locomotrice des réseaux axiaux enregistrée extracellulairement au niveau des racines ventrales. Mes expériences de neuroanatomie montrent que les fibres sérotoninergiques sont à proximité des neurones glutamatergiques et des motoneurones, fournissant un substrat potentiel aux effets observés en électrophysiologie. Le second, In the salamander as in other vertebrates, spinal motor circuits are under the influence of spinal and supraspinal systems. Among these systems, the serotonergic system plays an important role. In most studies, the application of exogenous serotonin revealed a modulator effect on spinal locomotor circuits. However, less is known about the effects produced by an increase in the release of endogenous serotonin (released at serotonergic synapses) by a selective serotonin reuptake inhibitor (SSRI). In tetrapods, SSRIs destabilize the activity of limb-controlling networks during fictive locomotion, but little is known about their effects on axial networks (controlling the trunk), which are coordinated with limb networks during locomotion. In addition to such neuromodulatory systems, spinal motor circuits are influenced by commands originating from the supraspinal circuits, some of which integrate sensory inputs and adapt the motor command generated by the spinal cord to perform orienting movements, to approach prey or escape from a predator. In salamanders, orienting movements based on visual information have been studied at the behavioral level, but the circuits underlying these movements are not elucidated. The first objective of my work was to study the effects of the increase in the concentration of endogenous serotonin on the fictive locomotor activity generated by the spinal axial network in the salamander. My work shows that the application of an SSRI destabilizes the locomotor activity of the axial networks recorded extracellularly at the level of the ventral roots. My neuroanatomy experiments show that serotonergic fibers are in close proximity with glutamatergic neurons and motoneurons, providing a potential substrate for the effects observed in the electrophysiological experiments. The second objective of my work was to characterize, from retina to spinal motoneurons, the visuomotor pathway in the salamander using neuroanatomy and calcium imaging. My

We present an algorithm that enables a robot to learn the visuomotor transformation from its joint angle space to visual space. The learned transformation can accurately predict location and shape of robot end effector's image projection. This paper extends past work by approximating the end effector by a planar region, rather than a point, in 3-D space, and through its use of spatially and temporally local, rather than global, measures of image consistency. Our robotic experiments demonstrate that the proposed algorithm can learn location and shape of the image region corresponding to the end effector, and how it deforms as the arm moves randomly in front of the camera. Our approach does not require that the end effector be identified with a specific marker. We also demonstrate that the region corresponding to the end effector can adapt to changes in the end effector shape. © 2015 IEEE.

We present an algorithm that enables a robot to learn the visuomotor transformation from its joint angle space to visual space. The learned transformation can accurately predict location and shape of robot end effector's image projection. This paper extends past work by approximating the end effector by a planar region, rather than a point, in 3-D space, and through its use of spatially and temporally local, rather than global, measures of image consistency. Our robotic experiments demonstrate that the proposed algorithm can learn location and shape of the image region corresponding to the end effector, and how it deforms as the arm moves randomly in front of the camera. Our approach does not require that the end effector be identified with a specific marker. We also demonstrate that the region corresponding to the end effector can adapt to changes in the end effector shape.

To explore the possible cortical mechanisms underlying the 3-dimensional (3D) visuomotor transformation for reaching, we trained a 4-layer feed-forward artificial neural network to compute a reach vector (output) from the visual positions of both the hand and target viewed from different eye and head orientations (inputs). The emergent properties of the intermediate layers reflected several known neurophysiological findings, for example, gain field-like modulations and position-dependent shifting of receptive fields (RFs). We performed a reference frame analysis for each individual network unit, simulating standard electrophysiological experiments, that is, RF mapping (unit input), motor field mapping, and microstimulation effects (unit outputs). At the level of individual units (in both intermediate layers), the 3 different electrophysiological approaches identified different reference frames, demonstrating that these techniques reveal different neuronal properties and suggesting that a comparison across these techniques is required to understand the neural code of physiological networks. This analysis showed fixed input-output relationships within each layer and, more importantly, within each unit. These local reference frame transformation modules provide the basic elements for the global transformation; their parallel contributions are combined in a gain field-like fashion at the population level to implement both the linear and nonlinear elements of the 3D visuomotor transformation.