Sens (physiologie)

Résumé

vignette|Les cinq sens d'après Aristote, représentés par leurs organes sensoriels inhérents : oreilles, yeux, langue, nez, peau. Du point de vue de la physiologie, les sens sont les systèmes sensoriels de la perception. Leur fonctionnement, leur classification, et la théorie épistémologique qui soutient leur étude sont abordés par plusieurs disciplines, principalement les neurosciences, mais aussi la psychologie cognitive (ou science cognitive), et toutes les philosophies et études sociales telle l’anthropologie ayant trait à la perception. Longtemps, en Occident, à la suite d'Aristote, on a considéré que l'être humain possède cinq sens. Mais ce chiffre est maintenant souvent porté à sept :

système tactile, relatif aux informations de pression, de toucher, de température, de vibration ou de douleur ;

système visuel, relatif à la couleur, la forme, la distance ou l’intensité lumineuse ;

système auditif, qui localise les sons, différencie leur intensité, fréquence, et renseigne s

Source officielle

https://fr.wikipedia.org/wiki/Sens_(physiologie)

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Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same functional unit, the neuron, and develop through the same learning mechanism, synaptic plasticity. It motivates the conjecture of a unifying theory to explain cortical representational learning across sensory modalities. In this thesis we present theories and computational models of learning and optimization in neural networks, postulating functional properties of synaptic plasticity that support the apparent universal learning capacity of cortical networks. In the past decades, a variety of theories and models have been proposed to describe receptive field formation in sensory areas. They include normative models such as sparse coding, and bottom-up models such as spike-timing dependent plasticity. We bring together candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. A surprising result is that our findings are independent of specific details, and allow for robust predictions of the learned receptive fields. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The Hebbian learning theory substantiates that synaptic plasticity can be interpreted as an optimization procedure, implementing stochastic gradient descent. In stochastic gradient descent inputs arrive sequentially, as in sensory streams. However, individual data samples have very little information about the correct learning signal, and it becomes a fundamental problem to know how many samples are required for reliable synaptic changes. Through estimation theory, we develop a novel adaptive learning rate model, that adapts the magnitude of synaptic changes based on the statistics of the learning signal, enabling an optimal use of data samples. Our model has a simple implementation and demonstrates improved learning speed, making this a promising candidate for large artificial neural network applications. The model also makes predictions on how cortical plasticity may modulate synaptic plasticity for optimal learning. The optimal sampling size for reliable learning allows us to estimate optimal learning times for a given model. We apply this theory to derive analytical bounds on times for the optimization of synaptic connections. First, we show this optimization problem to have exponentially many saddle-nodes, which lead to small gradients and slow learning. Second, we show that the number of input synapses to a neuron modulates the magnitude of the initial gradient, determining the duration of learning. Our final result reveals that the learning duration increases supra-linearly with the number of synapses, suggesting an effective limit on synaptic connections and receptive field sizes in developing neural networks.

EPFL2016

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Whiskers (also known as vibrissae) are sensory organs that are thought to have first appeared in Therapsida, a group of synapsids that includes mammals and evolutionary ancestors like the Thrinaxodon(1). They are highly conserved among mammals to the notable exception of the higher primates and are important for behavior, especially for nocturnal animals and the ones living in burrows(2). Furthermore, the mechanoreceptors of the vibrissae signal to the primary somatosensory cortex, where a discrete and well-defined structure in layer 4 represents each whisker(3). This structure, known as barrel cortex, occupies a large portion of the rodent brain. Strikingly, Prdm1 conditional KO (cKO) mice are one of the rare transgenic strains that do not develop whiskers while forming a pelage; on the other hand, the other head structures expressing Prdm1 (including pelage hair follicles and teeth) grow normally(4). In the developing whisker pad, Prdm1 expression is confined to the mesenchyme underlying the embryonic epidermis that will later form the whisker epidermal placode; it lasts until dermal papilla formation, when a âswitchâ occurs to the precursors of the inner root sheath. The Prdm1 ablation study we conducted revealed the absence of the patterned up-regulation of Bmp2 (marker of placode formation), Shh (a protein secreted by the epidermal placode responsible for the first epidermal signal) and Bmp4 (expressed in the pre-follicle mesenchyme). On the other hand, Lef1 is expressed uniformly in the mesenchyme of the whisker pad, thus pointing out the integrity of the Î²Catenin-mediated first mesenchymal signal. Those results suggest the role of Prdm1 as a master gene in whisker development. Prdm1 expressing cells are mainly non-proliferative; however, a small subpopulation at the periphery is cycling. Being Sox2 coexpressed with Prdm1 in the whisker mesenchyme, we performed lineage tracing with Sox2CreERT2 and ROSAYFP transgenic strains. Those experiments revealed that Sox2/Prdm1 expressing cells contribute to the formation of the dermal papilla, dermal sheath and putative pericytes of the whisker follicle and thus constitute a population of embryonic progenitors crucial for whisker formation. We subsequently investigated the development of the barrel cortex in our cKO mice. Intriguingly, a disorganized pattern was observed in cKO mice, suggesting that the loss of expression of a single gene, and consequently the disappearance of vibrissae, drastically impacts the development of the whole somatosensory system. Finally, we hypothesized that the loss of Prdm1 expression might explain the disappearance of whisker follicles in the human during evolution. Prdm1 being no longer expressed in the human lineage during hair follicle development(5), we reasoned that the loss of regulatory elements of its upstream genes might contribute to this phenomenon; we demonstrated that Lef1 acts upstream of Prdm1 during whisker development and are currently testing the role of its potential enhancers (Leaf) during this process. Bibliography: 1: (http://en.wikipedia.org/wiki/therapsid) 2: Petersen, C. C. H. The Functional Organization of the Barrel Cortex. Neuron 56, 339â355 (2007) 3: Woolsey, T. A. & Van der Loos, H. The structural organization of layer IV in the somatosensory region (SI) of mouse cerebral cortex. The description of a cortical field composed of discrete cytoarchitectonic units. Brain Res. 17, 205â242 (1970) 4: Robertson, E. J. et al. Blimp1 regulates development of the posterior forelimb, caudal pharyngeal arches, heart and sensory vibrissae in mice. Development 134, 4335â4345 (2007). 5: Sellheyer, K. & Krahl, D. Blimp-1: a marker of terminal differentiation but not of sebocytic progenitor cells. J. Cutan. Pathol. 37, 362â370 (2010).

EPFL2016

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The operation of telerobotic systems can be a challenging task, requiring intuitive and efficient interfaces to enable inexperienced users to attain a high level of proficiency. Body-Machine Interfaces (BoMI) represent a promising alternative to standard control devices, such as joysticks, because they leverage intuitive body motion and gestures. It has been shown that the use of Virtual Reality (VR) and first-person view perspectives can increase the user's sense of presence in avatars. However, it is unclear if these beneficial effects occur also in the teleoperation of non-anthropomorphic robots that display motion patterns different from those of humans. Here we describe experimental results on teleoperation of a non-anthropomorphic drone showing that VR correlates with a higher sense of spatial presence, whereas viewpoints moving coherently with the robot are associated with a higher sense of embodiment. Furthermore, the experimental results show that spontaneous body motion patterns are affected by VR and viewpoint conditions in terms of variability, amplitude, and robot correlates, suggesting that the design of BoMIs for drone teleoperation must take into account the use of Virtual Reality and the choice of the viewpoint.

2021

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EPFL2016