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In many cases of inherited retinal degenerations, ganglion cells are spared despite photoreceptor cell death, making it possible to stimulate them to restore visual function. Several studies have shown that it is possible to express an optogenetic protein in ganglion cells and make them light sensitive, a promising strategy to restore vision. However the spatial resolution of optogenetically-reactivated retinas has rarely been measured, especially in the primate. Since the optogenetic protein is also expressed in axons, it is unclear if these neurons will only be sensitive to the stimulation of a small region covering their somas and dendrites, or if they will also respond to any stimulation overlapping with their axon, dramatically impairing spatial resolution. Here we recorded responses of mouse and macaque retinas to random checkerboard patterns following an in vivo optogenetic therapy. We show that optogenetically activated ganglion cells are each sensitive to a small region of visual space. A simple model based on this small receptive field predicted accurately their responses to complex stimuli. From this model, we simulated how the entire population of light sensitive ganglion cells would respond to letters of different sizes. We then estimated the maximal acuity expected by a patient, assuming it could make an optimal use of the information delivered by this reactivated retina. The obtained acuity is above the limit of legal blindness. Our model also makes interesting predictions on how acuity might vary upon changing the therapeutic strategy, assuming an optimal use of the information present in the retinal activity. Optogenetic therapy could thus potentially lead to high resolution vision, under conditions that our model helps to determinine.

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Direction selective (DS) ganglion cells (GC) in the retina maintain their tuning across a broad range of light levels. Yet very different circuits can shape their responses from bright to dim light, and their respective contributions are difficult to tease apart. In particular, the contribution of the rod bipolar cell (RBC) primary pathway, a key player in dim light, is unclear. To understand its contribution to DSGC response, we designed an all-optical approach allowing precise manipulation of single retinal neurons. Our system activates single cells in the bipolar cell (BC) layer by two-photon (2P) temporally focused holographic illumination, while recording the activity in the ganglion cell layer by 2P Ca 2 imaging. By doing so, we demonstrate that RBCs provide an asymmetric input to DSGCs, suggesting they contribute to their direction selectivity. Our results suggest that every circuit providing an input to direction selective cells can generate direction selectivity by itself. This hints at a general principle to achieve robust selectivity in sensory areas.A major goal in neuroscience is to understand the circuits that embody the computations performed by sensory neurons. In particular, a striking feature of sensory processing is the ability of neurons to perform the same computation in different contexts. For example, neurons in the piriform cortex represent odor identity while remaining invariant to its exact concentration (Bolding and Franks, 2018). Similarly, neurons in the visual cortex can keep the same orientation tuning curve over different contrasts (Sclar and Freeman, 1982).In the retina, direction selective (DS) ganglion cells (GC) respond selectively to a motion direction in different contexts such as different backgrounds (Chen et al., 2016), different natural scenes (Im and Fried, 2016), and over a broad range of luminosities (Pearson and Kerschensteiner, 2015; Vaney et al., 2001;Yao et al., 2018). Switching the average luminance from dim to bright light will change the dominant circuits that convey visual information inside the retina, leading to major changes in the responses of ganglion cells (Pearson and Kerschensteiner, 2015;Wässle, 2004). For example, cells will change their ON-OFF polarity (Tikidji-Hamburyan et al., 2015). It is therefore particularly striking that direction selectivity remains constant across the ten billion fold change of luminance separating day from night (Yao et al., 2018), despite the involvement of distinct circuits. A major challenge is to understand how this feature is achieved.During daylight conditions, where rods are saturated and cones transmit light input, the major component responsible for direction selectivity is a functional asymmetry. This results either from an asymmetric inhibition or an asymmetric morphology. For example, in the ON-OFF

Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions, and self-assessment of performance. In this chapter, we review the research work focusing on the acquisition, management, and deployment of contextual information in robotic systems. Our aim is to show that several uses of contextual knowledge (at different representational levels) have been proposed in the literature, regarding many tasks that are typically required for mobile robots. As a result of this survey, we analyze which notions and approaches are applicable to the design and implementation of architectures for information fusion. More specifically, we sketch an architectural framework which enables for an effective engineering of systems that use contextual knowledge, by including the acquisition, representation, and use of contextual information into a framework for information fusion

Classifying neurons in different types is still an open challenge. In the retina, recent works have taken advantage of the ability to record a large number of cells to classify ganglion cells into different types based on functional information. While the first attempts in this direction used the receptive field properties of each cell to classify them, more recent approaches have proposed to cluster ganglion cells directly based on their response to standard stimuli. These two approaches have not been compared directly. Here we recorded the responses of a large number of ganglion cells and compared two methods for classifying them into types, one based on the receptive field properties, and the other one using their responses to standard stimuli. We show that the stimulus-based approach allows separating more types than the receptive field-based method, leading to a better classification. This better granularity is due to the fact that the stimulus-based method takes into account not only the linear part of ganglion cell function, but also non-linearities. A careful characterization of non-linear processing is thus key to allow functional classification of sensory neurons.