Abstract:The processing of motion in visual scenes is important for detecting and tracking moving objects as well as for monitoring self-motion through the induced optic flow. Specialized neural circuits have been identified in the vertebrate retina for detecting motion direction or for distinguishing between object motion and self-motion, although little is known about how information about these distinct features of visual motion is combined. The salamander retina, which is a widely used model system for analyzing re… Show more
“…5a). Direction-selective (DS) ganglion cells were detected by their directional tuning for drifting gratings (see Methods), though the class of DS cells may contain DS cells of two different types 39 .Fig. 5Analyzing subunit overlap for ganglion cell populations.…”
Neurons in sensory systems often pool inputs over arrays of presynaptic cells, giving rise to functional subunits inside a neuron’s receptive field. The organization of these subunits provides a signature of the neuron’s presynaptic functional connectivity and determines how the neuron integrates sensory stimuli. Here we introduce the method of spike-triggered non-negative matrix factorization for detecting the layout of subunits within a neuron’s receptive field. The method only requires the neuron’s spiking responses under finely structured sensory stimulation and is therefore applicable to large populations of simultaneously recorded neurons. Applied to recordings from ganglion cells in the salamander retina, the method retrieves the receptive fields of presynaptic bipolar cells, as verified by simultaneous bipolar and ganglion cell recordings. The identified subunit layouts allow improved predictions of ganglion cell responses to natural stimuli and reveal shared bipolar cell input into distinct types of ganglion cells.
“…5a). Direction-selective (DS) ganglion cells were detected by their directional tuning for drifting gratings (see Methods), though the class of DS cells may contain DS cells of two different types 39 .Fig. 5Analyzing subunit overlap for ganglion cell populations.…”
Neurons in sensory systems often pool inputs over arrays of presynaptic cells, giving rise to functional subunits inside a neuron’s receptive field. The organization of these subunits provides a signature of the neuron’s presynaptic functional connectivity and determines how the neuron integrates sensory stimuli. Here we introduce the method of spike-triggered non-negative matrix factorization for detecting the layout of subunits within a neuron’s receptive field. The method only requires the neuron’s spiking responses under finely structured sensory stimulation and is therefore applicable to large populations of simultaneously recorded neurons. Applied to recordings from ganglion cells in the salamander retina, the method retrieves the receptive fields of presynaptic bipolar cells, as verified by simultaneous bipolar and ganglion cell recordings. The identified subunit layouts allow improved predictions of ganglion cell responses to natural stimuli and reveal shared bipolar cell input into distinct types of ganglion cells.
“…Clustering analyses of functional ganglion cell types in the salamander, however, identified only one type with tiling receptive fields, whereas other types showed considerable overlap (Marre et al, 2012;Segev et al, 2006), leading to the speculation that tiling may not be a general property of ganglion cells in the salamander. Later, however, further examples of tiling for specific types of salamander ganglion cells have surfaced (though not in the context of general classification studies), when additional response characteristics were considered, such as adaptation (Kastner and Baccus, 2011) or direction selectivity (Kühn and Gollisch, 2016). It thus remains to be seen whether enhanced classification methods might provide a refined separation of recorded ganglion cells into perhaps a larger number of types with tiling receptive fields.…”
Salamanders have been habitual residents of research laboratories for more than a century, and their history in science is tightly interwoven with vision research. Nevertheless, many vision scientists – even those working with salamanders – may be unaware of how much our knowledge about vision, and particularly the retina, has been shaped by studying salamanders. In this review, we take a tour through the salamander history in vision science, highlighting the main contributions of salamanders to our understanding of the vertebrate retina. We further point out specificities of the salamander visual system and discuss the perspectives of this animal system for future vision research.
“…Similarly, OS cells were identified as cells with OSI>0.25 (significant 989 at 1% level) that were not DS or IRS. We only included cells with a total mean firing rate >1 Hz 990 during the presentation of the drifting gratings (Kühn and Gollisch, 2016). 991…”
Section: Detection Of Direction-and Orientation-selective Cells 969mentioning
9How neurons encode natural stimuli is a fundamental question for sensory neuroscience. In the early 10 visual system, standard encoding models assume that neurons linearly filter incoming stimuli through 11 their receptive fields, but artificial stimuli, such as reversing gratings, often reveal nonlinear spatial 12 processing. We investigated whether such nonlinear processing is relevant for the encoding of natural 13 images in ganglion cells of the mouse retina. We found that standard linear receptive field models fail 14 to capture the spiking activity for a large proportion of cells. These cells displayed pronounced 15 sensitivity to fine spatial contrast, and local signal rectification was identified as the dominant 16nonlinearity. In addition, we also observed a class of nonlinear ganglion cells with opposite tuning for 17 spatial contrast and a particular sensitivity for spatially homogeneous stimuli. Our work highlights 18 receptive field nonlinearities as a crucial component for understanding early sensory encoding in the 19 context of natural stimuli. 20 2007). Furthermore, linear RF models fail to predict responses of some ganglion cells to finely 40 structured white-noise stimulation (Freeman et al., 2015;Liu et al., 2017). Such failures are linked to 41 the nonlinear integration of excitatory signals in the RF center, which originate from presynaptic 42 bipolar cells (Borghuis et al., 2013;Demb et al., 2001a;Turner and Rieke, 2016). 43This raises the question to what extent nonlinear encoding plays a role in natural vision. In terms of 44 spatial structure, natural stimuli lie in between finely detailed and coarse stimuli, because the light 45 intensities of nearby regions in natural images are correlated (Burton and Moorhead, 1987), yet object 46 boundaries can lead to edges and strong changes of stimulus intensity over short distances (Turiel and 47 salamander retina (Liu et al., 2017;McIntosh et al., 2017) and that their performance is cell-type 55 specific (Turner and Rieke, 2016). 56In this work, we establish a connection of spatial RF nonlinearities to natural stimulus encoding in 57 retinal ganglion cells. We do so in the mouse retina, in which spatial integrationas measured with 58artificial stimuliappears to display a broad scope (Carcieri et al., 2003), with spatially linear 59 (Johnson et al., 2018;Krieger et al., 2017) as well as strongly nonlinear cells (Jacoby and Schwartz, 60 2017;Mani and Schwartz, 2017; Zhang et al., 2012). We first show that linear RF models 61 successfully predict responses to natural images for some ganglion cells and substantially fail for 62 others. We then connect model failure to spatial nonlinearities in the RF center. Finally, we analyze 63 the spatial integration characteristics of specific functional cell types, showing that cells of the same 64 type have similar properties. 65
Results
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Performance of linear-nonlinear models for predicting responses to natural images varies 67 strongly among retinal ganglion cells 68In order to survey whether linear...
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