General features of the retinal connectome determine the computation of motion anticipation

Motion anticipation allows the visual system to compensate for the slow speed of phototransduction so that a moving object can be accurately located. This correction is already present in the signal that ganglion cells send from the retina but the biophysical mechanisms underlying this computation are not known. Here we demonstrate that motion anticipation is computed autonomously within the dendritic tree of each ganglion cell and relies on feedforward inhibition. The passive and non-linear interaction of excitatory and inhibitory synapses enables the somatic voltage to encode the actual position of a moving object instead of its delayed representation. General rather than specific features of the retinal connectome govern this computation: an excess of inhibitory inputs over excitatory, with both being randomly distributed, allows tracking of all directions of motion, while the average distance between inputs determines the object velocities that can be compensated for

The iso-response method

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments

Information recovery from rank-order encoded images

The time to detection of a visual stimulus by the primate eye is recorded at 100 – 150ms. This near instantaneous recognition is in spite of the considerable processing required by the several stages of the visual pathway to recognise and react to a visual scene. How this is achieved is still a matter of speculation. Rank-order codes have been proposed as a means of encoding by the primate eye in the rapid transmission of the initial burst of information from the sensory neurons to the brain. We study the efficiency of rank-order codes in encoding perceptually-important information in an image. VanRullen and Thorpe built a model of the ganglion cell layers of the retina to simulate and study the viability of rank-order as a means of encoding by retinal neurons. We validate their model and quantify the information retrieved from rank-order encoded images in terms of the visually-important information recovered. Towards this goal, we apply the ‘perceptual information preservation algorithm’, proposed by Petrovic and Xydeas after slight modification. We observe a low information recovery due to losses suffered during the rank-order encoding and decoding processes. We propose to minimise these losses to recover maximum information in minimum time from rank-order encoded images. We first maximise information recovery by using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder decoding. We then apply the biological principle of lateral inhibition to minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap Correction algorithm. To test the perfomance of rank-order codes in a biologically realistic model, we design and simulate a model of the foveal-pit ganglion cells of the retina keeping close to biological parameters. We use this as a rank-order encoder and analyse its performance relative to VanRullen and Thorpe’s retinal model

Circuit Development in the Dorsal Lateral Geniculate Nucleus (dLGN) of the Mouse.

The visual system is one of the most widely used and best understood sensory systems and the dorsal lateral geniculate nucleus (dLGN) of the mouse has emerged as a model for investigating the cellular and molecular mechanisms underlying the development and activity-dependent refinement of sensory connections. Thalamic organization is highly conserved throughout species and the dLGN of the mouse possesses many features common to higher mammals, such as carnivores and primates. Two general classes of neuron are present within the dLGN, thalamocortical relay cells and interneurons, both of which receive direct retinal input. Axons of relay cells exit dLGN and convey visual information to layer IV of cortex, whereas interneurons are involved in local circuitry. In addition, dLGN receives rich nonretinal input from numerous areas of the brain. Studies thus far have focused on the retinogeniculate pathway and the development of connections between retinal ganglion cells (RGCs) and relay cells has been well characterized. However, there are still a number of unanswered questions about circuit development in dLGN. Here we examined two aspects that are not well understood, the pattern of retinal convergence onto interneurons and the structural and functional innervation of nonretinal projections. To address the first issue we conducted in vitro whole-cell recordings from acute thalamic slices of GAD67-GFP mice, a transgenic strain in which dLGN interneurons express GFP. We also did 3-D reconstructions of biocytin-labeled interneurons using multi-photon laser scanning microscopy in conjunction with anterograde labeling of retinogeniculate projections to examine the distribution of retinal contacts. To begin to examine the development of nonretinal connections in dLGN we made use of a transgenic mouse (golli-τ-GFP) to visualize corticogeniculate projections, one of the largest sources of nonretinal input to dLGN. Using this mouse we studied the timing and patterning of corticogeniculate innervation in relation to the development of the retinogeniculate pathway. We also used binocular enucleation and genetic deafferentation to test whether the retina plays a role in regulating nonretinal innervation. We found that there is a coordination of retinal and nonretinal innervation in dLGN. Projections from the retina were the first to innervate and they entered dLGN at perinatal ages. They also made functional connections with both relay cells and interneurons at early postnatal ages. Interestingly, relay cells underwent a period of retinogeniculate refinement, whereas the degree of retinal convergence onto interneurons was maintained. This possibly reflects the different roles that these two cell types have in dLGN. Both structural and functional corticogeniculate innervation was delayed in comparison and occurred postnatally, however in the absence of retinal input the timing of corticogeniculate innervation was accelerated. RGCs transmit the visual information encoded in the retina to dLGN so it may be necessary for these connections to be formed before those from nonretinal projections, which serve to modulate that signal on its way to cortex. Thus precise timing of retinal and nonretinal innervation may be important for the appropriate formation of connections in the visual system and the retina seems to be playing an important role in regulating this timing

Retinal ganglion cells and the magnocellular, parvocellular, and koniocellular subcortical visual pathways from the eye to the brain

In primates including humans, most retinal ganglion cells send signals to the lateral geniculate nucleus (LGN) of the thalamus. The anatomical and functional properties of the two major pathways through the LGN, the parvocellular (P) and magnocellular (M) pathways, are now well understood. Neurones in these pathways appear to convey a filtered version of the retinal image to primary visual cortex for further analysis. The properties of the P-pathway suggest it is important for high spatial acuity and red-green color vision, while those of the M-pathway suggest it is important for achromatic visual sensitivity and motion vision. Recent work has sharpened our understanding of how these properties are built in the retina, and described subtle but important nonlinearities that shape the signals that cortex receives. In addition to the P- and M-pathways, other retinal ganglion cells also project to the LGN. These ganglion cells are larger than those in the P- and M-pathways, have different retinal connectivity, and project to distinct regions of the LGN, together forming heterogenous koniocellular (K) pathways. Recent work has started to reveal the properties of these K-pathways, in the retina and in the LGN. The functional properties of K-pathways are more complex than those in the P- and M-pathways, and the K-pathways are likely to have a distinct contribution to vision. They provide a complementary pathway to the primary visual cortex, but can also send signals directly to extrastriate visual cortex. At the level of the LGN, many neurones in the K-pathways seem to integrate retinal with non-retinal inputs, and some may provide an early site of binocular convergence

Cell-Type Specific Roles for PTEN in Establishing a Functional Retinal Architecture

BACKGROUND: The retina has a unique three-dimensional architecture, the precise organization of which allows for complete sampling of the visual field. Along the radial or apicobasal axis, retinal neurons and their dendritic and axonal arbors are segregated into layers, while perpendicular to this axis, in the tangential plane, four of the six neuronal types form patterned cellular arrays, or mosaics. Currently, the molecular cues that control retinal cell positioning are not well-understood, especially those that operate in the tangential plane. Here we investigated the role of the PTEN phosphatase in establishing a functional retinal architecture. METHODOLOGY/PRINCIPAL FINDINGS: In the developing retina, PTEN was localized preferentially to ganglion, amacrine and horizontal cells, whose somata are distributed in mosaic patterns in the tangential plane. Generation of a retina-specific Pten knock-out resulted in retinal ganglion, amacrine and horizontal cell hypertrophy, and expansion of the inner plexiform layer. The spacing of Pten mutant mosaic populations was also aberrant, as were the arborization and fasciculation patterns of their processes, displaying cell type-specific defects in the radial and tangential dimensions. Irregular oscillatory potentials were also observed in Pten mutant electroretinograms, indicative of asynchronous amacrine cell firing. Furthermore, while Pten mutant RGC axons targeted appropriate brain regions, optokinetic spatial acuity was reduced in Pten mutant animals. Finally, while some features of the Pten mutant retina appeared similar to those reported in Dscam-mutant mice, PTEN expression and activity were normal in the absence of Dscam. CONCLUSIONS/SIGNIFICANCE: We conclude that Pten regulates somal positioning and neurite arborization patterns of a subset of retinal cells that form mosaics, likely functioning independently of Dscam, at least during the embryonic period. Our findings thus reveal an unexpected level of cellular specificity for the multi-purpose phosphatase, and identify Pten as an integral component of a novel cell positioning pathway in the retina