Biology subjects

Zapp, S. J.

Publications and source records attributed to Zapp, S. J..

5 recordsLinked to original sources

bioRxiv2024

Accelerated spike-triggered non-negative matrix factorization reveals coordinated ganglion cell subunit mosaics in the primate retina

A standard circuit motif in sensory systems is the pooling of sensory information from an upstream neuronal layer. A downstream neuron thereby collects signals across different locations in stimulus space, which together compose the neurons receptive field. In addition, nonlinear transformations in the signal transfer between the layers give rise to functional subunits inside the receptive field. For ganglion cells in the vertebrate retina, for example, receptive field subunits are thought to correspond to presynaptic bipolar cells. Identifying the number and locations of subunits from the stimulus-response relationship of a recorded ganglion cell has been an ongoing challenge in order to characterize the retinas functional circuitry and to build computational models that capture nonlinear signal pooling. Here we present a novel version of spike-triggered non-negative matrix factorization (STNMF), which can extract localized subunits in ganglion-cell receptive fields from recorded spiking responses under spatiotemporal white-noise stimulation. The method provides a more than 100-fold speed increase compared to a previous implementation, which can be harnessed for systematic screening of hyperparameters, such as sparsity regularization. We demonstrate the power and flexibility of this approach by analyzing populations of ganglion cells from salamander and primate retina. We find that subunits of midget as well as parasol ganglion cells in the marmoset retina form separate mosaics that tile visual space. Moreover, subunit mosaics show alignment with each other for ON and OFF midget as well as for ON and OFF parasol cells, indicating a spatial coordination of ON and OFF signals at the bipolar-cell level. Thus, STNMF can reveal organizational principles of signal transmission between successive neural layers, which are not easily accessible by other means.

neuroscience↗

bioRxiv2024

Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies

Retinal ganglion cells, the output neurons of the vertebrate retina, often display nonlinear summation of visual signals over their receptive fields. This creates sensitivity to spatial contrast, letting the cells respond to spatially structured visual stimuli even when no net change in overall illumination of the receptive field occurs. Yet, computational models of ganglion cell responses are often based on linear receptive fields, and typical nonlinear extensions, which separate receptive fields into nonlinearly combined subunits, are often cumbersome to fit to experimental data. Previous work has suggested to model spatial-contrast sensitivity in responses to flashed images by combining signals from the mean and variance of light intensity inside the receptive field. Here, we extend and adjust this spatial contrast model for application to spatiotemporal stimulation and explore its performance on spiking responses that we recorded from ganglion cells of marmosets under artificial and naturalistic movies. We show how the model can be fitted to experimental data and that it outperforms common models with linear spatial integration to different degrees for different types of ganglion cells. Finally, we use the model framework to infer the cells spatial scale of nonlinear spatial integration. Our work shows that the spatial contrast model can capture aspects of nonlinear spatial integration in the primate retina with only few free parameters. The model can be used to assess the cells functional properties under natural stimulation and provides a simple-to-obtain benchmark for comparison with more detailed nonlinear encoding models. Author SummaryOur visual experience depends on the retinas remarkable ability to detect light patterns and contrast in the world around us. Retinal ganglion cells, the output neurons of the retina, modulate their activity based on signals within small, specific regions of the visual scene, called their receptive fields. But many cells do not only encode overall brightness, summed linearly across the receptive field, but are also sensitive to local spatial contrast, that is, variations in brightness within the receptive field. Computational models that account for this nonlinear spatial integration exist, but require large amounts of data and are challenging to fit. We therefore developed the spatial contrast model, which takes a simple measure of light-intensity variations as an input, and tested it on measured responses of primate retinal ganglion cells to both artificial and naturalistic movies. The model substantially outperformed standard models with linear receptive fields, despite having only one additional tunable parameter. Furthermore, we used the model to investigate the spatial scale at which the cells integrate spatial contrast and found striking consistency across cell types. The spatial contrast model thus offers a practical tool for capturing retinal stimulus encoding and a simple-to-obtain benchmark for modeling nonlinear spatial integration.

neuroscience↗

bioRxiv2023

Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina

Spatially nonlinear stimulus integration by retinal ganglion cells lies at the heart of various computations performed by the retina. It arises from the nonlinear transmission of signals that ganglion cells receive from bipolar cells, which thereby constitute functional subunits within a ganglion cells receptive field. Inferring these subunits from recorded ganglion cell activity promises a new avenue for studying the functional architecture of the retina. This calls for efficient methods, which leave sufficient experimental time to leverage the acquired knowledge. Here, we combine concepts from super-resolution microscopy and computed tomography and introduce super-resolved tomographic reconstruction (STR) as a technique to efficiently stimulate and locate receptive field subunits. Simulations demonstrate that this approach can reliably identify subunits across a wide range of model variations, and application in recordings of primate parasol ganglion cells validates the experimental feasibility. STR can potentially reveal comprehensive subunit layouts within less than an hour of recording time, making it ideal for online analysis and closed-loop investigations of receptive field substructure in retina recordings.

neuroscience↗

bioRxiv2023

Natural stimuli drive concerted nonlinear responses in populations of retinal ganglion cells

The role of the vertebrate retina in early vision is generally described by the efficient coding theory, which predicts that the retina discards spatiotemporal correlations in natural scenes. It is unclear, however, whether the predicted decorrelation in the activity of ganglion cells, the retinas output neurons, holds under gaze shifts, which dominate the natural visual input. We here show that species-specific gaze patterns in natural stimuli can drive strong and correlated spiking responses both within and across distinct types of ganglion cells in marmoset as well as mouse retina. These concerted responses violate efficient coding and signal fixation periods with locally high spatial contrast. Finally, novel model-based analyses of ganglion cell responses to natural stimuli reveal that the observed response correlations follow from nonlinear pooling of ganglion cell inputs. Our results reveal how concerted population activity can surpass efficient coding to detect gaze-related stimulus features.

neuroscience↗

bioRxiv2022

Diversity of Ganglion Cell Responses to Saccade-like Image Shifts in the Primate Retina

Saccades are a fundamental part of natural vision. They interrupt fixations of the visual gaze and rapidly shift the image that falls onto the retina. These stimulus dynamics can cause activation or suppression of different retinal ganglion cells, but how they affect the encoding of visual information in different types of ganglion cells is largely unknown. Here, we recorded spiking responses to saccade-like shifts of luminance gratings from ganglion cells in isolated marmoset retinas and investigated how the activity depended on the combination of pre- and post-saccadic images. All identified cell types, On and Off parasol and midget cells as well as a type of Large Off cells, displayed distinct response patterns, including particular sensitivity to either the pre- or the post-saccadic image or combinations thereof. In addition, Off parasol and Large Off cells, but not On cells, showed pronounced sensitivity to whether the image changed across the transition. Stimulus sensitivity of On cells could be explained based on their responses to step changes in light intensity, whereas Off cells, in particular, parasol and the Large Off cells, seem to be affected by additional interactions that are not triggered during simple light-intensity flashes. Together, our data show that ganglion cells in the primate retina are sensitive to different combinations of pre- and post-saccadic visual stimuli. This contributes to the functional diversity of the retinas output signals and to asymmetries between On and Off pathways and provides evidence of signal processing beyond what is triggered by isolated steps in light intensity.

neuroscience↗