Receptive Field Vectors of Genetically-Identified Retinal Ganglion Cells Reveal Cell-Type-Dependent Visual Functions

Why do children with autism spectrum disorder have abnormal visual perception?

Autism spectrum disorder (ASD) is associated with severe impairment in social functioning. Visual information processing provides nonverbal cues that support social interactions. ASD children exhibit abnormalities in visual orientation, continuous visual exploration, and visual-spatial perception, causing social dysfunction, and mechanisms underlying these abnormalities remain unclear. Transmission of visual information depends on the retina-lateral geniculate nucleus-visual cortex pathway. In ASD, developmental abnormalities occur in rapid expansion of the visual cortex surface area with constant thickness during early life, causing abnormal transmission of the peak of the visual evoked potential (P100). We hypothesized that abnormal visual perception in ASD are related to the abnormal visual information transmission and abnormal development of visual cortex in early life, what's more, explored the mechanisms of abnormal visual symptoms to provide suggestions for future research.

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Objective. Retinal prostheses are promising devices to restore vision for patients with severe age-related macular degeneration or retinitis pigmentosa disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect. Its performance depends on our understanding of the retina's working mechanism and the evolvement of computer vision models. Recently, remarkable progress has been made in the field of processing algorithm for retinal prostheses where the new discovery of the retina's working principle and state-of-the-arts computer vision models are combined together.Approach. We investigated the related research on artificial intelligence techniques for retinal prostheses. The processing algorithm in these studies could be attributed to three types: computer vision-related methods, biophysical models, and deep learning models.Main results. In this review, we first illustrate the structure and function of the normal and degenerated retina, then demonstrate the vision rehabilitation mechanism of three representative retinal prostheses. It is necessary to summarize the computational frameworks abstracted from the normal retina. In addition, the development and feature of three types of different processing algorithms are summarized. Finally, we analyze the bottleneck in existing algorithms and propose our prospect about the future directions to improve the restoration effect.Significance. This review systematically summarizes existing processing models for predicting the response of the retina to external stimuli. What's more, the suggestions for future direction may inspire researchers in this field to design better algorithms for retinal prostheses.

Rapid Analysis of Visual Receptive Fields by Iterative Tomography

Many receptive fields in the early visual system show standard (center-surround) structure and can be analyzed using simple drifting patterns and a difference-of-Gaussians (DoG) model, which treats the receptive field as a linear filter of the visual image. But many other receptive fields show nonlinear properties such as selectivity for direction of movement. Such receptive fields are typically studied using discrete stimuli (moving or flashed bars and edges) and are modelled according to the features of the visual image to which they are most sensitive. Here, we harness recent advances in tomographic image analysis to characterize rapidly and simultaneously both the linear and nonlinear components of visual receptive fields. Spiking and intracellular voltage potential responses to briefly flashed bars are analyzed using non-negative matrix factorization (NNMF) and iterative reconstruction tomography (IRT). The method yields high-resolution receptive field maps of individual neurons and neuron ensembles in primate (marmoset, both sexes) lateral geniculate and rodent (mouse, male) retina. We show that the first two IRT components correspond to DoG-equivalent center and surround of standard [magnocellular (M) and parvocellular (P)] receptive fields in primate geniculate. The first two IRT components also reveal the spatiotemporal receptive field structure of nonstandard (on/off-rectifying) receptive fields. In rodent retina we combine NNMF-IRT with patch-clamp recording and dye injection to directly map spatial receptive fields to the underlying anatomy of retinal output neurons. We conclude that NNMF-IRT provides a rapid and flexible framework for study of receptive fields in the early visual system.

Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli

The retina dissects the visual scene into parallel information channels, which extract specific visual features through nonlinear processing. The first nonlinear stage is typically considered to occur at the output of bipolar cells, resulting from nonlinear transmitter release from synaptic terminals. In contrast, we show here that bipolar cells themselves can act as nonlinear processing elements at the level of their somatic membrane potential. Intracellular recordings from bipolar cells in the salamander retina revealed frequent nonlinear integration of visual signals within bipolar cell receptive field centers, affecting the encoding of artificial and natural stimuli. These nonlinearities provide sensitivity to spatial structure below the scale of bipolar cell receptive fields in both bipolar and downstream ganglion cells and appear to arise at the excitatory input into bipolar cells. Thus, our data suggest that nonlinear signal pooling starts earlier than previously thought: that is, at the input stage of bipolar cells.

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Some retinal bipolar cells represent visual contrast in a nonlinear fashion

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These bipolar cells also nonlinearly integrate visual signals over space

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The spatial nonlinearity affects the encoding of natural stimuli by bipolar cells

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The nonlinearity results from feedforward input, not from feedback inhibition

An offset ON-OFF receptive field is created by gap junctions between distinct types of retinal ganglion cells

In the vertebrate retina, the location of a neuron's receptive field in visual space closely corresponds to the physical location of synaptic input onto its dendrites, a relationship called the retinotopic map. We report the discovery of a systematic spatial offset between the ON and OFF receptive subfields in F-mini-ON retinal ganglion cells (RGCs). Surprisingly, this property does not come from spatially offset ON and OFF layer dendrites, but instead arises from a network of electrical synapses via gap junctions to RGCs of a different type, the F-mini-OFF. We show that the asymmetric morphology and connectivity of these RGCs can explain their receptive field offset, and we use a multicell model to explore the effects of receptive field offset on the precision of edge-location representation in a population. This RGC network forms a new electrical channel combining the ON and OFF feedforward pathways within the output layer of the retina.

The dynamic receptive fields of retinal ganglion cells

Retinal ganglion cells (RGCs) were one of the first classes of sensory neurons to be described in terms of a receptive field (RF). Over the last six decades, our understanding of the diversity of RGC types and the nuances of their response properties has grown exponentially. We will review the current understanding of RGC RFs mostly from studies in mammals, but including work from other vertebrates as well. We will argue for a new paradigm that embraces the fluidity of RGC RFs with an eye toward the neuroethology of vision. Specifically, we will focus on (1) different methods for measuring RGC RFs, (2) RF models, (3) feature selectivity and the distinction between fluid and stable RF properties, and (4) ideas about the future of understanding RGC RFs.