Functional connectivity in the retina at the resolution of photoreceptors

Field, Greg D.; Gauthier, Jeffrey; Sher, Alexander; Greschner, Martin; Machado, Timothy A.; Jepson, Lauren H.; Shlens, Jonathon; Gunning, Deborah E.; Mathieson, Keith; Dąbrowski, W.; Paninski, Liam; Litke, A. M.; Chichilnisky, E. J.

doi:10.1038/nature09424

Cited by 339 publications

(334 citation statements)

References 48 publications

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“…Surprisingly, SIFT is the worst descriptor in the first testing environment (8) similar to what has been shown in BRISK [16]. SIFT extracts several descriptors when the estimated orientation has multiple good candidates.…”

Section: Performance Evaluationsupporting

confidence: 71%

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FREAK: Fast Retina Keypoint

Alahi

Ortiz

Vandergheynst

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

1,461

954

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A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT) [17], Speed-up Robust Feature (SURF) [4], and more recently Binary Robust Invariant Scalable Keypoints (BRISK) [16] to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise.To best address the current requirements, we propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are thus competitive alternatives to existing keypoints in particular for embedded applications.

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Section: Performance Evaluationsupporting

confidence: 71%

“…Neuroscience has made lots of progress in understanding the visual system and how images are transmitted to the brain [8]. It is believed that the human retina extracts details from images using Difference of Gaussians (DoG) of various sizes and encodes such differences with action potentials.…”

Section: Motivationsmentioning

confidence: 99%

FREAK: Fast Retina Keypoint

Alahi

Ortiz

Vandergheynst

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

1,461

954

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“…178) In addition, extracellular multi-electrode recordings from hundreds of RGC with fine-grained white-noise stimulation allows mapping of RGC inputs from individual cone photoreceptors. 179) Intriguingly, tissue-clearing methodologies such as SCALE, CLARITY, SeeDB, CUBIC, and PACT have rendered the whole brain optically transparent, permitting circuit-and organ-wide mapping at cellular and subcellular resolution by endogenous expression of fluorescent proteins and post hoc detection of DNA, RNA, and peptides. [180][181][182][183][184] A mechanistic understanding of retinal regeneration will be necessary in order to develop new strategies for circuit reconstruction and vision restoration.…”

Section: Discussionmentioning

confidence: 99%

Challenges in Retinal Circuit Regeneration

Osakada

Takahashi²

2015

Biological & Pharmaceutical Bulletin

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Tremendous progress has been made in retinal regeneration, as exemplified by successful transplantation of retinal pigment epithelia and photoreceptor cells in the adult retina, as well as by generation of retinal tissue from embryonic stem cells and induced pluripotent cells. However, it remains unknown how new photoreceptors integrate within retinal circuits and contribute to vision restoration. There is a large gap in our understanding, at both the cellular and behavioral levels, of the functional roles of new neurons in the adult retina. This gap largely arises from the lack of appropriate methods for analyzing the organization and function of new neurons at the circuit level. To bridge this gap and understand the functional roles of new neurons in living animals, it will be necessary to identify newly formed connections, correlate them with function, manipulate their activity, and assess the behavioral outcome of these manipulations. Recombinant viral vectors are powerful tools not only for controlling gene expression and reprogramming cells, but also for tracing cell fates and neuronal connectivity, monitoring biological functions, and manipulating the physiological state of a specific cell population. These virus-based approaches, combined with electrophysiology and optical imaging, will provide circuit-level insight into neural regeneration and facilitate new strategies for achieving vision restoration in the adult retina. Herein, we discuss challenges and future directions in retinal regeneration research.

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“…3B). Indeed, mosaic spacing is one of the fundamental units defining cell types in the retina (Field and Chichilnisky 2007;Field et al 2010) and is often related to dendritic tiling or the extent to which the dendrites of retinal cell types avoid one another or overlap (Fig. 3E).…”

Section: Cell Type Specification In the Mammalian Visual Systemmentioning

confidence: 99%

So many pieces, one puzzle: cell type specification and visual circuitry in flies and mice

2014

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The visual system is a powerful model for probing the development, connectivity, and function of neural circuits. Two genetically tractable species, mice and flies, are together providing a great deal of understanding of these processes. Current efforts focus on integrating knowledge gained from three cross-fostering fields of research: (1) understanding how the fates of different cell types are specified during development, (2) revealing the synaptic connections between identified cell types (''connectomics'') by high-resolution three-dimensional circuit anatomy, and (3) causal testing of how identified circuit elements contribute to visual perception and behavior. Here we discuss representative examples from fly and mouse models to illustrate the ongoing success of this tripartite strategy, focusing on the ways it is enhancing our understanding of visual processing and other sensory systems.For many decades, the visual systems of both vertebrates and invertebrates have been a favorite arena for understanding how neural circuits are built and function. A considerable body of work has focused on the specification of cell types of the retina; for instance, the designation of different classes of photoreceptors with distinct spectral sensitivities in the Drosophila retina (Rister and Desplan 2011) and the establishment of the various cell types that comprise the vertebrate retina: photoreceptors, Muller glia, interneurons (horizontal, amacrine, and bipolar cells), and retinal ganglion cells (RGCs) (Livesey and Cepko 2001;Mu et al. 2004;Poch e and Reese 2006). With that knowledge in hand, focus in recent years has expanded to understanding how the circuits formed by these retinal cells are linked to the staggering number of diverse visual neurons in the brain, which in turn enables sophisticated and diverse computational tasks. The ultimate goal is to understand how cellular identity, function, and connectivity relate to visually guided behaviors. (Fig. 1A).

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Functional connectivity in the retina at the resolution of photoreceptors

Cited by 339 publications

References 48 publications

FREAK: Fast Retina Keypoint

FREAK: Fast Retina Keypoint

Challenges in Retinal Circuit Regeneration

So many pieces, one puzzle: cell type specification and visual circuitry in flies and mice

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