Novel neural circuit mechanism for visual edge detection

Stevens, Charles F.

doi:10.1073/pnas.1422673112

Cited by 5 publications

(17 citation statements)

References 22 publications

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“…These responses result from computations performed on retinal input (19). Computations of edge orientation by cells in V1 is an especially well-studied characteristic across species in terms of underlying anatomy and function (6,(20)(21)(22)(23).…”

Section: Discussionmentioning

confidence: 99%

Predicting visual acuity from the structure of visual cortex

Srinivasan

Carlo

Stevens

2015

Proc. Natl. Acad. Sci. U.S.A.

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Three decades ago, Rockel et al. proposed that neuronal surface densities (number of neurons under a square millimeter of surface) of primary visual cortices (V1s) in primates is 2.5 times higher than the neuronal density of V1s in nonprimates or many other cortical regions in primates and nonprimates. This claim has remained controversial and much debated. We replicated the study of Rockel et al. with attention to modern stereological precepts and show that indeed primate V1 is 2.5 times denser (number of neurons per square millimeter) than many other cortical regions and nonprimate V1s; we also show that V2 is 1.7 times as dense. As primate V1s are denser, they have more neurons and thus more pinwheels than similar-sized nonprimate V1s, which explains why primates have better visual acuity., in an influential and controversial article entitled "The basic uniformity in structure of the neocortex," reported that the number of neurons underneath a square millimeter of neocortical surface is constant for six cortical areas and five species with one exception: primate primary visual cortex (V1) has a surface density of about 250,000 neurons/mm 2 , around two and a half times the usual density for other areas studied.The Rockel et al. paper has, for a third of a century, continued to generate controversy for two reasons. One reason stems from its implications for an equally energetic debate among neuroscientist "lumpers" and "splitters." Cortical uniformity supports a theory of neocortical processing wherein different cortical areas are subserved by the same canonical circuit, a view favored by lumpers. Splitters, however, believe each cortical area to be different and doubt the paper's claims (2). The second reason is that studies from various laboratories using different measurement methods over the last three decades have alternately agreed and disagreed with Rockel et al.'s results (2).Notably, however, Rockel et al's studies have never been directly replicated. We set out to repeat the observations for the same areas and species Rockel et al. used. We used Rockel et al.'s counting techniques but with attention to the precepts of modern stereology (2). Our goal was to simply determine if Rockel et al.'s observations are repeatable rather than address the larger question of numerical uniformity of neocortex across species and areas. In an earlier publication (2), we confirmed Rockel et al.'s conclusions for nonvisual areas. Here we focus on the primary V1 for the same species used in the original report of Rockel et al.V1 is part of the visual circuit from the retina to the cortex, which is retinotopically organized, and the 2D image of the world that is mapped onto the retina is recreated in V1 (3). Cells within the retina capture visual information for each image location or pixel such as color and light intensity and convey it to structures in V1 (4, 5), which perform computations that contribute to visual abilities. An especially well-studied V1 structure is a pinwheel, which comprises orientation columns tha...

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Section: Discussionmentioning

confidence: 99%

Predicting visual acuity from the structure of visual cortex

Srinivasan

Carlo

Stevens

2015

Proc. Natl. Acad. Sci. U.S.A.

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“…The Fourier transform is the tool we deploy in dealing with spatial frequencies [ 12 ]. The visual cortex, V1, analyzes visual information by computing the signal intensity of an image, say, \begin{document}S(x)\end{document} , on the retina.…”

Section: Reviewmentioning

confidence: 99%

“…This is known as a filter, \begin{document}K(x)\end{document} . Note that \begin{document}K\end{document} depends on \begin{document}x\end{document} , the activated locality in the retina [ 12 ]. This filtering function is how the perception of the image is constructed, a percept.…”

Section: Reviewmentioning

confidence: 99%

The Biophysics of Visual Edge Detection: A Review of Basic Principles

Kesserwani

2020

Cureus

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The mathematization of nature is an age-old concept. The Greeks sought harmony in the celestial spheres. The Arab geometers constructed a spherical geometry of the heavens. Later, Galileo Galilei arithmetized kinematics. As the centuries advanced, polymaths like the Dutchman Christiaan Huygens applied more advanced mathematics in order to understand natural phenomena. It was not until the turn of the twentieth century that a more comprehensive mathematical approach to understanding biological phenomena was sought by D’Arcy Wentworth Thompson. This leads us to our current review of the biophysics of visual edge detection. This is an unfolding saga of stunning experimental revelations in unison with an underlying mathematical edifice. The concept of visual contrast is a fundamental idea in order to understand the phenomenon of visual edge detection. We begin with contrast visual testing and the development of frequency tuning curves, which provided an insight into the multi-channel processing of selective spatial frequencies by the visual cortex. The single-cell recordings from the simple cells of the cat visual cortex unfolded the gamma distribution curves of different neuronal firing frequencies for different spatial frequencies. The theoretical construction of the convolution of Gabor wavelets with stimulus intensity goes hand-in-hand with the experimental observation of the separation of simple visual cortical cells into even and odd functions, a spectacular finding. In this review, we march the reader through the mathematical basics and the pathophysiologic correlates. Beginning with a simple Fourier analysis of a square wave, Weber’s biophysical law, and the gamma distribution of contrast tuning curves, we gradually introduce Fourier transforms, the uncertainty principle of waveform analysis, the basics of wavelet theory, Gabor elementary signals and transforms, the concepts of coherence, and Weyl group representation theory. Group theory provides the symmetry operations necessary to preserve the fidelity of an image as it travels from the retina and cascades up the visual cortex. Unitary operators that allow a retinal displacement of an image to be reflected by a similar displacement in the visual cortex is also a fundamental principle. Along the way, we encounter the Convolution Theorem of Fourier transforms, which is critical in constructing a visual percept. We intermittently interject relevant clinical data as we unpack the mathematical complexities. The advanced mathematics deployed in the biophysics of vision makes for difficult reading. There is a paucity of step-by-step reviews of this subject. Our approach is heuristic and at the end of this review, one should be able to follow superficially the algorithmic steps in understanding visual edge detection using Gabor filters. Therefore, we will adopt the Socratic method of asking questions and providing answers to help us through the complex web of mathematics. In a nutshell, we will show that a Gabor fi...

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“…3). The net loss of Gabor-like receptive fields impacting orientation selectivity makes intuitive sense, as such receptive fields are typically interpreted to be edge-detectors [40]. This is also a new prediction that could potentially be tested experimentally in longitudinal experiments on the same individuals, measuring the receptive fields of a population of neurons in visual cortex in both youth and old age, inferring the receptive fields using, for example, generalized linear model fits [41][42][43][44][45][46], and comparing the distributions of the receptive fields at these different ages.…”

Section: Comparison To Experimental Results and Implications For Futumentioning

confidence: 99%

Dysregulation of excitatory neural firing replicates physiological and functional changes in aging visual cortex

Talyansky

Brinkman

2020

Preprint

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The mammalian visual system has been the focus of countless experimental and theoretical studies designed to elucidate principles of sensory coding. Most theoretical work has focused on networks intended to reflect developing or mature neural circuitry, in both health and disease. Few computational studies have attempted to model changes that occur in neural circuitry as an organism ages non-pathologically. In this work we contribute to closing this gap, studying how physiological changes correlated with advanced age impact the computational performance of a spiking network model of primary visual cortex (V1). Our results demonstrate that deterioration of homeostatic regulation of excitatory firing, coupled with long-term synaptic plasticity, is a sufficient mechanism to reproduce features of observed physiological and functional changes in neural activity data, specifically declines in inhibition and in selectivity to oriented stimuli. This suggests a potential causality between dysregulation of neuron firing and age-induced changes in brain physiology and performance. While this does not rule out deeper underlying causes or other mechanisms that could give rise to these changes, our approach opens new avenues for exploring these underlying mechanisms in greater depth and making predictions for future experiments. IntroductionDespite affecting approximately 100% of the human population, healthy aging, and in particular its impact on neurological performance, has been the focus of few theoretical and computational studies in neuroscience, 1 compared to diseases such as Alzheimer's [1][2][3][4][5][6][7][8][9][10][11][12] or , which often manifest in advanced age in humans [21]. Only recently has theoretical and computational work on aging in non-pathological networks begun to emerge [22]. Because advanced age is one of the most important risk factors for developing such neurological disorders [21,23], to fully understand the progression of these diseases we ought to have a baseline understanding of how the brain's circuitry changes under healthy aging, both in terms of physiological properties and functional performance. This would help dissociate disease-related changes from those caused during normal aging, and thereby allow researchers to focus their attention on treating potential causes of the disease progression not directly related to aging. On the other hand, understanding how the healthy brain ages may enable us to treat declines in performance caused solely by aging, in both healthy subjects and those with neurological disorders or diseases.In this work we seek to advance our understanding of potential mechanisms and consequences of ageinduced changes in physiology and performance in visual cortex. We do so by adapting a previouslydeveloped spiking network model of V1 activity, E-I Net [24]. The network structure of E-I Net is learned by training it on pixel images of natural scenes, to which it develops Gabor-like receptive fields. The synaptic and homeostatic learning rules are local, and therefore bio...

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Novel neural circuit mechanism for visual edge detection

Cited by 5 publications

References 22 publications

Predicting visual acuity from the structure of visual cortex

Predicting visual acuity from the structure of visual cortex

The Biophysics of Visual Edge Detection: A Review of Basic Principles

Dysregulation of excitatory neural firing replicates physiological and functional changes in aging visual cortex

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