Showing papers on "Orientation column published in 2012"
TL;DR: It is shown that, in contrast to pyramidal cells, the response of somatostatin-expressing inhibitory neurons in the superficial layers of the mouse visual cortex increases with stimulation of the receptive-field surround, establishing a cortical circuit for surround suppression and attributing a particular function to a genetically defined type of inhibitory neuron.
Abstract: The response of cortical neurons to a sensory stimulus is modulated by the context. In the visual cortex, for example, stimulation of a pyramidal cell's receptive-field surround can attenuate the cell's response to a stimulus in the centre of its receptive field, a phenomenon called surround suppression. Whether cortical circuits contribute to surround suppression or whether the phenomenon is entirely relayed from earlier stages of visual processing is debated. Here we show that, in contrast to pyramidal cells, the response of somatostatin-expressing inhibitory neurons (SOMs) in the superficial layers of the mouse visual cortex increases with stimulation of the receptive-field surround. This difference results from the preferential excitation of SOMs by horizontal cortical axons. By perturbing the activity of SOMs, we show that these neurons contribute to pyramidal cells' surround suppression. These results establish a cortical circuit for surround suppression and attribute a particular function to a genetically defined type of inhibitory neuron.
615 citations
TL;DR: It is found that an anatomical image alone can be used to predict the retinotopic organization of striate cortex for an individual with accuracy equivalent to 10-25 min of functional mapping, which indicates tight developmental linkage of structure and function within a primary, sensory cortical area.
235 citations
TL;DR: Orientation columns are iconic examples of topographic specificity, whereby axons within a column connect with cells of a single orientation preference, which together with cell-type specificity constitute the major determinants of nonrandom cortical connectivity.
79 citations
TL;DR: The data, spanning a 40-fold range of body sizes in Laurasiatheria and Euarchonta, do not support the conjecture that pinwheel density scales with body and brain size, and the noncolumnar layout in Glires appears size-insensitive.
Abstract: Meng et al. conjecture that pinwheel density scales with body and brain size. Our data, spanning a 40-fold range of body sizes in Laurasiatheria and Euarchonta, do not support this conclusion. The noncolumnar layout in Glires also appears size-insensitive. Thus, body and brain size may be understood as a constraint on the evolution of visual cortical circuitry, but not as a determining factor.
40 citations
TL;DR: The results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification.
40 citations
TL;DR: Neurons projecting to the posterior suprasylvian sulcus were more direction selective and preferred shorter stimuli, higher spatial frequencies, and higher temporal frequencies than neurons projecting to area 21, anticipating key differences between the functional properties of the target areas themselves.
33 citations
TL;DR: A general dynamical systems approach to the combined optimization of visual cortical maps of OP and another scalar feature such as OD or spatial frequency preference is developed and it is shown that each individual coupling energy leads to a different class of OP solutions with different correlations among the maps.
Abstract: In the primary visual cortex of primates and carnivores, functional architecture can be characterized by maps of various stimulus features such as orientation preference (OP), ocular dominance (OD), and spatial frequency. It is a long-standing question in theoretical neuroscience whether the observed maps should be interpreted as optima of a specific energy functional that summarizes the design principles of cortical functional architecture. A rigorous evaluation of this optimization hypothesis is particularly demanded by recent evidence that the functional architecture of orientation columns precisely follows species invariant quantitative laws. Because it would be desirable to infer the form of such an optimization principle from the biological data, the optimization approach to explain cortical functional architecture raises the following questions: i) What are the genuine ground states of candidate energy functionals and how can they be calculated with precision and rigor? ii) How do differences in candidate optimization principles impact on the predicted map structure and conversely what can be learned about a hypothetical underlying optimization principle from observations on map structure? iii) Is there a way to analyze the coordinated organization of cortical maps predicted by optimization principles in general? To answer these questions we developed a general dynamical systems approach to the combined optimization of visual cortical maps of OP and another scalar feature such as OD or spatial frequency preference. From basic symmetry assumptions we obtain a comprehensive phenomenological classification of possible inter-map coupling energies and examine representative examples. We show that each individual coupling energy leads to a different class of OP solutions with different correlations among the maps such that inferences about the optimization principle from map layout appear viable. We systematically assess whether quantitative laws resembling experimental observations can result from the coordinated optimization of orientation columns with other feature maps.
25 citations
TL;DR: Most mouse V1 neurons showed contrast adaptation that was robust regardless of whether the adapting stimulus matched the cell's preferred orientation or was orthogonal to it, and this study established a quantitative description of contrast adaptation in an animal model.
Abstract: Contrast adaptation is a commonly studied phenomenon in vision, where prolonged exposure to spatial contrast alters perceived stimulus contrast and produces characteristic shifts in the contrast re...
19 citations
TL;DR: This work describes the findings that contrast-invariant spatial tuning occurs not only in the responses of lateral geniculate nucleus (LGN) relay cells but also in their afferent retinal input, and suggests that a similar contrast-Invariant mechanism is found throughout the stages of the early visual pathway.
Abstract: Sensory cortex is able to encode a broad range of stimulus features despite a great variation in signal strength. In cat primary visual cortex (V1), for example, neurons are able to extract stimulus features like orientation or spatial configuration over a wide range of stimulus contrasts. The contrast-invariant spatial tuning found in V1 neuron responses has been modeled as a gain control mechanism, but at which stage of the visual pathway it emerges has remained unclear. Here we describe our findings that contrast-invariant spatial tuning occurs not only in the responses of lateral geniculate nucleus (LGN) relay cells but also in their afferent retinal input. Our evidence suggests that a similar contrast-invariant mechanism is found throughout the stages of the early visual pathway, and that the contrast-invariant spatial selectivity is evident in both retinal ganglion cell and LGN cell responses.
19 citations
TL;DR: In this paper, the authors explored the development of orientation selectivity in visual cortex with a focus on linear and nonlinear factors in a population of anesthetized 4-week postnatal kittens and adult cats.
Abstract: Orientation selectivity and its development are basic features of visual cortex. The original model of orientation selectivity proposes that elongated simple cell receptive fields are constructed from convergent input of an array of lateral geniculate nucleus neurons. However, orientation selectivity of simple cells in the visual cortex is generally greater than the linear contributions based on projections from spatial receptive field profiles. This implies that additional selectivity may arise from intracortical mechanisms. The hierarchical processing idea implies mainly linear connections, whereas cortical contributions are generally considered to be nonlinear. We have explored development of orientation selectivity in visual cortex with a focus on linear and nonlinear factors in a population of anesthetized 4-wk postnatal kittens and adult cats. Linear contributions are estimated from receptive field maps by which orientation tuning curves are generated and bandwidth is quantified. Nonlinear components are estimated as the magnitude of the power function relationship between responses measured from drifting sinusoidal gratings and those predicted from the spatial receptive field. Measured bandwidths for kittens are slightly larger than those in adults, whereas predicted bandwidths are substantially broader. These results suggest that relatively strong nonlinearities in early postnatal stages are substantially involved in the development of orientation tuning in visual cortex.
15 citations
TL;DR: It is suggested that a simple brain size–pinwheel density scaling law suffices in predicting the self-organized and disorganized orientation maps from primates to rodents.
Abstract: Kaschube et al . (Reports, 19 November 2010, p. 1113) argue that pinwheel density in three mammalian species follows a universal constant of π as predicted by their orientation-selective suppressive long-range connectivity model. We dispute their conclusions and suggest that a simple brain size–pinwheel density scaling law suffices in predicting the self-organized and disorganized orientation maps from primates to rodents.
TL;DR: This work establishes that the nonconvolutional (or long-range) connectivity is patchy and is co-aligned in the case of orientation learning, and shows how restricting the dimension of the space where the neurons live gives rise to patterns similar to cortical maps.
Abstract: We show how a Hopfield network with modifiable recurrent connections undergoing slow Hebbian learning can extract the underlying geometry of an input space. First, we use a slow and fast analysis to derive an averaged system whose dynamics derives from an energy function and therefore always converges to equilibrium points. The equilibria reflect the correlation structure of the inputs, a global object extracted through local recurrent interactions only. Second, we use numerical methods to illustrate how learning extracts the hidden geometrical structure of the inputs. Indeed, multidimensional scaling methods make it possible to project the final connectivity matrix onto a Euclidean distance matrix in a high-dimensional space, with the neurons labeled by spatial position within this space. The resulting network structure turns out to be roughly convolutional. The residual of the projection defines the nonconvolutional part of the connectivity, which is minimized in the process. Finally, we show how restricting the dimension of the space where the neurons live gives rise to patterns similar to cortical maps. We motivate this using an energy efficiency argument based on wire length minimization. Finally, we show how this approach leads to the emergence of ocular dominance or orientation columns in primary visual cortex via the self-organization of recurrent rather than feedforward connections. In addition, we establish that the nonconvolutional (or long-range) connectivity is patchy and is co-aligned in the case of orientation learning.
01 Jan 2012
TL;DR: Functional maps with submillimetre resolution will enable a much more precise correlation of brain functions with the neural tissue that supports them, and is likely to bring about major conceptual changes in systems neuroscience, especially in analysis methodology.
Abstract: The chief advantages of using high-field MRI for neuroscientific research are the improvements in spatial resolution and contrast that become available. Neuroscientists are interested in the spatial organisation of brain grey matter, in cortex and deep brain structures, and in the connectivity of white matter neuronal fibres. At lower field, it is very hard to distinguish cortical areas purely by their anatomical differences, or to discriminate subcomponents of basal ganglia and thalamus. This has led to a widely accepted method of functional image analysis involving warping of individual brains to a standardised template, together with significant image smoothing, which eliminates the possibility of detailed MRI-based mapping of human brain, and severely handicaps the exploration of individual differences and monitoring of brain changes over time. Even at a field of 3 T, the spatial resolution of MR tractography is limited to about 1.5 mm isotropic, hindering discrimination of crossing fibres. However, at fields of 7 T and above, the available high isotropic resolution of 0.4 mm and the varying myelin content of grey matter allow several cortical areas to be quite easily distinguished, and the varying iron content of deeper brain structures reveals their internal features. Higher spatial isotropic resolution in tractography can also be achieved, of 1 mm or better. Because blood oxygenation-dependent contrast (BOLD) also improves at high field, functional maps with submillimetre resolution can be acquired, showing columnar structures such as ocular dominance and orientation columns. These results will enable a much more precise correlation of brain functions with the neural tissue that supports them, and is likely to bring about major conceptual changes in systems neuroscience, especially in analysis methodology.
TL;DR: A combination of two gratings in different mutual relationships as in a plaid to study how visual cortical neurons differ in integrating signals from a limited number of preferred stimulus orientations represented in the geniculate afferents to the striate cortex.
09 Nov 2012
TL;DR: The next generation of policymakers and decision-makers will have to consider climate change in more detail than ever before.
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01 Jan 2012
TL;DR: In this article, a list of selected additional articles on the Science Web sites http://www.sciencemag.org/content/336/6080/413.4.full.html#ref-list-1, 5 of which can be accessed free: cite 13 articles
Abstract: clicking here. colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to others here. following the guidelines can be obtained by Permission to republish or repurpose articles or portions of articles ): September 4, 2012 www.sciencemag.org (this information is current as of The following resources related to this article are available online at http://www.sciencemag.org/content/336/6080/413.4.full.html version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag.org/content/336/6080/413.4.full.html#related found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/content/336/6080/413.4.full.html#ref-list-1 , 5 of which can be accessed free: cites 13 articles This article http://www.sciencemag.org/cgi/collection/tech_comment Technical Comments http://www.sciencemag.org/cgi/collection/neuroscience Neuroscience subject collections: This article appears in the following
TL;DR: Using extracellular recording of spike activity from single neurons of field 21a of the cat neocortex, the spatial organization of receptive fields (RFs) of such cells after conditions of presentation of an immobile blinking light spot and moving visual stimuli is examined.
Abstract: Using extracellular recording of spike activity from single neurons of field 21a of the cat neocortex, we examined in detail the spatial organization of receptive fields (RFs) of such cells after conditions of presentation of an immobile blinking light spot (a static RF) and moving visual stimuli (dynamic RFs). As was shown, the excitability of different RF subfields of a group of neurons possessing homogeneous on–off organization of the static RF changes significantly depended on the contrast, shape, dimension, orientation, and direction of movement of the applied mobile visual stimulus. This is manifested in changes in the number of discharge centers and shifts of their spatial localization. A hypothesis on the possible role of synchronous activation of the neurons neighboring the cell under study in the formation of an additional neuronal mechanism providing specialization of neuronal responses is proposed.
26 Sep 2012
TL;DR: This chapter shows a possible configuration of feature representation in the visual cortex using a three-dimensional (3D) self-organization model, and confirms the predicted 3D orientation representation using multi-slice, high-resolution functional magnetic resonance imaging (fMRI) performed in the cat visual cortex.
Abstract: Orientation selectivity of neurons in the primary visual cortex is thought to be an important requisite for the preprocessing of visual information, which is followed by more complex information processing and representation in the extrastriate cortex for visual perception. It is widely accepted that neurons in the primary visual cortex optimally responding to similar stimulus orientations are clustered in a manner of straight columns extending from the superficial to deep layers (Hubel & Wiesel, 1962, 1963a). The cerebral cortex is, however, folded inside a skull, which makes gyri and fundi. Particularly, in cats, area 17 (primary visual cortex) is located on the curved cortex called the lateral gyrus (Tusa et al., 1978). These facts raise questions of how the tangential arrangement of orientation columns is reconciled with the curvature of the gyrus, and whether the columns penetrate the cortex from the superficial to deep layers. In the first part of this chapter, we show a possible configuration of feature representation in the visual cortex using a three-dimensional (3D) self-organization model, and then confirm the predicted 3D orientation representation using multi-slice, high-resolution functional magnetic resonance imaging (fMRI) performed in the cat visual cortex (Tanaka et al., 2011). We obtained a close agreement in orientation representation between theoretical predictions and experimental observations. These studies demonstrated that in the curved cortex, preferred orientations are represented by wedgelike orientation columns which do not necessarily penetrate from superficial to deep layers, whereas in the flat cortex, preferred orientations are tended to be represented by classical straight columns.