Visual Cortex V1 Research Articles

Fourier Descriptors Based on the Structure of the Human Primary Visual Cortex with Applications to Object Recognition

In this paper we propose a supervised object recognition method using new global features and inspired by the model of the human primary visual cortex V1 as the semidiscrete roto-translation group $SE(2,N) = \mathbb Z_N\rtimes \mathbb R^2$. The proposed technique is based on generalized Fourier descriptors on the latter group, which are invariant to natural geometric transformations (rotations, translations). These descriptors are then used to feed an SVM classifier. We have tested our method against the COIL-100 image database and the ORL face database, and compared it with other techniques based on traditional descriptors, global and local. The obtained results have shown that our approach looks extremely efficient and stable to noise, in presence of which it outperforms the other techniques analyzed in the paper.

Neural architectures for stereo vision

Stereoscopic vision delivers a sense of depth based on binocular information but additionally acts as a mechanism for achieving correspondence between patterns arriving at the left and right eyes. We analyse quantitatively the cortical architecture for stereoscopic vision in two areas of macaque visual cortex. For primary visual cortex V1, the result is consistent with a module that is isotropic in cortical space with a diameter of at least 3 mm in surface extent. This implies that the module for stereo is larger than the repeat distance between ocular dominance columns in V1. By contrast, in the extrastriate cortical area V5/MT, which has a specialized architecture for stereo depth, the module for representation of stereo is about 1 mm in surface extent, so the representation of stereo in V5/MT is more compressed than V1 in terms of neural wiring of the neocortex. The surface extent estimated for stereo in V5/MT is consistent with measurements of its specialized domains for binocular disparity. Within V1, we suggest that long-range horizontal, anatomical connections form functional modules that serve both binocular and monocular pattern recognition: this common function may explain the distortion and disruption of monocular pattern vision observed in amblyopia.This article is part of the themed issue ‘Vision in our three-dimensional world’.

Brightness-color interactions in human early visual cortex.

The interaction between brightness and color causes there to be different color appearance when one and the same object is viewed against surroundings of different brightness. Brightness contrast causes color to be desaturated, as has been found in perceptual experiments on color induction and color-gamut expansion in human vision. However, it is not clear yet where in the cerebral cortex the brightness-color interaction that causes these major perceptual effects is located. One hypothesis is that brightness and color signals are processed separately and in parallel within the primary visual cortex V1 and only interact in extrastriate cortex. Another hypothesis is that color and brightness contrast interact strongly already within V1. We localized the brightness-color interaction in human V1 by means of recording the chromatic visual-evoked potential. The chromatic visual-evoked potential measurements decisively support the idea that brightness-color interaction arises in a recurrent inhibitory network in V1. Furthermore, our results show that the inhibitory signal for brightness-color interaction is generated by local brightness contrast at the boundary between target and surround, instead of by the luminance difference between the interior of the color target and its large background.

Tracing path-guided apparent motion in human primary visual cortex V1.

Vision is a constructive process. For example, a square, flashed at two distinct locations one after the other, appears to move smoothly between the two locations rather than as two separate flashes (apparent motion). Apparent motion is usually perceived along the shortest path between locations. Previous studies have shown that retinotopic activity in V1 correlates well with the subjective filling-in in apparent motion. If V1 activity truly reflects illusory motion, it should flexibly reflect filling-in of any path, subjectively perceived. Here, we used a path-guided apparent motion paradigm in which a faint cue, presented in addition to the squares, leads to a curved illusory motion path. We found retinotopic activity in V1 to reflect the illusory filling-in of the curved path, similarly to filling-in with linear, shortest paths. Moreover, our results show that activity along the linear path was less selective to stimulus conditions than the activity along the curved path. This finding may be interpreted as V1 activity representing a small subset of infinitely many possible solutions to ambiguous stimuli, whilst giving more weight to the shortest path/energy solution.

The representation of visual depth perception based on the plenoptic function in the retina and its neural computation in visual cortex V1.

BackgroundHow it is possible to “faithfully” represent a three-dimensional stereoscopic scene using Cartesian coordinates on a plane, and how three-dimensional perceptions differ between an actual scene and an image of the same scene are questions that have not yet been explored in depth. They seem like commonplace phenomena, but in fact, they are important and difficult issues for visual information processing, neural computation, physics, psychology, cognitive psychology, and neuroscience.ResultsThe results of this study show that the use of plenoptic (or all-optical) functions and their dual plane parameterizations can not only explain the nature of information processing from the retina to the primary visual cortex and, in particular, the characteristics of the visual pathway’s optical system and its affine transformation, but they can also clarify the reason why the vanishing point and line exist in a visual image. In addition, they can better explain the reasons why a three-dimensional Cartesian coordinate system can be introduced into the two-dimensional plane to express a real three-dimensional scene.Conclusions1. We introduce two different mathematical expressions of the plenoptic functions, P w and P v that can describe the objective world. We also analyze the differences between these two functions when describing visual depth perception, that is, the difference between how these two functions obtain the depth information of an external scene.2. The main results include a basic method for introducing a three-dimensional Cartesian coordinate system into a two-dimensional plane to express the depth of a scene, its constraints, and algorithmic implementation. In particular, we include a method to separate the plenoptic function and proceed with the corresponding transformation in the retina and visual cortex.3. We propose that size constancy, the vanishing point, and vanishing line form the basis of visual perception of the outside world, and that the introduction of a three-dimensional Cartesian coordinate system into a two dimensional plane reveals a corresponding mapping between a retinal image and the vanishing point and line.

Assessing the role of synchronization and phase coherence in neural communication comparing cortical recordings and integrate-and-fire network models

Cognitive functions likely require that the routes of neural communication can be flexibly modulated. A proposed mechanism for modulating the effective strength of the connections in the neural dynamics relies on band specific neural synchronization and phase relations [1]. Evidence of the modulation of neuronal interactions through the phase relation of rhythmic activity in the gamma band was provided in [2]. Further evidence based on the analysis of a network of integrate-and fire neurons [3] has shown that the phase relation also modulates information transfer and is not specific of the gamma band. Here we combine the study of experimental recordings of local field potentials (LFP’s) and multiple-unit activity (MUA) together with model data to better understand the origins of the phase-dependent modulation of interactions. Recordings include spontaneous activity and natural stimulus-driven activity in the monkey visual cortex V1 [4], as well as natural-stimulus driven activity in monkey auditory cortex [5]. Simulations were obtained extending the recurrent network of integrate-and-fire neurons used in [6] to model the connectivity between two different brain areas. We address some open questions regarding the generation, generality, and mechanistic nature of the phase-dependent modulation. We obtained, for any frequency band, the instantaneous power in each area (reflecting the local neural synchronization), and the instantaneous phases. We analyzed how the power correlation is modulated by the phase relation with a 1ms resolution, in contrast to the hundreds of milliseconds in [2]. We found that this modulation is accompanied by changes in the magnitude of the power of each area separately. Accordingly, we evaluated the role of the power determining the degree of phase coherence and thus the existence of a preferred phase relation. We found that the optimal phase relation associated with maximal power correlation always corresponds to the preferred phase relation for large powers. These results are not frequency band specific and were reproduced with model data, using both unidirectional and bidirectional connections, as well as both excitatory-excitatory and excitatory-inhibitory connections. Our analysis suggests that the degree of local neural synchronization that determines the power of a given rhythm in each of the interacting areas plays a role to be considered together with the one of the phase relations as part of the mechanisms that modulate dynamically the effective strength of the connections.

EGGDD: An explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain

Most of the traditional medical image fusion methods that use the multi-scale decomposition schemes suffer from the bad image representations and the loss of the dependency in different highpass subbands. To deal with these problems, a novel dependency model, named Explicit Generalized Gaussian Density Dependency (EGGDD) model, is developed by the shift-invariant shearlet transform (SIST). Substantially different from describing the dependency by two hidden states in the Hidden Markov Tree (HMT) model, we provide the scheme to explicitly describe the marginal statistics of each highpass subband using the Generalized Gaussian Density (GGD), as well as the dependency between different subbands by the Kullback–Leibler distance (KLD). After embedding the obtained dependency into each highpass subband, an efficient fusion scheme, inspired by the divisive normalization response in the V1 visual cortex model, is developed to combine the highpass-subband coefficients. The experiments demonstrate that the developed method can produce better fusion results than those of some existing methods by the comparison of visual sense and quantitative measurements in terms of mutual information, entropy, spatial frequency, standard deviation, QAB/F and QW.

Sustained Multifocal Attentional Enhancement of Stimulus Processing in Early Visual Areas Predicts Tracking Performance

Keeping track of multiple moving objects is an essential ability of visual perception. However, the mechanisms underlying this ability are not well understood. We instructed human observers to track five or seven independent randomly moving target objects amid identical nontargets and recorded steady-state visual evoked potentials (SSVEPs) elicited by these stimuli. Visual processing of moving targets, as assessed by SSVEP amplitudes, was continuously facilitated relative to the processing of identical but irrelevant nontargets. The cortical sources of this enhancement were located to areas including early visual cortex V1-V3 and motion-sensitive area MT, suggesting that the sustained multifocal attentional enhancement during multiple object tracking already operates at hierarchically early stages of visual processing. Consistent with this interpretation, the magnitude of attentional facilitation during tracking in a single trial predicted the speed of target identification at the end of the trial. Together, these findings demonstrate that attention can flexibly and dynamically facilitate the processing of multiple independent object locations in early visual areas and thereby allow for tracking of these objects.

Estimating nonstationary input signals from a single neuronal spike train

Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic assumption that presynaptic activity is constant over time. Here, we propose tracking temporal variations in input parameters with a two-step analysis method. First, nonstationary firing characteristics comprising the firing rate and non-Poisson irregularity are estimated from a spike train using a computationally feasible state-space algorithm. Then, information about the firing characteristics is converted into likely input parameters over time using a transformation formula, which was constructed by inverting the neuronal forward transformation of the input current to output spikes. By analyzing spike trains recorded in vivo, we found that neuronal input parameters are similar in the primary visual cortex V1 and middle temporal area, whereas parameters in the lateral geniculate nucleus of the thalamus were markedly different.

Model approach to neurological variants of visuo-spatial neglect

Neglect is a neurological disorder of spatial attention with reduced awareness of visual stimuli in the hemifield contralateral to an acute temporo-parietal lesion mainly of the right hemisphere. There is a close association of multisensory orientation centers (MSO) and vestibular tonus imbalance. A lesion of the dominant right MSO causes a left-sided neglect due to a lack of ipsilateral activation of the visual cortex, which is further enhanced by increasing inhibition from the contralateral visual cortex. The nondominant MSO in the left hemisphere might be involved in the manifestation of the less frequent and more transient right-sided neglect and in the plastic mechanisms of gradual recovery from left-sided neglect or extinction. There is evidence that a vestibular tonus inbalance due to peripheral or central vestibular pathway lesions may also induce a neglect. In a first model approach using an attractor network and assuming that there is only one MSO in the right hemisphere, it is possible to simulate attentional shifts into a visual hemifield and to induce a neglect. The neural network model consists of four layers of neurons: retina, MSO, visual cortex V1, and superior colliculus. The superior colliculus layer is modeled as a recurrent attractor network with one inhibitory interneuron and synaptic weights chosen to implement a winner-take-all network that centers the hill of activity on the strongest input. We are well aware of the simplifications used in the conceptual drawings and the computational model, but nevertheless hope that they will serve as an inspiration for further modeling and clinical studies.

Anthropomorphic Image Reconstruction via Hypoelliptic Diffusion

In this paper we study a model of geometry of vision due to Petitot, Citti, and Sarti. One of the main features of this model is that the primary visual cortex V1 lifts an image from $\mathbb{R}^2$ to the bundle of directions of the plane. Neurons are grouped into orientation columns, each of them corresponding to a point of this bundle. In this model a corrupted image is reconstructed by minimizing the energy necessary for the activation of the orientation columns corresponding to regions in which the image is corrupted. The minimization process intrinsically defines a hypoelliptic heat equation on the bundle of directions of the plane. In the original model, directions are considered both with and without orientation, giving rise, respectively, to a problem on the group of rototranslations of the plane $SE(2)$ or on the projective tangent bundle of the plane $PT\mathbb{R}^2$. We provide a mathematical proof of several important facts for this model. We first prove that the model is mathematically consistent only if directions are considered without orientation. We then prove that the convolution of an $L^2(\mathbb{R}^2,\mathbb{R})$ function (e.g., an image) with a two-dimensional (2D) Gaussian is generically a Morse function. This fact is important since the lift of Morse functions to $PT\mathbb{R}^2$ is defined on a smooth manifold. We then provide the explicit expression of the hypoelliptic heat kernel on $PT\mathbb{R}^2$ in terms of Mathieu functions. Finally, we present the main ideas of an algorithm which allows us to perform image reconstruction on real nonacademic images. A very interesting point is that this algorithm is massively parallelizable and needs no information on where the image is corrupted.

Suppressive Mechanisms in Monkey V1 Help to Solve the Stereo Correspondence Problem

Neurons encode the depth in stereoscopic images by combining the signals from the receptive fields in the two eyes. Local variations in single images can activate neurons that do not signal the correct disparity (false matches), giving rise to the stereo correspondence problem. We used binocular white-noise stimuli to decompose the responses of monkey primary visual cortex V1 neurons into the elements of a linear-nonlinear model (via spike-triggered covariance analysis). In our population of disparity-selective neurons, we find both excitatory and suppressive elements in many of the neurons. Their binocular receptive fields were aligned in a specific push-pull manner for disparity. We demonstrate that this arrangement reduces the responses to false matches but preserves the responses to true matches. The responses of the cells to the noise stimuli were well explained by a linear summation of the elements, followed by a nonlinearity. This model also explained the shape of independently measured disparity-tuning curves, although it overestimated the response magnitude. This study constitutes the first direct physiological evidence for the contribution of suppressive mechanisms to disparity selectivity. This new mechanism contributes to solving the stereo correspondence problem.

Do the Congenitally Blind Have a Stria of Gennari? First Intracortical Insights In Vivo

The primary visual cortex V1, when dissected, is characterized by an easily identifiable anatomical landmark: the stria of Gennari or Gennari stripe. However, the origin and function of the Gennari stripe is so far unknown. In order to shed some light on this question, we acquired 7-T magnetic resonance imaging (MRI) brain scans of congenitally blind (CB) people, who have never had visual experience. If the stria of Gennari requires visual input to develop or to maintain its homeostasis, such subjects should lack this structure. If it is reliably detectable in the CB, it must form and persist independently of visual sensation. This question has never previously been explored in living subjects. For the first time, the use of 7-T high-resolution MRI enables such investigations because of the excellent signal-to-noise ratio at this magnetic field strength. For comparison, we scanned sighted subjects using the same experimental parameters. We detected the stria of Gennari reliably in both sighted and blind subjects, showing that this anatomical feature is not a developmental result of visual input, and it does not degenerate in the absence of visual input.

Visual Tuning Properties of Genetically Identified Layer 2/3 Neuronal Types in the Primary Visual Cortex of Cre-Transgenic Mice

The putative excitatory and inhibitory cell classes within the mouse primary visual cortex V1 have different functional properties as studied using recording microelectrode. Excitatory neurons show high selectivity for the orientation angle of moving gratings while the putative inhibitory neurons show poor selectivity. However, the study of selectivity of the genetically identified interneurons and their subtypes remain controversial. Here we use novel Cre-driver and reporter mice to identify genetic subpopulations in vivo for two-photon calcium dye imaging: Wfs1(+)/Gad1(−) mice that labels layer 2/3 excitatory cell population and Pvalb(+)/Gad1(+) mice that labels a genetic subpopulation of inhibitory neurons. The cells in both mice were identically labeled with a tdTomato protein, visible in vivo, using a Cre-reporter line. We found that the Wfs1(+) cells exhibited visual tuning properties comparable to the excitatory population, i.e., high selectivity and tuning to the angle, direction, and spatial frequency of oriented moving gratings. The functional tuning of Pvalb(+) neurons was consistent with previously reported narrow-spiking interneurons in microelectrode studies, exhibiting poorer selectivity than the excitatory neurons. This study demonstrates the utility of Cre-transgenic mouse technology in selective targeting of subpopulations of neurons and makes them amenable to structural, functional, and connectivity studies.

On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex.

In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices. All files used for the simulations are made available through the journal web site1.

Comparisons of the Dynamics of Local Field Potential and Multiunit Activity Signals in Macaque Visual Cortex

The local field potential (LFP) and multiunit activity (MUA) are extracellularly recorded signals that describe local neuronal network dynamics. In our experiments, the LFP and MUA, recorded from the same electrode in macaque primary visual cortex V1 in response to drifting grating visual stimuli, were evaluated on coarse timescales (∼1-5 s) and fine timescales (<0.1 s). On coarse timescales, MUA and the LFP both produced sustained visual responses to optimal and non-optimal oriented visual stimuli. The sustainedness of the two signals across the population of recording sites was correlated (correlation coefficient, ∼0.4). At most recording sites, the MUA was at least as sustained as the LFP and significantly more sustained for optimal orientations. In previous literature, the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging studies was found to be more strongly correlated with the LFP than with the MUA as a result of the lack of sustained response in the MUA signal. Because we found that MUA was as sustained as the LFP, MUA may also be correlated with BOLD. On fine timescales, we computed the coherence between the LFP and MUA over the frequency range 10-150 Hz. The LFP and MUA were weakly but significantly coherent (∼0.14) in the gamma band (20-90 Hz). The amount of gamma-band coherence was correlated with the power in the gamma band of the LFP. The data were consistent with the proposal that the LFP and MUA are generated in a noisy, resonant cortical network.

Image discontinuity changes LFP gamma-band activity in primary visual cortex V1

Image discontinuity changes LFP gamma-band activity in primary visual cortex V1

'Black' dominance measured with different stimulus ensembles in macaque primary visual cortex V1

'Black' dominance measured with different stimulus ensembles in macaque primary visual cortex V1

Korelacja zmian w funkcjonowaniu słuchowo-werbalnym pacjentów po wszczepieniu implantu ślimakowego z ich oceną w badaniu pozytonowej emisyjnej tomografii komputerowej (PET)

An assumption was taken that in central nervous system (CNS) in patients above 15 years of age there are possible mechanisms of neuronal changes. Those changes allow for reconstruction or formation of natural activation pattern of appropriate brain structures responsible for auditory speech processing. The aim of the study was to observe if there are any dynamic functional changes in central nervous system and their correlation to the auditory-verbal skills of the patients. Nine right-handed patients between 15 and 36 years of age were examined, 6 females and 3 males. All of them were treated with cochlear implantation and are in frequent follow-up in the Department of Otolaryngology at the Medical University of Warsaw due to profound sensorineural hearing loss. In present study the patients were examined within 24 hours after the first fitting of the speech processor of the cochlear implant, and 1 and 2 years subsequently. Combination of performed examinations consisted of: positone emission tomography of the brain, and audiological tests including speech assessment. In the group of patients 4 were postlingually deaf, and 5 were prelinqually deaf. Postlingually deaf patients achieved great improvement of hearing and speech understanding. In their first PET examination very intensive activation of visual cortex V1 and V2 (BA17 and 18) was observed. There was no significant activation in the dominant (left) hemisphere of the brain. In PET examination performed 1 and 2 years after the cochlear implantation no more V1 and V2 activation region was observed. Instead particular regions of the left hemisphere got activated. In prelingually deaf patients no significant changes in central nervous system were noticeable neither in PET nor in speech assessment, although their hearing possibilities improved. Positive correlation was observed between the level of speech understanding, linguistic skills and the activation of appropriate areas of the left hemisphere of the brain in postlingually deaf patients treated with cochlear implants. No such correlation was noted in prelingualy patients treated with the same method.

What are we missing here? Brain imaging evidence for higher cognitive functions in primary visual cortex V1

AbstractFunctional magnetic resonance imaging (fMRI) and neuron electrophysiology (neurophysiology) are two well‐established ways to measure brain activity. Even though the spatial and temporal resolution of these techniques is very different, both measurements show a high level of consistency, i.e., for mapping feature preferences of cortical areas. There are, however, other striking differences between fMRI and neurophysiology, for example, fMRI has good accessibility to higher cognitive functions, a bias to measure synaptic activity, and a good sensitivity to detect feedback‐related activity, all of which can shed a new light on the function of well‐known brain areas like primary visual cortex, V1. Classically, it is believed that V1 cells are exhaustively characterized by their complex receptive field properties. Contrary to this view, however, fMRI shows that response properties in area V1 are spatially unstable, influenced by contextual information, and depend on internal states. This review will highlight some of the most striking new fMRI findings that show V1 is involved in higher cognitive functions. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 131–139, 2010