scholarly journals Selectivity and tolerance for visual texture in macaque V2

2016 ◽  
Vol 113 (22) ◽  
pp. E3140-E3149 ◽  
Author(s):  
Corey M. Ziemba ◽  
Jeremy Freeman ◽  
J. Anthony Movshon ◽  
Eero P. Simoncelli
Keyword(s):  
Texture Classification ◽  
Spatial Location ◽  
Image Features ◽  
Neuronal Responses ◽  
V1 Neurons ◽  
Visual Hierarchy ◽  
Sampling Process ◽  
Area V2 ◽  
Matched Samples ◽  
Image Transformations

As information propagates along the ventral visual hierarchy, neuronal responses become both more specific for particular image features and more tolerant of image transformations that preserve those features. Here, we present evidence that neurons in area V2 are selective for local statistics that occur in natural visual textures, and tolerant of manipulations that preserve these statistics. Texture stimuli were generated by sampling from a statistical model, with parameters chosen to match the parameters of a set of visually distinct natural texture images. Stimuli generated with the same statistics are perceptually similar to each other despite differences, arising from the sampling process, in the precise spatial location of features. We assessed the accuracy with which these textures could be classified based on the responses of V1 and V2 neurons recorded individually in anesthetized macaque monkeys. We also assessed the accuracy with which particular samples could be identified, relative to other statistically matched samples. For populations of up to 100 cells, V1 neurons supported better performance in the sample identification task, whereas V2 neurons exhibited better performance in texture classification. Relative to V1, the responses of V2 show greater selectivity and tolerance for the representation of texture statistics.

Neuronal responses to edges defined by luminance vs. temporal texture in macaque area V1

1997 ◽  
Vol 14 (5) ◽  
pp. 949-962 ◽  
Author(s):  
Avi Chaudhuri ◽  
Thomas D. Albright
Keyword(s):  
Luminance Contrast ◽  
Direction Selectivity ◽  
Image Features ◽  
Orientation Selectivity ◽  
Close Correspondence ◽  
Neuronal Responses ◽  
V1 Neurons ◽  
Texture Contrast ◽  
Direction Tuning ◽  
Made In

AbstractWe examined the responsivity, orientation selectivity, and direction selectivity of a sample of neurons in cortical area V1 of the macaque using visual stimuli consisting of drifting oriented contours defined by each of two very different figural cues: luminance contrast and temporal texture. Comparisons of orientation and direction tuning elicited by the different cues were made in order to test the hypothesis that the neuronal representations of these parameters are form-cue invariant. The majority of the sampled cells responded to both stimulus types, although responses to temporal texture stimuli were generally weaker than those elicited by luminance-defined stimuli. Of those units exhibiting orientation selectivity when tested with the luminance-defined stimuli, more than half were also selective for the orientation of the temporal texture stimuli. There was close correspondence between the preferred orientations and tuning bandwidths revealed with the two stimulus types. Of those units exhibiting directional selectivity when tested with the luminance-defined stimuli, about two-thirds were also selective for the direction of the temporal texture stimuli. There was close correspondence between the preferred directions revealed with the two stimulus types, although bidirectional responses were somewhat more common when temporal texture stimuli were used. These results indicate that many V1 neurons encode orientation and direction of motion of retinal image features in a manner that is largely independent of whether the feature is defined by luminance or temporal texture contrast. These neurons may contribute to perceptual phenomena in which figural cue identity is disregarded.

scholarly journals A direct interareal feedback-to-feedforward circuit in primate visual cortex

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Caitlin Siu ◽  
Justin Balsor ◽  
Sam Merlin ◽  
Frederick Federer ◽  
Alessandra Angelucci
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Projection Neurons ◽  
V1 Neurons ◽  
Cortical Areas ◽  
Area V2 ◽  
Computational Work ◽  
Primate Visual Cortex ◽  
Similar Area ◽  
Hierarchical Computation

AbstractThe mammalian sensory neocortex consists of hierarchically organized areas reciprocally connected via feedforward (FF) and feedback (FB) circuits. Several theories of hierarchical computation ascribe the bulk of the computational work of the cortex to looped FF-FB circuits between pairs of cortical areas. However, whether such corticocortical loops exist remains unclear. In higher mammals, individual FF-projection neurons send afferents almost exclusively to a single higher-level area. However, it is unclear whether FB-projection neurons show similar area-specificity, and whether they influence FF-projection neurons directly or indirectly. Using viral-mediated monosynaptic circuit tracing in macaque primary visual cortex (V1), we show that V1 neurons sending FF projections to area V2 receive monosynaptic FB inputs from V2, but not other V1-projecting areas. We also find monosynaptic FB-to-FB neuron contacts as a second motif of FB connectivity. Our results support the existence of FF-FB loops in primate cortex, and suggest that FB can rapidly and selectively influence the activity of incoming FF signals.

scholarly journals Neuronal Responses in Visual Area V2 (V2) of Macaque Monkeys with Strabismic Amblyopia

2011 ◽  
Vol 21 (9) ◽  
pp. 2033-2045 ◽  
Author(s):  
H. Bi ◽  
B. Zhang ◽  
X. Tao ◽  
R. S. Harwerth ◽  
E. L. Smith ◽  
...  
Keyword(s):  
Visual Area ◽  
Neuronal Responses ◽  
Strabismic Amblyopia ◽  
Macaque Monkeys ◽  
Area V2

scholarly journals Hyperspectral Imaging to Assess the Presence of Powdery Mildew (Erysiphe necator) in cv. Carignan Noir Grapevine Bunches

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 88 ◽  
Author(s):  
Claudia Pérez-Roncal ◽  
Ainara López-Maestresalas ◽  
Carlos Lopez-Molina ◽  
Carmen Jarén ◽  
Jorge Urrestarazu ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Hyperspectral Imaging ◽  
External Validation ◽  
Spatial Location ◽  
Image Features ◽  
Range Image ◽  
Spatial Image ◽  
Standard Normal Variate ◽  
Mean Centering ◽  
Standard Normal

Powdery mildew is a worldwide major fungal disease for grapevine, which adversely affects both crop yield and produce quality. Disease identification is based on visible signs of a pathogen once the plant has already been infected; therefore, techniques that allow objective diagnosis of the disease are currently needed. In this study, the potential of hyperspectral imaging (HSI) technology to assess the presence of powdery mildew in grapevine bunches was evaluated. Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range). Image processing was performed to extract spectral and spatial image features and then, classification models by means of Partial Least Squares Discriminant Analysis (PLS-DA) were carried out for healthy and infected pixels distinction within grape bunches. The best discrimination was achieved for the PLS-DA model with smoothing (SM), Standard Normal Variate (SNV) and mean centering (MC) pre-processing combination, reaching an accuracy of 85.33% in the cross-validation model and a satisfactory classification and spatial location of either healthy or infected pixels in the external validation. The obtained results suggested that HSI technology combined with chemometrics could be used for the detection of powdery mildew in black grapevine bunches.

scholarly journals Laminar dependence of neuronal correlations in visual cortex

2013 ◽  
Vol 109 (4) ◽  
pp. 940-947 ◽  
Author(s):  
Matthew A. Smith ◽  
Xiaoxuan Jia ◽  
Amin Zandvakili ◽  
Adam Kohn
Keyword(s):  
Visual Cortex ◽  
Field Potential ◽  
Low Frequency ◽  
Population Coding ◽  
Neuronal Responses ◽  
Cortical Function ◽  
Area V2 ◽  
Complementary Pattern ◽  
Deep Layers ◽  
Slow Timescale

Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30–50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.

scholarly journals Neuronal Responses to Texture-Defined Form in Macaque Visual Area V2

2011 ◽  
Vol 31 (23) ◽  
pp. 8543-8555 ◽  
Author(s):  
Y. El-Shamayleh ◽  
J. A. Movshon
Keyword(s):  
Visual Area ◽  
Neuronal Responses ◽  
Area V2

scholarly journals Transient inhibition to light explains stronger V1 responses to dark stimuli

2021 ◽  
Author(s):  
David St-Amand ◽  
Curtis L Baker
Keyword(s):  
Cortical Neurons ◽  
Natural Image ◽  
Simple Type ◽  
Neuronal Responses ◽  
Biologically Inspired ◽  
V1 Neurons ◽  
Novel Approach ◽  
Electrophysiological Responses ◽  
Light Stimuli ◽  
Insight Into

Neurons in the primary visual cortex (V1) receive excitation and inhibition from two different pathways processing lightness (ON) and darkness (OFF). V1 neurons overall respond more strongly to dark than light stimuli (Yeh, Xing and Shapley, 2010; Kremkow et al., 2014), consistent with a preponderance of darker regions in natural images (Ratliff et al., 2010), as well as human psychophysics (Buchner & Baumgartner, 2007). However, it has been unclear whether this "dark-dominance" is due to more excitation from the OFF pathway (Jin et al., 2008) or more inhibition from the ON pathway (Taylor et al., 2018). To understand the mechanisms behind dark-dominance, we record electrophysiological responses of individual simple-type V1 neurons to natural image stimuli and then train biologically inspired convolutional neural networks to predict the neuronal responses. Analyzing a sample of 74 neurons (in anesthetized, paralyzed cats) has revealed their responses to be more driven by dark than light stimuli, consistent with previous investigations (Yeh et al., 2010; Kremkow et al., 2013). We show this asymmetry to be predominantly due to slower inhibition to dark stimuli rather than by stronger excitation from the thalamocortical OFF pathway. Consistent with dark-dominant neurons having faster responses than light-dominant neurons (Komban et al., 2014), we find dark-dominance to solely occur in the early latencies of neuronal responses. Neurons that are strongly dark-dominated also tend to be less orientation selective. This novel approach gives us new insight into the dark-dominance phenomenon and provides an avenue to address new questions about excitatory and inhibitory integration in cortical neurons.

Response Facilitation From the “Suppressive” Receptive Field Surround of Macaque V1 Neurons

2007 ◽  
Vol 98 (4) ◽  
pp. 2168-2181 ◽  
Author(s):  
Jennifer M. Ichida ◽  
Lars Schwabe ◽  
Paul C. Bressloff ◽  
Alessandra Angelucci
Keyword(s):  
Receptive Field ◽  
Recurrent Network ◽  
Neuronal Responses ◽  
Top Down ◽  
Low Contrast ◽  
V1 Neurons ◽  
Feedback Model ◽  
Inhibitory Mechanisms ◽  
Stimulus Suppression ◽  
Feedback Connections

In primary visual cortex (V1), neuronal responses to optimally oriented stimuli in the receptive field (RF) center are usually suppressed by iso-oriented stimuli in the RF surround. The mechanisms and pathways giving rise to surround modulation, a possible neural correlate of perceptual figure-ground segregation, are not yet identified. We previously proposed that highly divergent and fast-conducting top-down feedback connections are the substrate for fast modulation arising from the more distant regions of the surround. We have recently implemented this idea into a recurrent network model ( Schwabe et al. 2006 ). The purpose of this study was to test a crucial prediction of this feedback model, namely that the suppressive “far” surround of V1 neurons can be facilitatory under conditions that weakly activate neurons in the RF center. Using single-unit recordings in macaque V1, we found iso-orientation far-surround facilitation when the RF center was driven by a low-contrast stimulus and the far surround by a small annular stimulus. Suppression occurred when the center stimulus contrast or the size of the surround stimulus was increased. This suggests that center-surround interactions result from excitatory and inhibitory mechanisms of similar spatial extent, and that changes in the balance of local excitation and inhibition, induced by surround stimulation, determine whether facilitation or suppression occurs. In layer 4C, the main target of geniculocortical afferents, lacking long-range intra-cortical connections, far-surround facilitation was rare and large surround fields were absent. This strongly suggests that feedforward connections do not contribute to far-surround modulation and that the latter is generated by intra-cortical mechanisms, likely involving top-down feedback.

Stochastic Nature of Precisely Timed Spike Patterns in Visual System Neuronal Responses

1999 ◽  
Vol 81 (6) ◽  
pp. 3021-3033 ◽  
Author(s):  
M. W. Oram ◽  
M. C. Wiener ◽  
R. Lestienne ◽  
B. J. Richmond
Keyword(s):  
Visual System ◽  
Statistical Models ◽  
Temporal Structure ◽  
Response Strength ◽  
Spike Count ◽  
Neural Responses ◽  
Neuronal Responses ◽  
V1 Neurons ◽  
Stochastic Nature ◽  
Spike Patterns

Stochastic nature of precisely timed spike patterns in visual system neuronal responses. It is not clear how information related to cognitive or psychological processes is carried by or represented in the responses of single neurons. One provocative proposal is that precisely timed spike patterns play a role in carrying such information. This would require that these spike patterns have the potential for carrying information that would not be available from other measures such as spike count or latency. We examined exactly timed (1-ms precision) triplets and quadruplets of spikes in the stimulus-elicited responses of lateral geniculate nucleus (LGN) and primary visual cortex (V1) neurons of the awake fixating rhesus monkey. Large numbers of these precisely timed spike patterns were found. Information theoretical analysis showed that the precisely timed spike patterns carried only information already available from spike count, suggesting that the number of precisely timed spike patterns was related to firing rate. We therefore examined statistical models relating precisely timed spike patterns to response strength. Previous statistical models use observed properties of neuronal responses such as the peristimulus time histogram, interspike interval, and/or spike count distributions to constrain the parameters of the model. We examined a new stochastic model, which unlike previous models included all three of these constraints and unlike previous models predicted the numbers and types of observed precisely timed spike patterns. This shows that the precise temporal structures of stimulus-elicited responses in LGN and V1 can occur by chance. We show that any deviation of the spike count distribution, no matter how small, from a Poisson distribution necessarily changes the number of precisely timed spike patterns expected in neural responses. Overall the results indicate that the fine temporal structure of responses can only be interpreted once all the coarse temporal statistics of neural responses have been taken into account.

scholarly journals Face Recognition Based on Histogram Equalization and LBP Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 33
Author(s):  
Herman Kh. Omar ◽  
Nada E. Tawfiq
Keyword(s):  
Face Recognition ◽  
Texture Classification ◽  
Histogram Equalization ◽  
Recognition System ◽  
Input Image ◽  
Image Features ◽  
Face Image ◽  
Wide Field ◽  
Face Recognition System ◽  
The Face

In the recent time bioinformatics take wide field in image processing. Face recognition which is basically the task of recognizing a person based on its facial image. It has become very popular in the last two decades, mainly because of the new methods developed and the high quality of the current visual instruments. There are different types of face recognition algorithms, and each method has a different approach to extract the image features and perform the matching with the input image. In this paper the Local Binary Patterns (LBP) was used, which is a particular case of the Texture Spectrum model, and powerful feature for texture classification. The face recognition system consists of recognizing the faces acquisition from a given data base via two phases. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database. The proposed algorithm for face recognition in this paper adopt the LBP features encode local texture information with default values. Apply histogram equalization and Resize the image into 80x60, divide it to five blocks, then Save every LBP feature as a vector table. Matlab R2019a was used to build the face recognition system. The Results which obtained are accurate and they are 98.8% overall (500 face image).

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