scholarly journals High precision coding in visual cortex

2019 ◽  
Author(s):  
Carsen Stringer ◽  
Michalis Michaelos ◽  
Marius Pachitariu
Keyword(s):  
Visual Cortex ◽  
Sensory Cortex ◽  
Visual Features ◽  
Single Neurons ◽  
Neural Noise ◽  
Learning Tasks ◽  
Visual Areas ◽  
Neural Populations ◽  
Behavioral States ◽  
Sequential Nature

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of perceptual learning tasks. Furthermore, higher-order visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

A Stereoscopic Look at Visual Cortex

2005 ◽  
Vol 93 (4) ◽  
pp. 1823-1826 ◽  
Author(s):  
Peter Neri
Keyword(s):  
Visual Cortex ◽  
Perceptual Experience ◽  
Binocular Disparity ◽  
Advanced Stage ◽  
Single Neurons ◽  
Previous Evidence ◽  
Mt Neurons ◽  
Visual Areas ◽  
V4 Neurons ◽  
New Framework

Three recent studies offer new insights into the way visual cortex handles binocular disparity signals. Two of these studies recorded from single neurons in two different visual areas of the monkey brain, one (V5/MT) in dorsal and one (V4) in ventral cortex. While V5/MT neurons respond similarly to neurons in primary visual cortex (V1), V4 neurons appear to reflect a more advanced stage in the analysis of retinal disparity, closer to the perceptual experience of stereoscopic depth. Both studies are consistent with a third study using fMRI to address similar questions in humans. Together with previous evidence, these results suggest a new framework for understanding stereoscopic processing based on the separation between ventral and dorsal streams in visual cortex.

Unconscious Processing of Unattended Features in Human Visual Cortex

2013 ◽  
Vol 25 (3) ◽  
pp. 329-337 ◽  
Author(s):  
Tatiana Aloi Emmanouil ◽  
Philip Burton ◽  
Tony Ro
Keyword(s):  
Visual Cortex ◽  
Visual Features ◽  
Multiple Features ◽  
Priming Effects ◽  
Unconscious Processing ◽  
Metacontrast Masking ◽  
Visual Areas ◽  
Human Participants ◽  
Task Irrelevant ◽  
And Task

Unconscious processing has been convincingly demonstrated for task-relevant feature dimensions. However, it is possible that the visual system is capable of more complex unconscious operations, extracting visual features even when they are unattended and task irrelevant. In the current study, we addressed this question by measuring unconscious priming using a task in which human participants attended to a target object's shape while ignoring its color. We measured both behavioral priming effects and priming-related fMRI activations from primes that were unconsciously presented using metacontrast masking. The results showed faster RTs and decreases in fMRI activation only when the primes were identical to the targets, indicating that primes were processed both in the attended shape and the unattended color dimensions. Reductions in activation were observed in early visual areas, including primary visual cortex, as well as in feature-responsive areas for shape and color. These results indicate that multiple features can be unconsciously encoded and possibly bound using the same visual networks activated by consciously perceived images.

scholarly journals Myelin densities in retinotopically defined dorsal visual areas of the macaque

2021 ◽  
Author(s):  
Xiaolian Li ◽  
Qi Zhu ◽  
Wim Vanduffel
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Cortical Thickness ◽  
New World Monkeys ◽  
Isotropic Resolution ◽  
Density Mapping ◽  
Area V2 ◽  
Visual Areas ◽  
Density Results

AbstractThe visuotopic organization of dorsal visual cortex rostral to area V2 in primates has been a longstanding source of controversy. Using sub-millimeter phase-encoded retinotopic fMRI mapping, we recently provided evidence for a surprisingly similar visuotopic organization in dorsal visual cortex of macaques compared to previously published maps in New world monkeys (Zhu and Vanduffel, Proc Natl Acad Sci USA 116:2306–2311, 2019). Although individual quadrant representations could be robustly delineated in that study, their grouping into hemifield representations remains a major challenge. Here, we combined in-vivo high-resolution myelin density mapping based on MR imaging (400 µm isotropic resolution) with fine-grained retinotopic fMRI to quantitatively compare myelin densities across retinotopically defined visual areas in macaques. Complementing previously documented differences in populational receptive-field (pRF) size and visual field signs, myelin densities of both quadrants of the dorsolateral posterior area (DLP) and area V3A are significantly different compared to dorsal and ventral area V3. Moreover, no differences in myelin density were observed between the two matching quadrants belonging to areas DLP, V3A, V1, V2 and V4, respectively. This was not the case, however, for the dorsal and ventral quadrants of area V3, which showed significant differences in MR-defined myelin densities, corroborating evidence of previous myelin staining studies. Interestingly, the pRF sizes and visual field signs of both quadrant representations in V3 are not different. Although myelin density correlates with curvature and anticorrelates with cortical thickness when measured across the entire cortex, exactly as in humans, the myelin density results in the visual areas cannot be explained by variability in cortical thickness and curvature between these areas. The present myelin density results largely support our previous model to group the two quadrants of DLP and V3A, rather than grouping DLP- with V3v into a single area VLP, or V3d with V3A+ into DM.

scholarly journals Top-down control of visual cortex by the frontal eye fields through oscillatory realignment

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Domenica Veniero ◽  
Joachim Gross ◽  
Stephanie Morand ◽  
Felix Duecker ◽  
Alexander T. Sack ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Transcranial Magnetic Stimulation ◽  
Visual Perception ◽  
Magnetic Stimulation ◽  
Single Pulse ◽  
Frontal Eye Fields ◽  
Phase Reset ◽  
Top Down ◽  
Visual Areas ◽  
Beta Frequency

AbstractVoluntary allocation of visual attention is controlled by top-down signals generated within the Frontal Eye Fields (FEFs) that can change the excitability of lower-level visual areas. However, the mechanism through which this control is achieved remains elusive. Here, we emulated the generation of an attentional signal using single-pulse transcranial magnetic stimulation to activate the FEFs and tracked its consequences over the visual cortex. First, we documented changes to brain oscillations using electroencephalography and found evidence for a phase reset over occipital sites at beta frequency. We then probed for perceptual consequences of this top-down triggered phase reset and assessed its anatomical specificity. We show that FEF activation leads to cyclic modulation of visual perception and extrastriate but not primary visual cortex excitability, again at beta frequency. We conclude that top-down signals originating in FEF causally shape visual cortex activity and perception through mechanisms of oscillatory realignment.

scholarly journals Adaptive Integration in the Visual Cortex by Depressing Recurrent Cortical Circuits

2008 ◽  
Vol 20 (7) ◽  
pp. 1847-1872 ◽  
Author(s):  
Mark C. W. van Rossum ◽  
Matthijs A. A. van der Meer ◽  
Dengke Xiao ◽  
Mike W. Oram
Keyword(s):  
Visual Cortex ◽  
Physiological Data ◽  
High Contrast ◽  
Cortical Circuits ◽  
Response Latencies ◽  
Low Contrast ◽  
Visual Areas ◽  
Higher Visual Areas ◽  
Recurrent Connections

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

scholarly journals Decoding the future from past experience: learning shapes predictions in early visual cortex

2015 ◽  
Vol 113 (9) ◽  
pp. 3159-3171 ◽  
Author(s):  
Caroline D. B. Luft ◽  
Alan Meeson ◽  
Andrew E. Welchman ◽  
Zoe Kourtzi
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sequence Structure ◽  
Neural Populations ◽  
Early Visual Cortex ◽  
Temporal Structures ◽  
Future Events ◽  
Sensory Predictions ◽  
Actual Stimulus ◽  
The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

scholarly journals Processing of the S-cone signals in the early visual cortex of primates

2013 ◽  
Vol 31 (2) ◽  
pp. 189-195 ◽  
Author(s):  
Youping Xiao
Keyword(s):  
Visual Cortex ◽  
Color Vision ◽  
Short Wavelength ◽  
Nonhuman Primates ◽  
Striate Cortex ◽  
Color Perception ◽  
Visual Pathway ◽  
Visual Features ◽  
Area V2 ◽  
Early Visual Cortex

AbstractThe short-wavelength-sensitive (S) cones play an important role in color vision of primates, and may also contribute to the coding of other visual features, such as luminance and motion. The color signals carried by the S cones and other cone types are largely separated in the subcortical visual pathway. Studies on nonhuman primates or humans have suggested that these signals are combined in the striate cortex (V1) following a substantial amplification of the S-cone signals in the same area. In addition to reviewing these studies, this review describes the circuitry in V1 that may underlie the processing of the S-cone signals and the dynamics of this processing. It also relates the interaction between various cone signals in V1 to the results of some psychophysical and physiological studies on color perception, which leads to a discussion of a previous model, in which color perception is produced by a multistage processing of the cone signals. Finally, I discuss the processing of the S-cone signals in the extrastriate area V2.

scholarly journals Unique spatial integration in mouse primary visual cortex and higher visual areas

2019 ◽  
Author(s):  
Kevin A. Murgas ◽  
Ashley M. Wilson ◽  
Valerie Michael ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Spatial Information ◽  
Spatial Scales ◽  
Spatial Integration ◽  
Global Features ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas

AbstractNeurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the area’s unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

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