scholarly journals Parallel fast and slow recurrent cortical processing mediates target and distractor selection in visual search

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Sarah E. Donohue ◽  
Mircea A. Schoenfeld ◽  
Jens-Max Hopf
Keyword(s):  
Visual Cortex ◽  
Visual Search ◽  
Electric Fields ◽  
Neural Correlates ◽  
Opposite Polarity ◽  
Exact Nature ◽  
Behavioral Performance ◽  
Distractor Processing ◽  
Reentrant Processing

AbstractVisual search has been commonly used to study the neural correlates of attentional allocation in space. Recent electrophysiological research has disentangled distractor processing from target processing, showing that these mechanisms appear to operate in parallel and show electric fields of opposite polarity. Nevertheless, the localization and exact nature of this activity is unknown. Here, using MEG in humans, we provide a spatiotemporal characterization of target and distractor processing in visual cortex. We demonstrate that source activity underlying target- and distractor-processing propagates in parallel as fast and slow sweep from higher to lower hierarchical levels in visual cortex. Importantly, the fast propagating target-related source activity bypasses intermediate levels to go directly to V1, and this V1 activity correlates with behavioral performance. These findings suggest that reentrant processing is important for both selection and attenuation of stimuli, and such processing operates in parallel feedback loops.

Higher-Order Visual Cortex Shows Stronger Neural Correlates of Visual and Multisensory Detection Behavior Compared to Primary Visual Cortex

2019 ◽  
Author(s):  
Guido Meijer ◽  
Pietro Marchesi ◽  
Jorge Mejias ◽  
Jorrit Montijn ◽  
Carien Lansink ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neural Correlates ◽  
Higher Order

scholarly journals Peculiarities of Neurostimulation by Intense Nanosecond Pulsed Electric Fields: How to Avoid Firing in Peripheral Nerve Fibers

2021 ◽  
Vol 22 (13) ◽  
pp. 7051
Author(s):  
Vitalii Kim ◽  
Emily Gudvangen ◽  
Oleg Kondratiev ◽  
Luis Redondo ◽  
Shu Xiao ◽  
...  
Keyword(s):  
Side Effects ◽  
Electric Fields ◽  
Pulsed Electric Fields ◽  
Opposite Polarity ◽  
Nerve Fibers ◽  
High Rate ◽  
Excitation Threshold ◽  
Compound Action Potentials ◽  
Supramaximal Stimulation ◽  
The Impact

Intense pulsed electric fields (PEF) are a novel modality for the efficient and targeted ablation of tumors by electroporation. The major adverse side effects of PEF therapies are strong involuntary muscle contractions and pain. Nanosecond-range PEF (nsPEF) are less efficient at neurostimulation and can be employed to minimize such side effects. We quantified the impact of the electrode configuration, PEF strength (up to 20 kV/cm), repetition rate (up to 3 MHz), bi- and triphasic pulse shapes, and pulse duration (down to 10 ns) on eliciting compound action potentials (CAPs) in nerve fibers. The excitation thresholds for single unipolar but not bipolar stimuli followed the classic strength–duration dependence. The addition of the opposite polarity phase for nsPEF increased the excitation threshold, with symmetrical bipolar nsPEF being the least efficient. Stimulation by nsPEF bursts decreased the excitation threshold as a power function above a critical duty cycle of 0.1%. The threshold reduction was much weaker for symmetrical bipolar nsPEF. Supramaximal stimulation by high-rate nsPEF bursts elicited only a single CAP as long as the burst duration did not exceed the nerve refractory period. Such brief bursts of bipolar nsPEF could be the best choice to minimize neuromuscular stimulation in ablation therapies.

scholarly journals Refractive index sensing and surface-enhanced Raman spectroscopy using silver–gold layered bimetallic plasmonic crystals

2017 ◽  
Vol 8 ◽  
pp. 2492-2503 ◽  
Author(s):  
Somi Kang ◽  
Sean E Lehman ◽  
Matthew V Schulmerich ◽  
An-Phong Le ◽  
Tae-woo Lee ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Refractive Index ◽  
Electric Fields ◽  
Surface Enhanced Raman Spectroscopy ◽  
Plasmonic Crystals ◽  
Metal Thickness ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Difference Time

Herein we describe the fabrication and characterization of Ag and Au bimetallic plasmonic crystals as a system that exhibits improved capabilities for quantitative, bulk refractive index (RI) sensing and surface-enhanced Raman spectroscopy (SERS) as compared to monometallic plasmonic crystals of similar form. The sensing optics, which are bimetallic plasmonic crystals consisting of sequential nanoscale layers of Ag coated by Au, are chemically stable and useful for quantitative, multispectral, refractive index and spectroscopic chemical sensing. Compared to previously reported homometallic devices, the results presented herein illustrate improvements in performance that stem from the distinctive plasmonic features and strong localized electric fields produced by the Ag and Au layers, which are optimized in terms of metal thickness and geometric features. Finite-difference time-domain (FDTD) simulations theoretically verify the nature of the multimode plasmonic resonances generated by the devices and allow for a better understanding of the enhancements in multispectral refractive index and SERS-based sensing. Taken together, these results demonstrate a robust and potentially useful new platform for chemical/spectroscopic sensing.

scholarly journals Visual search for object categories is predicted by the representational architecture of high-level visual cortex

2017 ◽  
Vol 117 (1) ◽  
pp. 388-402 ◽  
Author(s):  
Michael A. Cohen ◽  
George A. Alvarez ◽  
Ken Nakayama ◽  
Talia Konkle
Keyword(s):  
Visual Cortex ◽  
Visual Search ◽  
Visual System ◽  
Visual Processing ◽  
Search Task ◽  
Visual Search Task ◽  
Visual Object ◽  
Neural Responses ◽  
Object Categories ◽  
High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

scholarly journals Neural correlates of central versus peripheral target detection during complex visual search

2011 ◽  
Vol 11 (11) ◽  
pp. 1294-1294
Author(s):  
S. Utz ◽  
G. W. Humphreys ◽  
J. P. McCleery
Keyword(s):  
Visual Search ◽  
Target Detection ◽  
Neural Correlates ◽  
Peripheral Target
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