Suggestions(1)
Exact(3)
These networks should have ramifications in many visual areas that code different visual features such as motions, colors, shapes, and so forth.
Receptive fields in many visual areas have a complex substructure and consist not only of an excitatory receptive field center (the classical receptive field [cRF]) but often have a surrounding region (the non-cRF), where stimuli are thought not to drive the cell by themselves but modulate responses to a central stimulus.
Although the temporally modulated stimuli that many of these studies use are known to give strong visual evoked fields/potentials (Fylan et al., 1997; Hermann, 2001; Fawcett et al., 2004), we also know that such stimuli excite many visual areas (see the literature on retinotopic mapping, e.g. Engel et al., 1997; Sereno et al., 1995).
Similar(57)
By overlaying R1 and retinotopic maps, we found that many visual area borders were associated with significant R1 increases including V1, V3A, MT, V6, V6A, V8/VO1, FST, and VIP.
Nevertheless, without such projection, the pattern of activity in many visual cortical areas is dramatically modified.
When combined with relevant visual stimulation, electrical microstimulation in many visual cortical areas significantly and reliably affects behaviour in a variety of visual perceptual tasks (Cicmil and Krug, 2015).
DOI: http://dx.doi.org/10.7554/eLife.02813.002 While many visual brain areas contain representations of the current eye position, the nature and the role of this eye position signal (EPS) are still unclear.
However, at a lower threshold of P < 10−2.5, many mid-level visual areas (especially MT) showed a significant positive functional connection to a small common anterior/ventral portion of LIM.
This finding is remarkable because there are many neurons in early visual areas that are highly selective for stimulus orientation.
In addition to their tuning to shapes, many neurons in early visual areas are also tuned to other features, such as colors and movement directions (Leventhal, Thompson, Liu, Zhou, & Ault, 1995).
Although signatures of prediction errors have been observed in early visual areas, many of these studies have not reported prediction errors on every level of the hierarchy (Fang et al. 2008; den Ouden et al. 2009; Alink et al. 2010).
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com