scholarly journals Very Early Responses to Colour Stimuli Detected in Prestriate Visual Cortex by Magnetoencephalography (MEG)

2017 ◽  
Author(s):  
Yoshihito Shigihara ◽  
Hideyuki Hoshi ◽  
Semir Zeki
Keyword(s):  
Visual Cortex ◽  
Lateral Geniculate Nucleus ◽  
Visual Motion ◽  
Peak Time ◽  
List Type ◽  
Stimulus Interval ◽  
Lateral Geniculate ◽  
Prestriate Cortex ◽  
Colour Signals ◽  
Visual Evoked

AbstractPrevious studies with the visual motion and form systems show that visual stimuli belonging to these categories trigger much earlier latency responses from the visual cortex than previously supposed and that the source of the earliest signals can be located in either the prestriate cortex or in both the striate (V1) and prestriate cortex. This is consistent with the known anatomical connections since, in addition to the classical retino-geniculo-striate cortex input, there are direct anatomical inputs from both the lateral geniculate nucleus and the pulvinar that reach the prestriate visual cortex without passing through V1. In pursuing our studies, we thought it especially interesting to study another cardinal visual attribute, namely colour, to learn whether colour stimuli also provoke very early responses, at less than 50 ms, from visual cortex. To address the question, we asked participants to view stimuli that changed in colour and used magneto-encephalography to detect very early responses (< 50 ms) in the occipital visual cortex. Our results show that coloured stimuli also provoke an early cortical response (M30), with an average peak time at 30.8 ms, thus bringing the colour system into line with the visual motion and form systems. We conclude that colour signals reach visual cortex, including prestriate visual cortex, earlier than previously supposed.Key points summaryWe measured visual evoked responses to colour stimuli using magnetoencephalography.An early response was identified, at around 30 ms after stimulation.The sources of the response were estimated to be in prestriate cortex.Colour signals thus appear to evoke very early cortical responses, just like form and motion signals.Abbreviations listMEGmagnetoencephalography;LGNlateral geniculate nucleus;EEGelectroencephalography;ISIinter-stimulus interval;VEFvisual evoked magnetic field;SNRsignal to noise ratio;fTfemto Tesla;ANOVAanalysis of variance;MSPmultiple sparse priors;MRImagnetic resonance imaging;RMSroot-mean-square;SOIsensor of interest.

The projection from the lateral geniculate nucleus to the prestriate cortex of the macaque monkey

1981 ◽  
Vol 213 (1190) ◽  
pp. 73-80 ◽  
Keyword(s):  
Visual Cortex ◽  
Lateral Geniculate Nucleus ◽  
Macaque Monkey ◽  
Cell Distribution ◽  
Area 17 ◽  
Large Scatter ◽  
Large Size ◽  
Lateral Geniculate ◽  
Prestriate Cortex ◽  
The One

By injecting the enzyme horseradish peroxidase into the prestriate cortex of the macaque monkey and examining the lateral geniculate nucleus (l. g. n.) for retrograde label, the presence of a direct projection from the l. g. n. to prestriate visual cortex (Brodmann’s areas 18 and 19) was confirmed. Labelled cells occurred in all layers of the l. g. n., distributed in a roughly columnar fashion. The large scatter in cell distribution indicated a lower retinotopic precision for this projection than for the one to area 17. Labelled cells are of a medium to large size and, in each section, a few were located near the laminar border or in interlaminar zones. The functional significance of this projection is discussed.

scholarly journals Elastic Net Model of Ocular Dominance: Overall Stripe Pattern and Monocular Deprivation

1994 ◽  
Vol 6 (4) ◽  
pp. 615-621 ◽  
Author(s):  
Geoffrey J. Goodhill ◽  
David J. Willshaw
Keyword(s):  
Visual Cortex ◽  
Lateral Geniculate Nucleus ◽  
Primary Visual Cortex ◽  
Ocular Dominance ◽  
Monocular Deprivation ◽  
Elastic Net ◽  
Global Order ◽  
Stripe Pattern ◽  
Lateral Geniculate ◽  
Stripe Width

The elastic net (Durbin and Willshaw 1987) can account for the development of both topography and ocular dominance in the mapping from the lateral geniculate nucleus to primary visual cortex (Goodhill and Willshaw 1990). Here it is further shown for this model that (1) the overall pattern of stripes produced is strongly influenced by the shape of the cortex: in particular, stripes with a global order similar to that seen biologically can be produced under appropriate conditions, and (2) the observed changes in stripe width associated with monocular deprivation are reproduced in the model.

Oscillations and Long-Lasting Correlations in a Model of the Lateral Geniculate Nucleus and Visual Cortex

2000 ◽  
Vol 84 (4) ◽  
pp. 1863-1868 ◽  
Author(s):  
Kyle L. Kirkland ◽  
Adam M. Sillito ◽  
Helen E. Jones ◽  
David C. West ◽  
George L. Gerstein
Keyword(s):  
Experimental Data ◽  
Visual Cortex ◽  
Cell Membrane ◽  
Lateral Geniculate Nucleus ◽  
Feedback Inhibition ◽  
Frequency Range ◽  
Lateral Geniculate ◽  
Low Threshold ◽  
Thalamocortical System

We have previously developed a model of the corticogeniculate system to explore cortically induced synchronization of lateral geniculate nucleus (LGN) neurons. Our model was based on the experiments of Sillito et al. Recently Brody discovered that the LGN events found by Sillito et al. correlate over a much longer period of time than expected from the stimulus-driven responses and proposed a cortically induced slow covariation in LGN cell membrane potentials to account for this phenomenon. We have examined the data from our model, and we found, to our surprise, that the model shows the same long-term correlation. The model's behavior was the result of a previously unsuspected oscillatory effect, not a slow covariation. The oscillations were in the same frequency range as the well-known spindle oscillations of the thalamocortical system. In the model, the strength of feedback inhibition from the cortex and the presence of low-threshold calcium channels in LGN cells were important. We also found that by making the oscillations more pronounced, we could get a better fit to the experimental data.

Changes of visual evoked potential induced by lateral geniculate nucleus kindling in rats

2008 ◽  
Vol 79 (2-3) ◽  
pp. 146-150 ◽  
Author(s):  
Takashi Ishikawa ◽  
Akinori Fujiwara ◽  
Kenshi Takechi ◽  
Jun Ago ◽  
Naotaka Matsumoto ◽  
...  
Keyword(s):  
Lateral Geniculate Nucleus ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Lateral Geniculate ◽  
Visual Evoked

scholarly journals Contrast Sensitivity Is Enhanced by Expansive Nonlinear Processing in the Lateral Geniculate Nucleus

2008 ◽  
Vol 99 (1) ◽  
pp. 367-372 ◽  
Author(s):  
Thang Duong ◽  
Ralph D. Freeman
Keyword(s):  
Visual Cortex ◽  
Lateral Geniculate Nucleus ◽  
Visual Processing ◽  
Linear Prediction ◽  
Visual Pathway ◽  
Low Contrast ◽  
Response Data ◽  
Lateral Geniculate ◽  
Nonlinear Processing ◽  
Contrast Response

The firing rates of neurons in the central visual pathway vary with stimulus strength, but not necessarily in a linear manner. In the contrast domain, the neural response function for cells in the primary visual cortex is characterized by expansive and compressive nonlinearities at low and high contrasts, respectively. A compressive nonlinearity at high contrast is also found for early visual pathway neurons in the lateral geniculate nucleus (LGN). This mechanism affects processing in the visual cortex. A fundamentally related issue is the possibility of an expansive nonlinearity at low contrast in LGN. To examine this possibility, we have obtained contrast–response data for a population of LGN neurons. We find for most cells that the best-fit function requires an expansive component. Additionally, we have measured the responses of LGN neurons to m-sequence white noise and examined the static relationship between a linear prediction and actual spike rate. We find that this static relationship is well fit by an expansive nonlinear power law with average exponent of 1.58. These results demonstrate that neurons in early visual pathways exhibit expansive nonlinear responses at low contrasts. Although this thalamic expansive nonlinearity has been largely ignored in models of early visual processing, it may have important consequences because it potentially affects the interpretation of a variety of visual functions.

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