Neurons in primary visual cortex represent distribution of luminance

Wang, Yong; Wang, Yi

doi:10.14814/phy2.12966

Cited by 4 publications

(3 citation statements)

References 91 publications

(127 reference statements)

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“…We used three different measurements of L50 (L50, L50b, and L50n) to make the data analysis as transparent as possible and verify the replicability of results across different measurements. The L50 describes the absolute stimulus luminance, which is important to report because cortical neurons respond to luminance temporal changes ( Mazade et al, 2019 ; Wang and Wang, 2016 ; Xing et al, 2014 ). The L50b and L50n describe the luminance contrast as the difference between stimulus and background luminance (L50b), and the difference divided by the luminance range (L50n).…”

Section: Resultsmentioning

confidence: 99%

Image luminance changes contrast sensitivity in visual cortex

et al. 2021

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SUMMARY Accurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.

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Section: Resultsmentioning

confidence: 99%

Image luminance changes contrast sensitivity in visual cortex

et al. 2021

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“…Researches during last few years have shown that in several areas of visual system, information processing involves dynamical and nonlinear processes as seen in retinal ganglion cells [109, 110], retina [111], lateral geniculate nucleus [104] and visual cortex [112]. In addition, spatial integration of information in retinal ganglion cells [109, 110] and colored visual stimuli processing of primary visual cortex [113] also involve nonlinear dynamics.…”

Section: Discussionmentioning

confidence: 99%

Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction

et al. 2019

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Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.

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“…Using the reverse correlation technique in which small bright and dark bars are presented rapidly without interval in random positions (DeAngelis et al ., ), previous studies have shown that the rapid presentation of stimuli is useful in capturing the response properties of V1 neurons. This technique was widely used to map the spatiotemporal profile of the receptive field of V1 neurons (Jones & Palmer, ; DeAngelis et al ., ,b; Conway & Livingstone, ; Ringach, ) and to investigate the orientation and spatial frequency tunings (Ringach et al ., ; Mazer et al ., ; Ringach & Shapley, ; Nishimoto et al ., ) and the contrast and luminance tuning of V1 cells (Hu et al ., ; Li & Wang, ; Wang et al ., ; Wang & Wang, ). Hu et al .…”

Section: Discussionmentioning

confidence: 99%

Contrast coding in the primary visual cortex depends on temporal contexts

Dai

Wang

2018

Eur J of Neuroscience

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Contrast response function in the primary visual cortex (V1) has long been described as following a sigmoid curve. However, this is mainly based on measuring neural responses to drifting contrast grating in a stable stimulation, a model that does not consider the effects of motion or length of stimulus presentation. During natural viewing, the visual system can obtain sufficient information for identifying the shapes defined by contrast from a single glance; acquiring greater knowledge of the neuronal response properties to contrast in such a short timescale is necessary to understand the underlying mechanisms. We investigated responses of cat V1 neurons to contrast presented by static grating for 40 ms without pause compared to drifting grating presented continuously for 2000 ms. The neuronal response to transiently presented contrast could be well described by a linear function. Further examination of the effects of motion and presentation duration on contrast responses demonstrated that motion increased response sensitivity in the low-contrast range, while brief presentation increased response sensitivity in the high-contrast range. Motion and prolonged presentation (adaptation) together resulted in an asymptotic sigmoid curve with a saturation response in the high-contrast range. These results suggest that motion mainly enhance the neural response sensitivity to low-contrast objects, while short and rapid presentation mainly enhance the neural sensitivity to high-contrast stimulus. Our findings indicate that multiple factors influence the properties of contrast response functions, suggesting that V1 neuron contrast coding is flexible and depends on the temporal contexts.

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Neurons in primary visual cortex represent distribution of luminance

Cited by 4 publications

References 91 publications

Image luminance changes contrast sensitivity in visual cortex

Image luminance changes contrast sensitivity in visual cortex

Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction

Contrast coding in the primary visual cortex depends on temporal contexts

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