Neuronal population mechanisms of lightness perception

The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses. NEW & NOTEWORTHY A core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

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Neuronal population mechanisms of lightness perception

10.1101/294280 ◽

2018 ◽

Author(s):

Douglas A. Ruff ◽

David H. Brainard ◽

Marlene R. Cohen

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Neuronal Population ◽

Neuronal Responses ◽

Lightness Perception ◽

Neuronal Mechanism ◽

Area V4 ◽

Neuronal Populations ◽

Types Of Information ◽

The Way

AbstractThe way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria, but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses.New & NoteworthyA core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

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Faculty Opinions recommendation of Neuronal responses during and after the presentation of static visual stimuli in macaque primary visual cortex.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.5373957.5326054 ◽

2010 ◽

Author(s):

Marcello Rosa

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Visual Stimuli ◽

Neuronal Responses

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Neuronal Responses during and after the Presentation of Static Visual Stimuli in Macaque Primary Visual Cortex

Journal of Neuroscience ◽

10.1523/jneurosci.0815-10.2010 ◽

2010 ◽

Vol 30 (38) ◽

pp. 12619-12631 ◽

Cited By ~ 14

Author(s):

D. McLelland ◽

P. M. Baker ◽

B. Ahmed ◽

W. Bair

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Visual Stimuli ◽

Neuronal Responses

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Bottom-up saliency and top-down learning in the primary visual cortex of monkeys

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1803854115 ◽

2018 ◽

Vol 115 (41) ◽

pp. 10499-10504 ◽

Cited By ~ 14

Author(s):

Yin Yan ◽

Li Zhaoping ◽

Wu Li

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Detection Task ◽

Late Response ◽

Late Component ◽

Neuronal Responses ◽

Top Down ◽

Bottom Up ◽

Response Component ◽

Orientation Contrast

Early sensory cortex is better known for representing sensory inputs but less for the effect of its responses on behavior. Here we explore the behavioral correlates of neuronal responses in primary visual cortex (V1) in a task to detect a uniquely oriented bar—the orientation singleton—in a background of uniformly oriented bars. This singleton is salient or inconspicuous when the orientation contrast between the singleton and background bars is sufficiently large or small, respectively. Using implanted microelectrodes, we measured V1 activities while monkeys were trained to quickly saccade to the singleton. A neuron’s responses to the singleton within its receptive field had an early and a late component, both increased with the orientation contrast. The early component started from the outset of neuronal responses; it remained unchanged before and after training on the singleton detection. The late component started ∼40 ms after the early one; it emerged and evolved with practicing the detection task. Training increased the behavioral accuracy and speed of singleton detection and increased the amount of information in the late response component about a singleton’s presence or absence. Furthermore, for a given singleton, faster detection performance was associated with higher V1 responses; training increased this behavioral–neural correlate in the early V1 responses but decreased it in the late V1 responses. Therefore, V1’s early responses are directly linked with behavior and represent the bottom-up saliency signals. Learning strengthens this link, likely serving as the basis for making the detection task more reflexive and less top-down driven.

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Visual exposure optimizes stimulus encoding in primary visual cortex

10.1101/502328 ◽

2018 ◽

Cited By ~ 3

Author(s):

Andreea Lazar ◽

Chris Lewis ◽

Pascal Fries ◽

Wolf Singer ◽

Danko Nikolić

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Visual Processing ◽

Neuronal Response ◽

Extended Period ◽

Sensory Cells ◽

Neuronal Populations ◽

Visual Exposure ◽

Early Visual Cortex ◽

Sensory Cortices

SummarySensory exposure alters the response properties of individual neurons in primary sensory cortices. However, it remains unclear how these changes affect stimulus encoding by populations of sensory cells. Here, recording from populations of neurons in cat primary visual cortex, we demonstrate that visual exposure enhances stimulus encoding and discrimination. We find that repeated presentation of brief, high-contrast shapes results in a stereotyped, biphasic population response consisting of a short-latency transient, followed by a late and extended period of reverberatory activity. Visual exposure selectively improves the stimulus specificity of the reverberatory activity, by increasing the magnitude and decreasing the trial-to-trial variability of the neuronal response. Critically, this improved stimulus encoding is distributed across the population and depends on precise temporal coordination. Our findings provide evidence for the existence of an exposure-driven optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.

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Perceptual detection depends on spike count integration

10.1101/865410 ◽

2019 ◽

Author(s):

Jackson J. Cone ◽

Morgan L. Bade ◽

Nicolas Y. Masse ◽

Elizabeth A. Page ◽

David J. Freedman ◽

...

Keyword(s):

Neural Networks ◽

Cerebral Cortex ◽

Visual Cortex ◽

Recurrent Neural Networks ◽

Neural Circuit ◽

Visual Detection ◽

Spike Count ◽

Neuronal Responses ◽

Neuronal Populations ◽

And Behavior

AbstractWhenever the retinal image changes some neurons in visual cortex increase their rate of firing, while others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements were delivered to the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity was costly. This work clarifies neuronal decoding strategies employed by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.

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