Masking reduces orientation selectivity in rat visual cortex

Alwis, Dasuni S.; Richards, Katrina; Price, Nicholas S. C.

doi:10.1152/jn.00366.2016

Cited by 14 publications

(22 citation statements)

References 57 publications

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“…The lack of effect of ISI on lapse rate also argues against a cognitive mechanism for the effects of ISI on behavior. Finally, the disproportional increase in threshold (and decrease in FA rates) with suppression of V1 is consistent with effects of ISI on behavior acting through effects on sensory coding, and not through more cognitive mechanisms like forward masking and attentional blink (Raymond et al, 1992;Macknik and Livingstone, 1998;Alwis et al, 2016). Notably, these phenomena also tend to act on much shorter time-scales (tens of milliseconds) than intervals used in this study, making them unlikely candidates to explain the effects of ISI on behavior.…”

Section: °4supporting

confidence: 80%

Neuronal Adaptation Reveals a Suboptimal Decoding of Orientation Tuned Populations in the Mouse Visual Cortex

2019

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Sensory information is encoded by populations of cortical neurons. Yet, it is unknown how this information is used for even simple perceptual choices such as discriminating orientation. To determine the computation underlying this perceptual choice, we took advantage of the robust visual adaptation in mouse primary visual cortex (V1). We first designed a stimulus paradigm in which we could vary the degree of neuronal adaptation measured in V1 during an orientation discrimination task. We then determined how adaptation affects task performance for mice of both sexes and tested which neuronal computations are most consistent with the behavioral results given the adapted population responses in V1. Despite increasing the reliability of the population representation of orientation among neurons, and improving the ability of a variety of optimal decoders to discriminate target from distractor orientations, adaptation increases animals' behavioral thresholds. Decoding the animals' choice from neuronal activity revealed that this unexpected effect on behavior could be explained by an overreliance of the perceptual choice circuit on target preferring neurons and a failure to appropriately discount the activity of neurons that prefer the distractor. Consistent with this all-positive computation, we find that animals' task performance is susceptible to subtle perturbations of distractor orientation and optogenetic suppression of neuronal activity in V1. This suggests that to solve this task the circuit has adopted a suboptimal and task-specific computation that discards important task-related information.

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Section: °4supporting

confidence: 80%

Neuronal Adaptation Reveals a Suboptimal Decoding of Orientation Tuned Populations in the Mouse Visual Cortex

2019

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“…In an electrophysiological investigation of visual masking in anaesthetized Long Evans rats, we showed that in primary visual cortex, neuronal responses to oriented circular gratings were altered by the presentation of a mask with analogous trends to those observed in other mammalian species (Alwis et al, ; Bridgeman, , ; Kovács et al, ; Rolls et al, ). Population decoding of these data allowed us to reliably predict orientation in a coarse discrimination task (e.g., horizontal vs. vertical), and decoding performance decreased when a mask was presented (Dell, Arabzadeh, & Price, ).…”

Section: Introductionsupporting

confidence: 62%

“…Visual masking therefore offers a powerful tool to investigate the neuronal correlates of perception. The neuronal mechanisms responsible for visual masking are unclear, but likely involve a complex interaction of mechanisms occurring throughout the retina, thalamus and cortex (Alwis, Richards, & Price, 2016;Fehmi, Adkins, & Lindsley, 1969;Levick & Zacks, 1970;Rolls, Tovee, & Panzeri, 1999).…”

Section: Introductionmentioning

confidence: 99%

Differences in perceptual masking between humans and rats

2019

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Introduction The perception of a target stimulus can be impaired by a subsequent mask stimulus, even if they do not overlap temporally or spatially. This “backward masking” is commonly used to modulate a subject's awareness of a target and to characterize the temporal dynamics of vision. Masking is most apparent with brief, low‐contrast targets, making detection difficult even in the absence of a mask. Although necessary to investigate the underlying neural mechanisms, evaluating masking phenomena in animal models is particularly challenging, as the task structure and critical stimulus features to be attended must be learned incrementally through rewards and feedback. Despite the increasing popularity of rodents in vision research, it is unclear if they are susceptible to masking illusions. Methods We characterized how spatially surrounding masks affected the detection of sine‐wave grating targets. Results In humans (n = 5) and rats (n = 7), target detection improved with contrast and was reduced by the presence of a mask. After controlling for biases to respond induced by the presence of the mask, a clear reduction in detectability was caused by masks. This reduction was evident when data were averaged across all animals, but was only individually significant in three animals. Conclusions While perceptual masking occurs in rats, it may be difficult to observe consistently in individual animals because the complexity of the requisite task pushes the limits of their behavioral capabilities. We suggest methods to ensure that masking, and similarly subtle effects, can be reliably characterized in future experiments.

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“…Moreover, the disproportional decrease in FA rates (and increase in threshold) with suppression 433 of V1 is consistent with effects of ISI on behavior acting through effects on sensory coding, and 434 not through more cognitive mechanisms like forward masking and attentional blink (Raymond et 435 al., 1992;Macknik and Livingstone, 1998;Alwis et al, 2016). Notably, these phenomena also 436 tend to act on much shorter time-scales (tens of milliseconds) than intervals used in this study, 437 making them unlikely candidates to explain the effects of ISI on behavior.…”

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confidence: 86%

Neuronal adaptation reveals a suboptimal decoding of orientation tuned populations in the mouse visual cortex

Jin

Beck

Glickfeld

2018

Preprint

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25 26 Sensory information is encoded by populations of cortical neurons. Yet, it is unknown how this 27 information is used for even simple perceptual choices such as discriminating orientation. To 28 determine the computation underlying this perceptual choice, we took advantage of the robust 29 adaptation in the mouse visual system. We find that adaptation increases animals' thresholds 30 for orientation discrimination. This was unexpected since optimal computations that take 31 advantage of all available sensory information predict that the shift in tuning and increase in 32 signal-to-noise ratio in the adapted condition should improve discrimination. Instead, we find 33 that the effects of adaptation on behavior can be explained by the appropriate reliance of the 34 perceptual choice circuits on target preferring neurons, but the failure to discount neurons that 35 prefer the distractor. This suggests that to solve this task the circuit has adopted a suboptimal 36 strategy that discards important task-related information to implement a feed-forward visual 37 computation. 38 Graf et al., 2011). This orientation identification strategy is attractive in that, once it is learned, 56 the same computation can be used to generalize across multiple tasks (i.e. detection and 57 discrimination). However, this computation likely requires complex circuits (with knowledge of 58 the full tuning curve of each neuron) and learning rules to act upon the combined output of a 59 diversely tuned population (Deneve et al., 1999). 60 61 Instead, when faced with perceptual choices, human and animal subjects often implement task-62 specific strategies that require less complex circuits and learning rules (Zhang et al., 2010; 63 Fulvio et al., 2014;Yu et al., 2017; Djurdjevic et al., 2018). Such a task-specific computation 64 4 might directly evaluate the identity or level of activity within distinct neuronal ensembles, 65 agnostic to the tuning of neurons in those ensembles, as in a linear classifier. By removing the 66 need for an absolute stimulus estimate, the circuits that compute perceptual choice may operate 67 faster and be more amenable to simple cellular associative learning rules. 68 69Our goal is to understand how these computational approaches are realized by biological 70 circuits, and how sensory information is integrated by these circuits to make perceptual 71 decisions. Stimulus-specific adaptation is a useful tool for evaluating how sensory information is 72 used to guide perceptual choice since it has predictable effects on neuronal activity and sensory 73 encoding (Müller et al., 1999; Dragoi et al., 2000). By sparsifying and increasing the signal-to-74 noise of neuronal population responses, stimulus-specific adaptation is expected to increase the 75 information about the presented orientation (Ulanovsky et al., 2003;Wark et al., 2007). In 76 addition, if the stimulus orientation is estimated, adaptation to the distractor is also expected to 77 improve orientation discrimination thresholds by decreasing the con...

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Masking reduces orientation selectivity in rat visual cortex

Cited by 14 publications

References 57 publications

Neuronal Adaptation Reveals a Suboptimal Decoding of Orientation Tuned Populations in the Mouse Visual Cortex

Neuronal Adaptation Reveals a Suboptimal Decoding of Orientation Tuned Populations in the Mouse Visual Cortex

Differences in perceptual masking between humans and rats

Neuronal adaptation reveals a suboptimal decoding of orientation tuned populations in the mouse visual cortex

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