Abstract:Most single units recorded from macaque secondary visual cortex (V2) respond with higher firing rates to synthetic texture images containing "naturalistic" higher-order statistics than to spectrally matched "noise" images lacking these statistics. In contrast, few single units in V1 show this property. We explored how the strength and dynamics of response vary across the different layers of visual cortex by recording multiunit (defined as high-frequency power in the local field potential) and gamma-band activi… Show more
“…We thus conclude that populations of neurons in areas V2 and V4 are likely candidates for the locus of neural selectivity and invariance giving rise to the opposing effects of stimulus size. More generally, these results suggest that the particular summary statistics used here (correlations across the responses of differently tuned V1 neurons) are represented by populations of V2 and V4 neurons—an idea that is consistent with their computation from V1 afferents and supported by previous psychophysical 18 and physiological 27 – 32 , 43 studies.…”
Section: Resultssupporting
confidence: 86%
“…These statistics capture image features that are sufficient to account for the appearance of many visual textures 23 – 25 . In addition, they have been used as a basis for explaining visual crowding in the periphery 17 , 18 , 26 , and are particularly effective in selectively driving responses of V2 and V4 neurons 27 – 32 . Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Our experiments, and model, are built on the backbone of a particular statistical characterization for visual texture 23 . That model has proven useful: it is reasonably compact (~700 statistics), it is capable of capturing the appearance of a wide variety of different naturally occurring textures 23 , 24 , 49 , and it preserves image attributes that drive V2 and V4 neurons robustly and selectively 27 – 32 . Nevertheless, it was developed by hand, using a qualitative and somewhat ad hoc procedure for selecting the set of features, which are surely are not perfectly aligned with the information captured in any particular neuronal population.…”
Sensory processing necessitates discarding some information in service of preserving and reformatting more behaviorally relevant information. Sensory neurons seem to achieve this by responding selectively to particular combinations of features in their inputs, while averaging over or ignoring irrelevant combinations. Here, we expose the perceptual implications of this tradeoff between selectivity and invariance, using stimuli and tasks that explicitly reveal their opposing effects on discrimination performance. We generate texture stimuli with statistics derived from natural photographs, and ask observers to perform two different tasks: Discrimination between images drawn from families with different statistics, and discrimination between image samples with identical statistics. For both tasks, the performance of an ideal observer improves with stimulus size. In contrast, humans become better at family discrimination but worse at sample discrimination. We demonstrate through simulations that these behaviors arise naturally in an observer model that relies on a common set of physiologically plausible local statistical measurements for both tasks.
“…We thus conclude that populations of neurons in areas V2 and V4 are likely candidates for the locus of neural selectivity and invariance giving rise to the opposing effects of stimulus size. More generally, these results suggest that the particular summary statistics used here (correlations across the responses of differently tuned V1 neurons) are represented by populations of V2 and V4 neurons—an idea that is consistent with their computation from V1 afferents and supported by previous psychophysical 18 and physiological 27 – 32 , 43 studies.…”
Section: Resultssupporting
confidence: 86%
“…These statistics capture image features that are sufficient to account for the appearance of many visual textures 23 – 25 . In addition, they have been used as a basis for explaining visual crowding in the periphery 17 , 18 , 26 , and are particularly effective in selectively driving responses of V2 and V4 neurons 27 – 32 . Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Our experiments, and model, are built on the backbone of a particular statistical characterization for visual texture 23 . That model has proven useful: it is reasonably compact (~700 statistics), it is capable of capturing the appearance of a wide variety of different naturally occurring textures 23 , 24 , 49 , and it preserves image attributes that drive V2 and V4 neurons robustly and selectively 27 – 32 . Nevertheless, it was developed by hand, using a qualitative and somewhat ad hoc procedure for selecting the set of features, which are surely are not perfectly aligned with the information captured in any particular neuronal population.…”
Sensory processing necessitates discarding some information in service of preserving and reformatting more behaviorally relevant information. Sensory neurons seem to achieve this by responding selectively to particular combinations of features in their inputs, while averaging over or ignoring irrelevant combinations. Here, we expose the perceptual implications of this tradeoff between selectivity and invariance, using stimuli and tasks that explicitly reveal their opposing effects on discrimination performance. We generate texture stimuli with statistics derived from natural photographs, and ask observers to perform two different tasks: Discrimination between images drawn from families with different statistics, and discrimination between image samples with identical statistics. For both tasks, the performance of an ideal observer improves with stimulus size. In contrast, humans become better at family discrimination but worse at sample discrimination. We demonstrate through simulations that these behaviors arise naturally in an observer model that relies on a common set of physiologically plausible local statistical measurements for both tasks.
“…We also found significant differences in VEPs between PS-synthesized and phase-randomized textures. According to previous imaging ( Freeman et al, 2013 ) and electrophysiological ( Ziemba et al, 2019 ) studies, these differences could be related to differential neural processing in V1 and V2 for naturalistic textures.…”
The primate visual system analyzes statistical information in natural images and uses it for the immediate perception of scenes, objects, and surface materials. To investigate the dynamical encoding of image statistics in the human brain, we measured visual evoked potentials (VEPs) for 166 natural textures and their synthetic versions, and performed a reverse-correlation analysis of the VEPs and representative texture statistics of the image. The analysis revealed occipital VEP components strongly correlated with particular texture statistics. VEPs correlated with low-level statistics, such as subband SDs, emerged rapidly from 100 to 250 ms in a spatial frequency dependent manner. VEPs correlated with higher-order statistics, such as subband kurtosis and cross-band correlations, were observed at slightly later times. Moreover, these robust correlations enabled us to inversely estimate texture statistics from VEP signals via linear regression and to reconstruct texture images that appear similar to those synthesized with the original statistics. Additionally, we found significant differences in VEPs at 200–300 ms between some natural textures and their Portilla–Simoncelli (PS) synthesized versions, even though they shared almost identical texture statistics. This differential VEP was related to the perceptual “unnaturalness” of PS-synthesized textures. These results suggest that the visual cortex rapidly encodes image statistics hidden in natural textures specifically enough to predict the visual appearance of a texture, while it also represents high-level information beyond image statistics, and that electroencephalography can be used to decode these cortical signals.
“…Historically, the bulk of neurophysiological measurements of the visual properties of macaque V1 neurons have been carried out in successive extracellular recordings from individual neurons or small numbers of neurons using conventional single-electrodes (e.g. Hawken & Parker, 1984; Livingstone & Hubel, 1984; Ringach et al, 1997; Schiller et al, 1976) or low-channel count linear arrays (Hansen, Chelaru, & Dragoi, 2012; Nigam, Pojoga, & Dragoi, 2019; Ziemba et al, 2019). Typically, from such data, the distributions of those properties are studied in aggregated sets of recordings accumulated across multiple sessions.…”
Primary visual cortex (V1) has been the focus of extensive neurophysiological investigation, and its laminar organization provides a key exemplar of the functional logic of neocortical microcircuits. Using newly developed high-density, linear array probes, we measured visual responses from large populations of simultaneously recorded neurons distributed across layers of macaque V1. In single recordings, myriad differences in the functional properties of neuronal subpopulations could be observed. In particular, we found that although standard measurements of orientation selectivity yielded only minor differences between laminar compartments, decoding of stimulus orientation from layer 4C responses was superior to that of both superficial and deep layers within the same cortical column. The superior orientation discrimination within layer 4C was associated with greater response reliability of individual neurons rather than lower correlated activity with neuronal populations. The results demonstrate the utility of high-density electrophysiology in revealing the functional organization and network properties of neocortical microcircuits in single experiments.
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