scholarly journals Probabilistic representation in human visual cortex reflects uncertainty in serial decisions

2019 ◽  
Author(s):  
R.S. van Bergen ◽  
J.F.M. Jehee
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Probability Distributions ◽  
Sensory Information ◽  
Population Level ◽  
Neural Representation ◽  
Serial Dependence ◽  
Population Activity ◽  
Human Visual Cortex ◽  
Sensory Uncertainty

AbstractHow does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution – a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception.

scholarly journals Orientation and color tuning of the human visual gamma rhythm

2021 ◽  
Author(s):  
Ye Li ◽  
William Bosking ◽  
Michael S Beauchamp ◽  
Sameer A Sheth ◽  
Daniel Yoshor ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Brain Activity ◽  
Population Level ◽  
Gamma Oscillations ◽  
Cell Orientation ◽  
Population Activity ◽  
Color Tuning ◽  
Human Visual Cortex ◽  
Gamma Rhythm

Narrowband gamma oscillations (NBG: ~20-60Hz) in visual cortex reflect rhythmic fluctuations in population activity generated by underlying circuits tuned for stimulus location, orientation, and color. Consequently, the amplitude and frequency of induced NBG activity is highly sensitive to these stimulus features. For example, in the non-human primate, NBG displays biases in orientation and color tuning at the population level. Such biases may relate to recent reports describing the large-scale organization of single-cell orientation and color tuning in visual cortex, thus providing a potential bridge between measurements made at different scales. Similar biases in NBG population tuning have been predicted to exist in the human visual cortex, but this has yet to be fully examined. Using intracranial recordings from human visual cortex, we investigated the tuning of NBG to orientation and color, both independently and in conjunction. NBG was shown to display a cardinal orientation bias (horizontal) and also an end- and mid-spectral color bias (red/blue and green). When jointly probed, the cardinal bias for orientation was attenuated and an end-spectral preference for red and blue predominated. These data both elaborate on the close, yet complex, link between the population dynamics driving NBG oscillations and known feature selectivity biases in visual cortex, adding to a growing set of stimulus dependencies associated with the genesis of NBG. Together, these two factors may provide a fruitful testing ground for examining multi-scale models of brain activity, and impose new constraints on the functional significance of the visual gamma rhythm.

scholarly journals Learning shapes cortical dynamics to enhance integration of relevant sensory input

2021 ◽  
Author(s):  
Angus Chadwick ◽  
Adil Khan ◽  
Jasper Poort ◽  
Antonin Blot ◽  
Sonja Hofer ◽  
...  
Keyword(s):  
Neural Coding ◽  
Sensory Input ◽  
Temporal Dynamics ◽  
Network Dynamics ◽  
Sensory Information ◽  
Neural Population ◽  
Cortical Circuits ◽  
Population Activity ◽  
Cortical Dynamics ◽  
Network Output

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.

scholarly journals Enhanced attentional gain as a mechanism for generalized perceptual learning in human visual cortex

2014 ◽  
Vol 112 (5) ◽  
pp. 1217-1227 ◽  
Author(s):  
Anna Byers ◽  
John T. Serences
Keyword(s):  
Visual Cortex ◽  
Perceptual Learning ◽  
Broad Class ◽  
Sensory Information ◽  
Large Set ◽  
Human Visual Cortex ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Response Profiles ◽  
Sensory Responses

Learning to better discriminate a specific visual feature (i.e., a specific orientation in a specific region of space) has been associated with plasticity in early visual areas ( sensory modulation) and with improvements in the transmission of sensory information from early visual areas to downstream sensorimotor and decision regions ( enhanced readout). However, in many real-world scenarios that require perceptual expertise, observers need to efficiently process numerous exemplars from a broad stimulus class as opposed to just a single stimulus feature. Some previous data suggest that perceptual learning leads to highly specific neural modulations that support the discrimination of specific trained features. However, the extent to which perceptual learning acts to improve the discriminability of a broad class of stimuli via the modulation of sensory responses in human visual cortex remains largely unknown. Here, we used functional MRI and a multivariate analysis method to reconstruct orientation-selective response profiles based on activation patterns in the early visual cortex before and after subjects learned to discriminate small offsets in a set of grating stimuli that were rendered in one of nine possible orientations. Behavioral performance improved across 10 training sessions, and there was a training-related increase in the amplitude of orientation-selective response profiles in V1, V2, and V3 when orientation was task relevant compared with when it was task irrelevant. These results suggest that generalized perceptual learning can lead to modified responses in the early visual cortex in a manner that is suitable for supporting improved discriminability of stimuli drawn from a large set of exemplars.

scholarly journals Dynamic and reversible remapping of network representations in an unchanging environment

2020 ◽  
Author(s):  
Isabel I. C. Low ◽  
Alex H. Williams ◽  
Malcolm G. Campbell ◽  
Scott W. Linderman ◽  
Lisa M. Giocomo
Keyword(s):  
Entorhinal Cortex ◽  
Neural Coding ◽  
Environmental Changes ◽  
Sensory Information ◽  
Population Level ◽  
Spatial Coding ◽  
Medial Entorhinal Cortex ◽  
Neural Representations ◽  
Cell Firing ◽  
Firing Properties

AbstractIn response to environmental changes, the medial entorhinal cortex alters its single-cell firing properties. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique contextual episodes. However, it is unknown how the entorhinal circuit transitions between different representations, particularly when sensory information is not delineated into discrete contexts. Here, we describe spontaneous and abrupt transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of medial entorhinal neurons and correlated with changes in running speed. While remapping altered spatial coding in individual neurons, we show that features of the environment were statistically preserved at the population-level, enabling simple decoding strategies. These findings provoke a reconsideration of how medial entorhinal cortex dynamically represents space and broadly suggest a remarkable capacity for higher-order cortical circuits to rapidly and substantially reorganize their neural representations.

Modulation of Sensory Suppression: Implications for Receptive Field Sizes in the Human Visual Cortex

2001 ◽  
Vol 86 (3) ◽  
pp. 1398-1411 ◽  
Author(s):  
Sabine Kastner ◽  
Peter De Weerd ◽  
Mark A. Pinsk ◽  
M. Idette Elizondo ◽  
Robert Desimone ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Receptive Field ◽  
Neural Representation ◽  
Lower Visual Field ◽  
Human Visual Cortex ◽  
Area V4 ◽  
Upper Visual Field ◽  
Neurophysiological Studies ◽  
Extrastriate Areas

Neurophysiological studies in monkeys show that when multiple visual stimuli appear simultaneously in the visual field, they are not processed independently, but rather interact in a mutually suppressive way. This suggests that multiple stimuli compete for neural representation. Consistent with this notion, we have previously found in humans that functional magnetic resonance imaging (fMRI) signals in V1 and ventral extrastriate areas V2, V4, and TEO are smaller for simultaneously presented (i.e., competing) stimuli than for the same stimuli presented sequentially (i.e., not competing). Here we report that suppressive interactions between stimuli are also present in dorsal extrastriate areas V3A and MT, and we compare these interactions to those in areas V1 through TEO. To exclude the possibility that the differences in responses to simultaneously and sequentially presented stimuli were due to differences in the number of transient onsets, we tested for suppressive interactions in area V4, in an experiment that held constant the number of transient onsets. We found that the fMRI response to a stimulus in the upper visual field was suppressed by the presence of nearby stimuli in the lower visual field. Further, we excluded the possibility that the greater fMRI responses to sequential compared with simultaneous presentations were due to exogeneous attentional cueing by having our subjects count T's or L's at fixation, an attentionally demanding task. Behavioral testing demonstrated that neither condition interfered with performance of the T/L task. Our previous findings suggested that suppressive interactions among nearby stimuli in areas V1 through TEO were scaled to the receptive field (RF) sizes of neurons in those areas. Here we tested this idea by parametrically varying the spatial separation among stimuli in the display. Display sizes ranged from 2 × 2° to 7 × 7° and were centered at 5.5° eccentricity. Based on the effects of display size on the magnitude of suppressive interactions, we estimated that RF sizes at an eccentricity of 5.5° were <2° in V1, 2–4° in V2, 4–6° in V4, larger than 7° (but still confined to a quadrant) in TEO, and larger than 6° (confined to a quadrant) in V3A. These estimates of RF sizes in human visual cortex are strikingly similar to those measured in physiological mapping studies in the homologous visual areas in monkeys.

scholarly journals Reconstructing representations of dynamic visual objects in early visual cortex

2015 ◽  
Vol 113 (5) ◽  
pp. 1453-1458 ◽  
Author(s):  
Edmund Chong ◽  
Ariana M. Familiar ◽  
Won Mok Shim
Keyword(s):  
Visual Cortex ◽  
Population Level ◽  
Neural Representation ◽  
Test Case ◽  
Missing Information ◽  
Sensory Data ◽  
Visual Objects ◽  
Retinal Input ◽  
Early Visual Cortex ◽  
Object Features

As raw sensory data are partial, our visual system extensively fills in missing details, creating enriched percepts based on incomplete bottom-up information. Despite evidence for internally generated representations at early stages of cortical processing, it is not known whether these representations include missing information of dynamically transforming objects. Long-range apparent motion (AM) provides a unique test case because objects in AM can undergo changes both in position and in features. Using fMRI and encoding methods, we found that the “intermediate” orientation of an apparently rotating grating, never presented in the retinal input but interpolated during AM, is reconstructed in population-level, feature-selective tuning responses in the region of early visual cortex (V1) that corresponds to the retinotopic location of the AM path. This neural representation is absent when AM inducers are presented simultaneously and when AM is visually imagined. Our results demonstrate dynamic filling-in in V1 for object features that are interpolated during kinetic transformations.

scholarly journals Representation of uncertainty in macaque visual cortex

2019 ◽  
Author(s):  
Olivier J. Hénaff ◽  
Zoe M. Boundy-Singer ◽  
Kristof Meding ◽  
Corey M. Ziemba ◽  
Robbe L. T. Goris
Keyword(s):  
Visual Cortex ◽  
Neural Circuits ◽  
Sensory Information ◽  
Gain Control ◽  
Response Strength ◽  
Control Mechanisms ◽  
Stimulus Uncertainty ◽  
Population Activity ◽  
Stimulus Features ◽  
Do So

Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a view of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test our theory, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is invariant to the source of uncertainty, and that it is specific to the features encoded by these neurons. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and derive how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity.

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