Altered functional interactions between neurons in primary visual cortex of macaque monkeys with experimental amblyopia

Amblyopia, a disorder in which vision through one of the eyes is degraded, arises because of defective processing of information by the visual system. Amblyopia often develops in humans after early misalignment of the eyes (strabismus) and can be simulated in macaque monkeys by artificially inducing strabismus. In such amblyopic animals, single-unit responses in primary visual cortex (V1) are appreciably reduced when evoked by the amblyopic eye compared with the other (fellow) eye. However, this degradation in single V1 neuron responsivity is not commensurate with the marked losses in visual sensitivity and resolution measured behaviorally. Here we explored the idea that changes in patterns of coordinated activity across populations of V1 neurons may contribute to degraded visual representations in amblyopia, potentially making it more difficult to read out evoked activity to support perceptual decisions. We studied the visually evoked activity of V1 neuronal populations in three macaques ( Macaca nemestrina) with strabismic amblyopia and in one control animal. Activity driven through the amblyopic eye was diminished, and these responses also showed more interneuronal correlation at all stimulus contrasts than responses driven through the fellow eye or responses in the control animal. A decoding analysis showed that responses driven through the amblyopic eye carried less visual information than other responses. Our results suggest that part of the reduced visual capacity of amblyopes may be due to changes in the patterns of functional interaction among neurons in V1. NEW & NOTEWORTHY Previous work on the neurophysiological basis of amblyopia has largely focused on relating behavioral deficits to changes in visual processing by single neurons in visual cortex. In this study, we recorded simultaneously from populations of primary visual cortical (V1) neurons in macaques with amblyopia. We found changes in the strength and pattern of shared response variability between neurons. These changes in neuronal interactions could impair the visual representations of V1 populations driven by the amblyopic eye.

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Altered functional interactions between neurons in primary visual cortex of macaque monkeys with experimental amblyopia

10.1101/604009 ◽

2019 ◽

Author(s):

Katerina Acar ◽

Lynne Kiorpes ◽

J. Anthony Movshon ◽

Matthew A. Smith

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Visual Processing ◽

Cortical Neurons ◽

Visual Sensitivity ◽

Visual Responses ◽

Macaque Monkeys ◽

Fellow Eye ◽

Neuronal Populations ◽

Evoked Activity

AbstractAmblyopia, a disorder in which vision through one of the eyes is degraded, arises because of defective processing of information by the visual system. Amblyopia often develops in humans after early misalignment of the eyes (strabismus), and can be simulated in macaque monkeys by artificially inducing strabismus. In such amblyopic animals, single-unit responses in primary visual cortex (V1) are appreciably reduced when evoked by the amblyopic eye compared to the other (fellow) eye. However, this degradation in single V1 neuron responsivity is not commensurate with the marked losses in visual sensitivity and resolution measured behaviorally. Here we explored the idea that changes in patterns of coordinated activity across populations of V1 neurons may contribute to degraded visual representations in amblyopia, potentially making it more difficult to read out evoked activity to support perceptual decisions. We studied the visually-evoked activity of V1 neuronal populations in three macaques (M. nemestrina) with strabismic amblyopia and in one control. Activity driven through the amblyopic eye was diminished, and these responses also showed more interneuronal correlation at all stimulus contrasts than responses driven through the fellow eye or responses in the control. A decoding analysis showed that responses driven through the amblyopic eye carried less visual information than other responses. Our results suggest that part of the reduced visual capacity of amblyopes may be due to changes in the patterns of functional interaction among neurons in V1.New and noteworthyAmblyopia is a developmental disorder of visual processing that reduces visual function and changes the visual responses of cortical neurons in macaque monkeys. The neuronal and behavioral changes are not always well correlated. We found that the interactions among neurons in the visual cortex of monkeys with amblyopia are also altered. These changes may contribute to amblyopic visual deficits by diminishing the amount of information relayed by neuronal populations driven by the amblyopic eye.

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Specific excitatory connectivity for feature integration in mouse primary visual cortex

10.1101/072595 ◽

2016 ◽

Author(s):

Dylan R Muir ◽

Patricia Molina-Luna ◽

Morgane M Roth ◽

Fritjof Helmchen ◽

Björn M Kampa

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Visual Processing ◽

Visual Stimuli ◽

Visual Representations ◽

Feature Binding ◽

Local Network ◽

Visual Responses ◽

Visual Properties ◽

The Brain

AbstractLocal excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a ‘like-to-like’ scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a ‘feature binding’ scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by a assuming ‘feature binding’ connectivity. Unlike under the ‘like-to-like’ scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1.Author summaryThe brain is a highly complex structure, with abundant connectivity between nearby neurons in the neocortex, the outermost and evolutionarily most recent part of the brain. Although the network architecture of the neocortex can appear disordered, connections between neurons seem to follow certain rules. These rules most likely determine how information flows through the neural circuits of the brain, but the relationship between particular connectivity rules and the function of the cortical network is not known. We built models of visual cortex in the mouse, assuming distinct rules for connectivity, and examined how the various rules changed the way the models responded to visual stimuli. We also recorded responses to visual stimuli of populations of neurons in anaesthetised mice, and compared these responses with our model predictions. We found that connections in neocortex probably follow a connectivity rule that groups together neurons that differ in simple visual properties, to build more complex representations of visual stimuli. This finding is surprising because primary visual cortex is assumed to support mainly simple visual representations. We show that including specific rules for non-random connectivity in cortical models, and precisely measuring those rules in cortical tissue, is essential to understanding how information is processed by the brain.

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Superior colliculus encodes visual saliency before the primary visual cortex

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1701003114 ◽

2017 ◽

Vol 114 (35) ◽

pp. 9451-9456 ◽

Cited By ~ 41

Author(s):

Brian J. White ◽

Janis Y. Kan ◽

Ron Levy ◽

Laurent Itti ◽

Douglas P. Munoz

Keyword(s):

Visual Cortex ◽

Superior Colliculus ◽

Primary Visual Cortex ◽

Visual Processing ◽

Visual Saliency ◽

Saliency Map ◽

Stimulus Onset ◽

Brain Areas ◽

V1 Neurons ◽

Processing Stream

Models of visual attention postulate the existence of a bottom-up saliency map that is formed early in the visual processing stream. Although studies have reported evidence of a saliency map in various cortical brain areas, determining the contribution of phylogenetically older pathways is crucial to understanding its origin. Here, we compared saliency coding from neurons in two early gateways into the visual system: the primary visual cortex (V1) and the evolutionarily older superior colliculus (SC). We found that, while the response latency to visual stimulus onset was earlier for V1 neurons than superior colliculus superficial visual-layer neurons (SCs), the saliency representation emerged earlier in SCs than in V1. Because the dominant input to the SCs arises from V1, these relative timings are consistent with the hypothesis that SCs neurons pool the inputs from multiple V1 neurons to form a feature-agnostic saliency map, which may then be relayed to other brain areas.

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Dynamic distortion of the orientation representational space after learning in the mouse primary visual cortex

10.1101/2021.03.25.437032 ◽

2021 ◽

Author(s):

Julien Corbo ◽

John P McClure ◽

Orhan Batuhan Erkat ◽

Pierre-Olivier Polack

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Visual Processing ◽

Sensory Integration ◽

Orientation Discrimination ◽

Stimulus Generalization ◽

Behavioral Adaptation ◽

V1 Neurons ◽

Orientation Space ◽

Representational Space

Learning is an essential cognitive mechanism that supports behavioral adaptation through neural processing adjustments. Learning was shown to modify sensory integration, yet the nature of those modifications and the computational advantages they confer remain unclear. By comparing the responses of primary visual cortex (V1) neurons evoked by oriented stimuli in naive mice and mice performing an orientation discrimination task, we found that the representations of rewarded and non-rewarded cues were sparser, more accurate and more stable in trained mice. This improved representation was associated with a distortion of the V1 orientation space such that stimuli close to the task cues were represented as the task stimuli themselves. This distortion was context-dependent, as it was absent in trained mice passively viewing the cues. Hence, visual processing in V1 was dynamically adapted to enhance the reliability of the representation of the learned cues and favor stimulus generalization in the task-relevant computational space.

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Model-based characterization of the selectivity of neurons in primary visual cortex

10.1101/2021.09.13.460153 ◽

2021 ◽

Author(s):

Felix Bartsch ◽

Bruce G Cumming ◽

Daniel A Butts

Keyword(s):

Visual Cortex ◽

Spatial Frequency ◽

Primary Visual Cortex ◽

Visual Processing ◽

Frequency Tuning ◽

Nonlinear Models ◽

Field Size ◽

V1 Neurons ◽

Model Based ◽

Spatial Frequencies

To understand the complexity of stimulus selectivity in primary visual cortex (V1), models constructed to match observed responses to complex time-varying stimuli, instead of to explain responses to simple parametric stimuli, are increasingly used. While such models often can more accurately reflect the computations performed by V1 neurons in more natural visual environments, they do not by themselves provide insight into established measures of V1 neural selectivity such as receptive field size, spatial frequency tuning and phase invariance. Here, we suggest a series of analyses that can be directly applied to encoding models to link complex encoding models to more interpretable aspects of stimulus selectivity, applied to nonlinear models of V1 neurons recorded in awake macaque in response to random bar stimuli. In linking model properties to more classical measurements, we demonstrate several novel aspects of V1 selectivity not available to simpler experimental measurements. For example, we find that individual spatiotemporal elements of the V1 models often have a smaller spatial scale than the overall neuron sensitivity, and that this results in non-trivial tuning to spatial frequencies. Additionally, our proposed measures of nonlinear integration suggest that more classical classifications of V1 neurons into simple versus complex cells are spatial-frequency dependent. In total, rather than obfuscate classical characterizations of V1 neurons, model-based characterizations offer a means to more fully understand their selectivity, and provide a means to link their classical tuning properties to their roles in more complex, natural, visual processing.

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Causal role for sleep-dependent reactivation of learning-activated sensory ensembles for fear memory consolidation

Nature Communications ◽

10.1038/s41467-021-21471-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Brittany C. Clawson ◽

Emily J. Pickup ◽

Amy Ensing ◽

Laura Geneseo ◽

James Shaver ◽

...

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Memory Consolidation ◽

Critical Role ◽

Visual Presentation ◽

Fear Memory ◽

Memory Recall ◽

Visual Cue ◽

V1 Neurons ◽

Targeted Recombination

AbstractLearning-activated engram neurons play a critical role in memory recall. An untested hypothesis is that these same neurons play an instructive role in offline memory consolidation. Here we show that a visually-cued fear memory is consolidated during post-conditioning sleep in mice. We then use TRAP (targeted recombination in active populations) to genetically label or optogenetically manipulate primary visual cortex (V1) neurons responsive to the visual cue. Following fear conditioning, mice respond to activation of this visual engram population in a manner similar to visual presentation of fear cues. Cue-responsive neurons are selectively reactivated in V1 during post-conditioning sleep. Mimicking visual engram reactivation optogenetically leads to increased representation of the visual cue in V1. Optogenetic inhibition of the engram population during post-conditioning sleep disrupts consolidation of fear memory. We conclude that selective sleep-associated reactivation of learning-activated sensory populations serves as a necessary instructive mechanism for memory consolidation.

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A direct interareal feedback-to-feedforward circuit in primate visual cortex

Nature Communications ◽

10.1038/s41467-021-24928-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Caitlin Siu ◽

Justin Balsor ◽

Sam Merlin ◽

Frederick Federer ◽

Alessandra Angelucci

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Projection Neurons ◽

V1 Neurons ◽

Cortical Areas ◽

Area V2 ◽

Computational Work ◽

Primate Visual Cortex ◽

Similar Area ◽

Hierarchical Computation

AbstractThe mammalian sensory neocortex consists of hierarchically organized areas reciprocally connected via feedforward (FF) and feedback (FB) circuits. Several theories of hierarchical computation ascribe the bulk of the computational work of the cortex to looped FF-FB circuits between pairs of cortical areas. However, whether such corticocortical loops exist remains unclear. In higher mammals, individual FF-projection neurons send afferents almost exclusively to a single higher-level area. However, it is unclear whether FB-projection neurons show similar area-specificity, and whether they influence FF-projection neurons directly or indirectly. Using viral-mediated monosynaptic circuit tracing in macaque primary visual cortex (V1), we show that V1 neurons sending FF projections to area V2 receive monosynaptic FB inputs from V2, but not other V1-projecting areas. We also find monosynaptic FB-to-FB neuron contacts as a second motif of FB connectivity. Our results support the existence of FF-FB loops in primate cortex, and suggest that FB can rapidly and selectively influence the activity of incoming FF signals.

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Differential contributions of magnocellular and parvocellular pathways to the contrast response of neurons in bush baby primary visual cortex (V1)

Visual Neuroscience ◽

10.1017/s095252380017107x ◽

2000 ◽

Vol 17 (1) ◽

pp. 71-76 ◽

Cited By ~ 22

Author(s):

JOHN D. ALLISON ◽

PETER MELZER ◽

YUCHUAN DING ◽

A.B. BONDS ◽

VIVIEN A. CASAGRANDE

Keyword(s):

Visual Cortex ◽

Primary Visual Cortex ◽

Response Amplitude ◽

Contrast Threshold ◽

Peak Response ◽

V1 Neurons ◽

Bush Baby ◽

High Stimulus ◽

Average Contrast ◽

Contrast Response

How neurons in the primary visual cortex (V1) of primates process parallel inputs from the magnocellular (M) and parvocellular (P) layers of the lateral geniculate nucleus (LGN) is not completely understood. To investigate whether signals from the two pathways are integrated in the cortex, we recorded contrast-response functions (CRFs) from 20 bush baby V1 neurons before, during, and after pharmacologically inactivating neural activity in either the contralateral LGN M or P layers. Inactivating the M layer reduced the responses of V1 neurons (n = 10) to all stimulus contrasts and significantly elevated (t = 8.15, P < 0.01) their average contrast threshold from 8.04 (± 4.1)% contrast to 22.46 (± 6.28)% contrast. M layer inactivation also significantly reduced (t = 4.06, P < 0.01) the average peak response amplitude. Inactivating the P layer did not elevate the average contrast threshold of V1 neurons (n = 10), but significantly reduced (t = 4.34, P < 0.01) their average peak response amplitude. These data demonstrate that input from the M pathway can account for the responses of V1 neurons to low stimulus contrasts and also contributes to responses to high stimulus contrasts. The P pathway appears to influence mainly the responses of V1 neurons to high stimulus contrasts. None of the cells in our sample, which included cells in all output layers of V1, appeared to receive input from only one pathway. These findings support the view that many V1 neurons integrate information about stimulus contrast carried by the LGN M and P pathways.

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