scholarly journals Gain control from beyond the classical receptive field in primate primary visual cortex

2003 ◽  
Vol 20 (3) ◽  
pp. 221-230 ◽  
Author(s):  
BEN S. WEBB ◽  
CHRIS J. TINSLEY ◽  
NICK E. BARRACLOUGH ◽  
AMANDA PARKER ◽  
ANDREW M. DERRINGTON
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Gain Control ◽  
Salient Feature ◽  
Retinal Eccentricity ◽  
V1 Neurons ◽  
Contrast Gain ◽  
Classical Receptive Field ◽  
Extrastriate Areas

Gain control is a salient feature of information processing throughout the visual system. Heeger (1991, 1992) described a mechanism that could underpin gain control in primary visual cortex (V1). According to this model, a neuron's response is normalized by dividing its output by the sum of a population of neurons, which are selective for orientations covering a broad range. Gain control in this scheme is manifested as a change in the semisaturation constant (contrast gain) of a V1 neuron. Here we examine how flanking and annular gratings of the same or orthogonal orientation to that preferred by a neuron presented beyond the receptive field modulate gain in V1 neurons in anesthetized marmosets (Callithrix jacchus). To characterize how gain was modulated by surround stimuli, the Michaelis–Menten equation was fitted to response versus contrast functions obtained under each stimulus condition. The modulation of gain by surround stimuli was modelled best as a divisive reduction in response gain. Response gain varied with the orientation of surround stimuli, but was reduced most when the orientation of a large annular grating beyond the classical receptive field matched the preferred orientation of neurons. The strength of surround suppression did not vary significantly with retinal eccentricity or laminar distribution. In the marmoset, as in macaques (Angelucci et al., 2002a, b), gain control over the sort of distances reported here (up to 10 deg) may be mediated by feedback from extrastriate areas.

Neural Representation of the Luminance and Brightness of a Uniform Surface in the Macaque Primary Visual Cortex

2001 ◽  
Vol 86 (5) ◽  
pp. 2559-2570 ◽  
Author(s):  
Masaharu Kinoshita ◽  
Hidehiko Komatsu
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Neural Activity ◽  
Dynamic Range ◽  
Neural Representation ◽  
Homogeneous Surface ◽  
V1 Neurons ◽  
Classical Receptive Field ◽  
Multiple Unit

The perceived brightness of a surface is determined not only by the luminance of the surface (local information), but also by the luminance of its surround (global information). To better understand the neural representation of surface brightness, we investigated the effects of local and global luminance on the activity of neurons in the primary visual cortex (V1) of awake macaque monkeys. Single- and multiple-unit recordings were made from V1 while the monkeys were performing a visual fixation task. The classical receptive field of each neuron was identified as a region responding to a spot stimulus. Neural responses were assessed using homogeneous surfaces at least three times as large as the receptive field as stimuli. We first examined the sensitivity of neurons to variation in local surface luminance, while the luminance of the surround was held constant. The activity of a large majority of surface-responsive neurons (106/115) varied monotonically with changes in surface luminance; in some the dynamic range was over 3 log units. This monotonic relation between surface luminance and neural activity was more evident later in the stimulus period than early on. The effect of the global luminance on neural activity was then assessed in 81 of the surface-responsive neurons by varying the luminance of the surround while holding the luminance of the surface constant. The activity of one group of neurons (25/81) was unaffected by the luminance of the surround; these neurons appear to encode the physical luminance of a surface covering the receptive field. The responses of the other neurons were affected by the luminance of the surround. The effects of the luminances of the surface and the surround on the activities of 26 of these neurons were in the same direction (either increased or decreased), while the effects on the remaining 25 neurons were in opposite directions. The activities of the latter group of neurons seemed to parallel the perceived brightness of the surface, whereas the former seemed to encode the level of illumination. There were differences across different types of neurons with regard to the layer distribution. These findings indicate that global luminance information significantly modulates the activity of surface-responsive V1 neurons and that not only physical luminance, but also perceived brightness, of a homogeneous surface is represented in V1.

Dynamic Properties of Recurrent Inhibition in Primary Visual Cortex: Contrast and Orientation Dependence of Contextual Effects

2000 ◽  
Vol 83 (2) ◽  
pp. 1019-1030 ◽  
Author(s):  
Valentin Dragoi ◽  
Mriganka Sur
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Dynamic Properties ◽  
Orientation Dependence ◽  
Recurrent Inhibition ◽  
Inhibitory Neurons ◽  
Classical Receptive Field ◽  
Contextual Interactions ◽  
Receptive Field Center

A fundamental feature of neural circuitry in the primary visual cortex (V1) is the existence of recurrent excitatory connections between spiny neurons, recurrent inhibitory connections between smooth neurons, and local connections between excitatory and inhibitory neurons. We modeled the dynamic behavior of intermixed excitatory and inhibitory populations of cells in V1 that receive input from the classical receptive field (the receptive field center) through feedforward thalamocortical afferents, as well as input from outside the classical receptive field (the receptive field surround) via long-range intracortical connections. A counterintuitive result is that the response of oriented cells can be facilitated beyond optimal levels when the surround stimulus is cross-oriented with respect to the center and suppressed when the surround stimulus is iso-oriented. This effect is primarily due to changes in recurrent inhibition within a local circuit. Cross-oriented surround stimulation leads to a reduction of presynaptic inhibition and a supraoptimal response, whereas iso-oriented surround stimulation has the opposite effect. This mechanism is used to explain the orientation and contrast dependence of contextual interactions in primary visual cortex: responses to a center stimulus can be both strongly suppressed and supraoptimally facilitated as a function of surround orientation, and these effects diminish as stimulus contrast decreases.

scholarly journals Constrained inference in sparse coding reproduces contextual effects and predicts laminar neural dynamics

2019 ◽  
Author(s):  
Federica Capparelli ◽  
Klaus Pawelzik ◽  
Udo Ernst
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Sparse Coding ◽  
Contextual Effects ◽  
Direct Access ◽  
Contextual Processing ◽  
Classical Receptive Field ◽  
Non Linear ◽  
Visual Scenes

AbstractA central goal in visual neuroscience is to understand computational mechanisms and to identify neural structures responsible for integrating local visual features into global representations. When probed with complex stimuli that extend beyond their classical receptive field, neurons display non-linear behaviours indicative of such integration processes already in early stages of visual processing. Recently some progress has been made in explaining these effects from first principles by sparse coding models with a neurophysiologically realistic inference dynamics. They reproduce some of the complex response characteristics observed in primary visual cortex, but only when the context is located near the classical receptive field, since the connection scheme they propose include interactions only among neurons with overlapping input fields. Longer-range interactions required for addressing the plethora of contextual effects reaching beyond this range do not exist. Hence, a satisfactory explanation of contextual phenomena in terms of realistic interactions and dynamics in visual cortex is still missing. Here we propose an extended generative model for visual scenes that includes spatial dependencies among different features. We derive a neurophysiologically realistic inference scheme under the constraint that neurons have direct access to only local image information. The scheme can be interpreted as a network in primary visual cortex where two neural populations are organized in different layers within orientation hypercolumns that are connected by local, short-range and long-range recurrent interactions. When trained with natural images, the model predicts a connectivity structure linking neurons with similar orientation preferences matching the typical patterns found for long-ranging horizontal axons and feedback projections in visual cortex. Subjected to contextual stimuli typically used in empirical studies our model replicates several hallmark effects of contextual processing and predicts characteristic differences for surround modulation between the two model populations. In summary, our model provides a novel framework for contextual processing in the visual system proposing a well-defined functional role for horizontal axons and feedback projections.Author summaryAn influential hypothesis about how the brain processes visual information posits that each given stimulus should be efficiently encoded using only a small number of cells. This idea led to the development of a class of models that provided a functional explanation for various response properties of visual neurons, including the non-linear modulations observed when localized stimuli are placed in a broader spatial context. However, it remains to be clarified through which anatomical structures and neural connectivities a network in the cortex could perform the computations that these models require. In this paper we propose a model for encoding spatially extended visual scenes. Imposing the constraint that neurons in visual cortex have direct access only to small portions of the visual field we derive a simple yet realistic neural population dynamics. Connectivities optimized for natural scenes conform with anatomical findings and the resulting model reproduces a broad set of physiological observations, while exposing the neural mechanisms relevant for spatio-temporal information integration.

The maximum range and timing of excitatory contextual modulation in monkey primary visual cortex

2006 ◽  
Vol 23 (5) ◽  
pp. 721-728 ◽  
Author(s):  
D.M. ALEXANDER ◽  
J.J. WRIGHT
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Contextual Information ◽  
Contextual Modulation ◽  
V1 Neurons ◽  
Empirical Survey ◽  
Maximum Range ◽  
Different Types

Contextual modulations of receptive field properties by distal stimulus configurations have been shown for a variety of stimulus paradigms. A survey of excitatory contextual modulation data for V1 shows the maximum scale of interactions, measured in terms of distance in V1, to be between 10 mm and 30 mm. Different types of excitatory contextual modulation in V1 occur throughout the interval of 40–250 ms after stimulus delivery. This window provides opportunity for global propagation of visual contextual information to a subset of V1 neurons, via several routes within the visual system. We propose a number of experiments and analyses to confirm the results from this empirical survey.

scholarly journals Learning Divisive Normalization in Primary Visual Cortex

2019 ◽  
Author(s):  
Max F. Burg ◽  
Santiago A. Cadena ◽  
George H. Denfield ◽  
Edgar Y. Walker ◽  
Andreas S. Tolias ◽  
...  
Keyword(s):  
Neural Networks ◽  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Building Block ◽  
Natural Images ◽  
Natural Stimuli ◽  
Classical Receptive Field ◽  
Divisive Normalization ◽  
The Brain

AbstractDivisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by empirical data and applicable to arbitrary stimuli. Here, we developed an image-computable DN model and tested its ability to predict spiking responses of a large number of neurons to natural images. In macaque primary visual cortex (V1), we found that our model outperformed linear-nonlinear and wavelet-based feature representations and performed on par with state-of-the-art convolutional neural network models. Our model learns the pool of normalizing neurons and the magnitude of their contribution end-to-end from the data, answering a long-standing question about the tuning properties of DN: within the classical receptive field, oriented features were normalized preferentially by features with similar orientations rather than non-specifically as currently assumed. Overall, our work refines our view on gain control within the classical receptive field, quantifies the relevance of DN under stimulation with natural images and provides a new, high-performing, and compactly understandable model of V1.Author summaryDivisive normalization is a computational building block apparent throughout sensory processing in the brain. Numerous studies in the visual cortex have highlighted its importance by explaining nonlinear neural response properties to synthesized simple stimuli like overlapping gratings with varying contrasts. However, we do not know if and how this normalization mechanism plays a role when processing complex stimuli like natural images. Here, we applied modern machine learning methods to build a general divisive normalization model that is directly informed by data and quantifies the importance of divisive normalization. By learning the normalization mechanism from a data set of natural images and neural responses from macaque primary visual cortex, our model made predictions as accurately as current stat-of-the-art convolutional neural networks. Moreover, our model has fewer parameters and offers direct interpretations of them. Specifically, we found that neurons that respond strongly to a specific orientation are preferentially normalized by other neurons that are highly active for similar orientations. Overall, we propose a biologically motivated model of primary visual cortex that is compact, more interpretable, performs on par with standard convolutional neural networks and refines our view on how normalization operates in visual cortex when processing natural stimuli.

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

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.

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

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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