scholarly journals Learning divisive normalization in primary visual cortex

2021 ◽  
Vol 17 (6) ◽  
pp. e1009028
Author(s):  
Max F. Burg ◽  
Santiago A. Cadena ◽  
George H. Denfield ◽  
Edgar Y. Walker ◽  
Andreas S. Tolias ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Experimental Studies ◽  
Response Properties ◽  
Feature Representations ◽  
Divisive Normalization ◽  
Direct Interpretation ◽  
Significant Step ◽  
The Brain

Divisive 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 neural 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 measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed.

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.

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 Homeostatic plasticity mechanisms in mouse V1

2017 ◽  
Vol 372 (1715) ◽  
pp. 20160504 ◽  
Author(s):  
Megumi Kaneko ◽  
Michael P. Stryker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Neuronal Activity ◽  
Dynamic Range ◽  
Ocular Dominance ◽  
Homeostatic Plasticity ◽  
Ocular Dominance Plasticity ◽  
The Brain

Mechanisms thought of as homeostatic must exist to maintain neuronal activity in the brain within the dynamic range in which neurons can signal. Several distinct mechanisms have been demonstrated experimentally. Three mechanisms that act to restore levels of activity in the primary visual cortex of mice after occlusion and restoration of vision in one eye, which give rise to the phenomenon of ocular dominance plasticity, are discussed. The existence of different mechanisms raises the issue of how these mechanisms operate together to converge on the same set points of activity. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

Receptive field expansion in adult visual cortex is linked to dynamic changes in strength of cortical connections

1995 ◽  
Vol 74 (2) ◽  
pp. 779-792 ◽  
Author(s):  
A. Das ◽  
C. D. Gilbert
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Visual Space ◽  
Final Analysis ◽  
Before And After ◽  
On Line ◽  
Adult Cats ◽  
Visual Cortex Neurons ◽  
Cortical Connection

1. Receptive field (RF) sizes of neurons in adult primary visual cortex are dynamic, expanding and contracting in response to alternate stimulation outside and within the RF over periods ranging from seconds to minutes. The substrate for this dynamic expansion was shown to lie in cortex, as opposed to subcortical parts of the visual pathway. The present study was designed to examine changes in cortical connection strengths that could underlie this observed plasticity by measuring the changes in cross-correlation histograms between pairs of primary visual cortex neurons that are induced to dynamically change their RF sizes. 2. Visually driven neural activity was recorded from single units in the superficial layers of primary visual cortex in adult cats, with two independent electrodes separated by 0.1–5 mm at their tips, and cross-correlated on-line. The neurons were then conditioned by stimulation with an “artificial scotoma,” a field of flashing random dots filling the region of visual space around a blank rectangle enclosing the RFs of the recorded neurons. The neuronal RFs were tested for expansion and their visually driven output again cross-correlated. After this, the neurons were stimulated vigorously through their RF centers to induce the field to collapse, and the visually driven output from the collapsed RFs was again cross-correlated. Cross-correlograms obtained before and after conditioning, and after RF collapse, were normalized by their flanks to control for changes in peak size due solely to fluctuations in spike rate. 3. A total of 37 pairs of neurons that showed distinct cross-correlogram peaks, and whose RF borders were clearly discernible both before and after conditioning, were used in the final analysis. Of these neuron pairs, conditioning led to a clear expansion of RF boundaries in 28 pairs, whereas in 9 pairs the RFs did not expand. RFs that did expand showed no significant shifts in their orientation preference, orientation selectivity, or ocularity. 4. When the RFs of a pair of neurons expanded with conditioning, the area of the associated flank-normalized cross-correlogram peaks also increased (by a factor ranging from 0.84 up to 3.5). Correlograms returned to their preconditioning values when RFs collapsed.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Blindsight and Unconscious Vision: What They Teach Us about the Human Visual System

2016 ◽  
Vol 23 (5) ◽  
pp. 529-541 ◽  
Author(s):  
Sara Ajina ◽  
Holly Bridge
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Neural Activity ◽  
Human Visual System ◽  
Visual Information ◽  
Visual Loss ◽  
Physiological Conditions ◽  
The World ◽  
The Brain

Damage to the primary visual cortex removes the major input from the eyes to the brain, causing significant visual loss as patients are unable to perceive the side of the world contralateral to the damage. Some patients, however, retain the ability to detect visual information within this blind region; this is known as blindsight. By studying the visual pathways that underlie this residual vision in patients, we can uncover additional aspects of the human visual system that likely contribute to normal visual function but cannot be revealed under physiological conditions. In this review, we discuss the residual abilities and neural activity that have been described in blindsight and the implications of these findings for understanding the intact system.

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