scholarly journals Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

2021 ◽  
Vol 17 (10) ◽  
pp. e1009528
Author(s):  
Ziniu Wu ◽  
Harold Rockwell ◽  
Yimeng Zhang ◽  
Shiming Tang ◽  
Tai Sing Lee
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Gabor Filter ◽  
Neuronal Response ◽  
Projection Pursuit ◽  
Response Prediction ◽  
Sparse Code ◽  
Natural Scenes ◽  
V1 Neurons ◽  
Front End

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.

Range and Mechanism of Encoding of Horizontal Disparity in Macaque V1

2002 ◽  
Vol 87 (1) ◽  
pp. 209-221 ◽  
Author(s):  
S.J.D. Prince ◽  
B. G. Cumming ◽  
A. J. Parker
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Tuning Curve ◽  
Rate Of Change ◽  
Careful Examination ◽  
Phase Encoding ◽  
Tuning Curves ◽  
V1 Neurons ◽  
Gabor Functions ◽  
Spatial Frequencies

The responses of single cortical neurons were measured as a function of the binocular disparity of dynamic random dot stereograms for a large sample of neurons ( n = 787) from V1 of the awake macaque. From this sample, we selected 180 neurons whose tuning curves were strongly tuned for disparity, well sampled and well described by one-dimensional Gabor functions. The fitted parameters of the Gabor functions were used to resolve three outstanding issues in binocular stereopsis. First, we considered whether tuning curves can be meaningfully divided into discrete tuning types. Careful examination of the distributions of the Gabor parameters that determine tuning shape revealed no evidence for clustering. We conclude that a continuum of tuning types is present. Second, we investigated the mechanism of disparity encoding for V1 neurons. The shape of the disparity tuning function can be used to distinguish between position-encoding (in which disparity is encoded by an interocular shift in receptive field position) and phase-encoding (in which disparity is encoded by a difference in the receptive field profile in the 2 eyes). Both position and phase encoding were found to be common. This was confirmed by an independent assessment of disparity encoding based on the measurement of disparity sensitivity for sinusoidal luminance gratings of different spatial frequencies. The contributions of phase and position to disparity encoding were compared by estimating a population average of the rate of change in firing rate per degree of disparity. When this was calculated separately for the phase and position contributions, they were found to be closely similar. Third, we investigated the range of disparity tuning in V1 as a function of eccentricity in the parafoveal range. We find few cells which are selective for disparities greater than ±1 ° even at the largest eccentricity of ∼5 °. The preferred disparity was correlated with the spatial scale of the tuning curve, and for most units lay within a ±π radians phase limit. Such a size-disparity correlation is potentially useful for the solution of the correspondence problem.

Neuronal response to texture- and contrast-defined boundaries in early visual cortex

2007 ◽  
Vol 24 (1) ◽  
pp. 65-77 ◽  
Author(s):  
YUNING SONG ◽  
CURTIS L. BAKER
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Cortical Neurons ◽  
Visual Cues ◽  
Neuronal Response ◽  
Luminance Contrast ◽  
Movement Direction ◽  
Natural Scenes ◽  
Neural Responses ◽  
Early Visual Cortex

Natural scenes contain a variety of visual cues that facilitate boundary perception (e.g., luminance, contrast, and texture). Here we explore whether single neurons in early visual cortex can process both contrast and texture cues. We recorded neural responses in cat A18 to both illusory contours formed by abutting gratings (ICs, texture-defined) and contrast-modulated gratings (CMs, contrast-defined). We found that if a neuron responded to one of the two stimuli, it also responded to the other. These neurons signaled similar contour orientation, spatial frequency, and movement direction of the two stimuli. A given neuron also exhibited similar selectivity for spatial frequency of the fine, stationary grating components (carriers) of the stimuli. These results suggest that the cue-invariance of early cortical neurons extends to different kinds of texture or contrast cues, and might arise from a common nonlinear mechanism.

Magnitude of Perceived Change in Natural Images May Be Linearly Proportional to Differences in Neuronal Firing Rates

2010 ◽  
Vol 23 (4) ◽  
pp. 349-372 ◽  
Author(s):  
Pei-Ying Chua ◽  
Tom Troscianko ◽  
P. George Lovell ◽  
David Tolhurst ◽  
Mazviita Chirimuuta ◽  
...  
Keyword(s):  
Neuronal Response ◽  
Neuronal Firing ◽  
Natural Scenes ◽  
Action Potential Firing ◽  
Firing Rates ◽  
V1 Neurons ◽  
Perceived Change ◽  
Potential Firing ◽  
Neuronal Firing Rate ◽  
Image Pairs

AbstractWe are studying how people perceive naturalistic suprathreshold changes in the colour, size, shape or location of items in images of natural scenes, using magnitude estimation ratings to characterise the sizes of the perceived changes in coloured photographs. We have implemented a computational model that tries to explain observers' ratings of these naturalistic differences between image pairs. We model the action-potential firing rates of millions of neurons, having linear and non-linear summation behaviour closely modelled on real V1 neurons. The numerical parameters of the model's sigmoidal transducer function are set by optimising the same model to experiments on contrast discrimination (contrast 'dippers') on monochrome photographs of natural scenes. The model, optimised on a stimulus-intensity domain in an experiment reminiscent of the Weber–Fechner relation, then produces tolerable predictions of the ratings for most kinds of naturalistic image change. Importantly, rating rises roughly linearly with the model's numerical output, which represents differences in neuronal firing rate in response to the two images under comparison; this implies that rating is proportional to the neuronal response.

scholarly journals Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons

2002 ◽  
Vol 88 (5) ◽  
pp. 2530-2546 ◽  
Author(s):  
James R. Cavanaugh ◽  
Wyeth Bair ◽  
J. Anthony Movshon
Keyword(s):  
Receptive Field ◽  
Field Model ◽  
Neuronal Response ◽  
Field Size ◽  
Spatial Integration ◽  
Receptive Field Size ◽  
V1 Neurons ◽  
Field Center ◽  
Horizontal Connections ◽  
Receptive Field Center

Information is integrated across the visual field to transform local features into a global percept. We now know that V1 neurons provide more spatial integration than originally thought due to the existence of their nonclassical inhibitory surrounds. To understand spatial integration in the visual cortex, we have studied the nature and extent of center and surround influences on neuronal response. We used drifting sinusoidal gratings in circular and annular apertures to estimate the sizes of the receptive field's excitatory center and suppressive surround. We used combinations of stimuli inside and outside the receptive field to explore the nature of the surround influence on the receptive field center as a function of the relative and absolute contrast of stimuli in the two regions. We conclude that the interaction is best explained as a divisive modulation of response gain by signals from the surround. We then develop a receptive field model based on the ratio of signals from Gaussian-shaped center and surround mechanisms. We show that this model can account well for the variations in receptive field size with contrast that we and others have observed and for variations in size with the state of contrast adaptation. The model achieves this success by simple variations in the relative gain of the two component mechanisms of the receptive field. This model thus offers a parsimonious explanation of a variety of phenomena involving changes in apparent receptive field size and accounts for these phenomena purely in terms of two receptive field mechanisms that do not themselves change in size. We used the extent of the center mechanism in our model as an indicator of the spatial extent of the central excitatory portion of the receptive field. We compared the extent of the center to measurements of horizontal connections within V1 and determined that horizontal intracortical connections are well matched in extent to the receptive field center mechanism. Input to the suppressive surround may come in part from feedback signals from higher areas.

Two-dimensional receptive-field organization in striate cortical neurons of the cat

1994 ◽  
Vol 11 (4) ◽  
pp. 703-720 ◽  
Author(s):  
Ming Sun ◽  
A. B. Bonds
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Ocular Dominance ◽  
Receptive Fields ◽  
Field Size ◽  
Two Dimensional ◽  
Cortical Layers ◽  
Field Organization ◽  
Receptive Field Organization ◽  
Cortical Receptive Fields

AbstractThe two-dimensional organization of receptive fields (RFs) of 44 cells in the cat visual cortex and four cells from the cat LGN was measured by stimulation with either dots or bars of light. The light bars were presented in different positions and orientations centered on the RFs. The RFs found were arbitrarily divided into four general types: Punctate, resembling DOG filters (11%); those resembling Gabor filters (9%); elongate (36%); and multipeaked-type (44%). Elongate RFs, usually found in simple cells, could show more than one excitatory band or bifurcation of excitatory regions. Although regions inhibitory to a given stimulus transition (e.g. ON) often coincided with regions excitatory to the opposite transition (e.g. OFF), this was by no means the rule. Measurements were highly repeatable and stable over periods of at least 1 h. A comparison between measurements made with dots and with bars showed reasonable matches in about 40% of the cases. In general, bar-based measurements revealed larger RFs with more structure, especially with respect to inhibitory regions. Inactivation of lower cortical layers (V-VI) by local GABA injection was found to reduce sharpness of detail and to increase both receptive-field size and noise in upper layer cells, suggesting vertically organized RF mechanisms. Across the population, some cells bore close resemblance to theoretically proposed filters, while others had a complexity that was clearly not generalizable, to the extent that they seemed more suited to detection of specific structures. We would speculate that the broadly varying forms of cat cortical receptive fields result from developmental processes akin to those that form ocular-dominance columns, but on a smaller scale.

scholarly journals Behavioral Receptive Field for Ocular Following in Humans: Dynamics of Spatial Summation and Center-Surround Interactions

2006 ◽  
Vol 95 (6) ◽  
pp. 3712-3726 ◽  
Author(s):  
Frédéric V. Barthélemy ◽  
Ivo Vanzetta ◽  
Guillaume S. Masson
Keyword(s):  
Visual Field ◽  
Receptive Field ◽  
Visual Motion ◽  
Neuronal Response ◽  
Spatial Summation ◽  
Visuomotor Transformations ◽  
Ocular Following ◽  
Linear Integration ◽  
Response Onset ◽  
Linear Contrast

Visual neurons integrate information over a finite part of the visual field with high selectivity. This classical receptive field is modulated by peripheral inputs that play a role in both neuronal response normalization and contextual modulations. However, the consequences of these properties for visuomotor transformations are yet incompletely understood. To explore those, we recorded short-latency ocular following responses in humans to large center-only and center-surround stimuli. We found that eye movements are triggered by a mechanism that integrates motion over a restricted portion of the visual field, the size of which depends on stimulus contrast and increases as a function of time after response onset. We also found evidence for a strong nonisodirectional center-surround organization, responsible for normalizing the central, driving input so that motor responses are set to their most linear contrast dynamics. Such response normalization is delayed about 20 ms relative to tracking onset, gradually builds up over time, and is partly tuned for surround orientation/direction. These results outline the spatiotemporal organization of a behavioral receptive field, which might reflect a linear integration among subpopulations of cortical visual motion detectors.

scholarly journals Attention operates uniformly throughout the classical receptive field and the surround

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Bram-Ernst Verhoef ◽  
John HR Maunsell
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Spatial Summation ◽  
Field Structure ◽  
Neuronal Responses ◽  
Large Variability ◽  
Area V4 ◽  
Classical Receptive Field ◽  
Spatially Varying

Shifting attention among visual stimuli at different locations modulates neuronal responses in heterogeneous ways, depending on where those stimuli lie within the receptive fields of neurons. Yet how attention interacts with the receptive-field structure of cortical neurons remains unclear. We measured neuronal responses in area V4 while monkeys shifted their attention among stimuli placed in different locations within and around neuronal receptive fields. We found that attention interacts uniformly with the spatially-varying excitation and suppression associated with the receptive field. This interaction explained the large variability in attention modulation across neurons, and a non-additive relationship among stimulus selectivity, stimulus-induced suppression and attention modulation that has not been previously described. A spatially-tuned normalization model precisely accounted for all observed attention modulations and for the spatial summation properties of neurons. These results provide a unified account of spatial summation and attention-related modulation across both the classical receptive field and the surround.

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