Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque Primary Visual Cortex

2002 ◽  
Vol 88 (1) ◽  
pp. 455-463 ◽  
Author(s):  
Dario L. Ringach
Keyword(s):  
Visual Cortex ◽  
Spatial Structure ◽  
Spatial Frequency ◽  
Primary Visual Cortex ◽  
Image Coding ◽  
Population Analysis ◽  
Receptive Fields ◽  
Simple Cell ◽  
Field Function ◽  
Symmetry Classes

I present measurements of the spatial structure of simple-cell receptive fields in macaque primary visual cortex (area V1). Similar to previous findings in cat area 17, the spatial profile of simple-cell receptive fields in the macaque is well described by two-dimensional Gabor functions. A population analysis reveals that the distribution of spatial profiles in primary visual cortex lies approximately on a one-parameter family of filter shapes. Surprisingly, the receptive fields cluster into even- and odd-symmetry classes with a tendency for neurons that are well tuned in orientation and spatial frequency to have odd-symmetric receptive fields. The filter shapes predicted by two recent theories of simple-cell receptive field function, independent component analysis and sparse coding, are compared with the data. Both theories predict receptive fields with a larger number of subfields than observed in the experimental data. In addition, these theories do not generate receptive fields that are broadly tuned in orientation and low-pass in spatial frequency, which are commonly seen in monkey V1. The implications of these results for our understanding of image coding and representation in primary visual cortex are discussed.

The Joint Development of Orientation and Ocular Dominance: Role of Constraints

1997 ◽  
Vol 9 (5) ◽  
pp. 959-970 ◽  
Author(s):  
Christian Piepenbrock ◽  
Helge Ritter ◽  
Klaus Obermayer
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Ocular Dominance ◽  
Receptive Fields ◽  
Simple Cell ◽  
Orientation Preference ◽  
Joint Development ◽  
Independent Work ◽  
Insight Into

Correlation-based learning (CBL) has been suggested as the mechanism that underlies the development of simple-cell receptive fields in the primary visual cortex of cats, including orientation preference (OR) and ocular dominance (OD) (Linsker, 1986; Miller, Keller, & Stryker, 1989). CBL has been applied successfully to the development of OR and OD individually (Miller, Keller, & Stryker, 1989; Miller, 1994; Miyashita & Tanaka, 1991; Erwin, Obermayer, & Schulten, 1995), but the conditions for their joint development have not been studied (but see Erwin & Miller, 1995, for independent work on the same question) in contrast to competitive Hebbian models (Obermayer, Blasdel, & Schulten, 1992). In this article, we provide insight into why this has been the case: OR and OD decouple in symmetric CBL models, and a joint development of OR and OD is possible only in a parameter regime that depends on nonlinear mechanisms.

scholarly journals Orientation tuning depends on spatial frequency in mouse visual cortex

2016 ◽  
Author(s):  
Inbal Ayzenshtat ◽  
Jesse Jackson ◽  
Rafael Yuste
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Natural Scenes ◽  
Response Properties ◽  
Layer 2 ◽  
Mouse Visual Cortex

AbstractThe response properties of neurons to sensory stimuli have been used to identify their receptive fields and functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.Significance StatementIn this manuscript we demonstrate that the orientation selectivity of neurons in primary visual cortex of mouse is highly dependent on the stimulus SF. This dependence is realized quantitatively in a decrease in the selectivity strength of cells in non-optimum SF, and more importantly, it is also evident qualitatively in a shift in the preferred orientation of cells in non-optimum SF. We show that a receptive-field model of a 2D asymmetric Gabor, rather than a symmetric one, can explain this surprising observation. Therefore, we propose that the receptive fields of neurons in layer 2/3 of mouse visual cortex are spatially asymmetric and this asymmetry could be used effectively by the visual system to encode natural scenes.Highlights–Orientation selectivity is dependent on spatial frequency.–Asymmetric Gabor model can explain this dependence.

scholarly journals Replicating Receptive Fields of Simple and Complex Cells in Primary Visual Cortex in a Neuronal Network Model with Temporal and Population Sparseness and Reliability

2012 ◽  
Vol 24 (10) ◽  
pp. 2700-2725 ◽  
Author(s):  
Takuma Tanaka ◽  
Toshio Aoyagi ◽  
Takeshi Kaneko
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Learning Rule ◽  
Simple Cell ◽  
Complex Cells ◽  
Receptive Field Properties ◽  
Output Neurons ◽  
Simple And Complex Cells

We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

scholarly journals The spatial structure of feedforward information in mouse primary visual cortex

2019 ◽  
Author(s):  
Jun Zhuang ◽  
Rylan S Larsen ◽  
Kevin T Takasaki ◽  
Naveen D Ouellette ◽  
Tanya L Daigle ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Spatial Structure ◽  
Structural Properties ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Laminar Structure ◽  
Motion Direction ◽  
Multiple Modalities ◽  
New Model ◽  
Spatial Frequencies

Location-sensitive and motion-sensitive units are the two major functional types of feedforward projections from lateral genicular nucleus (LGN) to primary visual cortex (V1) in mouse. The distribution of these inputs in cortical depth remains under debate. By measuring the calcium activities of LGN axons in V1 of awake mice, we systematically mapped their functional and structural properties. Although both types distributed evenly across cortical depth, we found that they differ significantly across multiple modalities. Compared to the location-sensitive axons, which possessed confined spatial receptive fields, the motion-sensitive axons lacked spatial receptive fields, preferred lower temporal, higher spatial frequencies and had wider horizontal bouton spread. Furthermore, the motion-sensitive axons showed a strong depth-dependent motion direction bias while the location-sensitive axons showed a depth-independent OFF dominance. Overall, our results suggest a new model of receptive biases and laminar structure of thalamic inputs to V1.

scholarly journals Feedforward mechanisms of masking in mouse V1

2021 ◽  
Author(s):  
Dylan Barbera ◽  
Nicholas J. Priebe ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Spatial Structure ◽  
Primary Visual Cortex ◽  
Sensory Neurons ◽  
Spatial Configuration ◽  
Receptive Fields ◽  
Cortical Activity ◽  
Afferent Input ◽  
Orientation Tuning ◽  
V1 Neurons

AbstractSensory neurons not only encode stimuli that align with their receptive fields but are also modulated by context. For example, the responses of neurons in mouse primary visual cortex (V1) to gratings of their preferred orientation are modulated by the presence of superimposed orthogonal gratings (“plaids”). The effects of this modulation can be diverse: some neurons exhibit cross-orientation suppression while other neurons have larger responses to a plaid than its components. We investigated whether these diverse forms of masking could be explained by a unified circuit mechanism. We report that the suppression of cortical activity does not alter the effects of masking, ruling out cortical mechanisms. Instead, we demonstrate that the heterogeneity of plaid responses is explained by an interaction between stimulus geometry and orientation tuning. Highly selective neurons uniformly exhibit cross-orientation suppression, whereas in weakly-selective neurons masking depends on the spatial configuration of the stimulus, with effects transitioning systematically between suppression and facilitation. Thus, the diverse responses of mouse V1 neurons emerge as a consequence of the spatial structure of the afferent input to V1, with no need to invoke cortical interactions.

scholarly journals Human primary visual cortex (V1) is selective for second-order spatial frequency

2011 ◽  
Vol 105 (5) ◽  
pp. 2121-2131 ◽  
Author(s):  
Luke E. Hallum ◽  
Michael S. Landy ◽  
David J. Heeger
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Selective Adaptation ◽  
Second Order ◽  
Filter Model ◽  
First Order ◽  
Spatial Frequencies ◽  
Early Visual Cortex

A variety of cues can differentiate objects from their surrounds. These include “first-order” cues such as luminance modulations and “second-order” cues involving modulations of orientation and contrast. Human sensitivity to first-order modulations is well described by a computational model involving spatially localized filters that are selective for orientation and spatial frequency (SF). It is widely held that first-order modulations are represented by the firing rates of simple and complex cells (“first-order” neurons) in primary visual cortex (V1) that, likewise, have spatially localized receptive fields that are selective for orientation- and SF. Human sensitivity to second-order modulations is well described by a filter-rectify-filter (FRF) model, with first- and second-order filters selective for orientation and SF. However, little is known about how neuronal activity in visual cortex represents second-order modulations. We tested the FRF model by using an functional (f)MRI-adaptation protocol to characterize the selectivity of activity in visual cortex to second-order, orientation-defined gratings of two different SFs. fMRI responses throughout early visual cortex exhibited selective adaptation to these stimuli. The low-SF grating was a more effective adapter than the high-SF grating, incompatible with the FRF model. To explain the results, we extended the FRF model by incorporating normalization, yielding a filter-rectify-normalize-filter model, in which normalization enhances selectivity for second-order SF but only for low spatial frequencies. We conclude that neurons in human visual cortex are selective for second-order SF, that normalization (surround suppression) contributes to this selectivity, and that the selectivity in higher visual areas is simply fed forward from V1.

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