scholarly journals Development of Maps of Simple and Complex Cells in the Primary Visual Cortex

2011 ◽  
Vol 5 ◽  
Author(s):  
Ján Antolík ◽  
James A. Bednar
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Complex Cells ◽  
Simple And Complex Cells

Selective effects of aging on simple and complex cells in primary visual cortex of rhesus monkeys

2012 ◽  
Vol 1470 ◽  
pp. 17-23 ◽  
Author(s):  
Zhen Liang ◽  
Hongxin Li ◽  
Yun Yang ◽  
Guangxing Li ◽  
Yong Tang ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Rhesus Monkeys ◽  
Complex Cells ◽  
Simple And Complex Cells ◽  
Effects Of Aging ◽  
Selective Effects

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.

Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex

2012 ◽  
Vol 24 (5) ◽  
pp. 1271-1296 ◽  
Author(s):  
Michael Teichmann ◽  
Jan Wiltschut ◽  
Fred Hamker
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Hebbian Learning ◽  
Natural Images ◽  
Complex Cells ◽  
Cell Responses ◽  
Feature Detectors ◽  
Learning Rules ◽  
Static Images ◽  
Good Interpretation

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.

scholarly journals Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex

2020 ◽  
Author(s):  
Ali Almasi ◽  
Hamish Meffin ◽  
Shaun L. Cloherty ◽  
Yan Wong ◽  
Molis Yunzab ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Object Identification ◽  
Visual Object ◽  
Object Processing ◽  
Complex Cells ◽  
Feature Selectivity ◽  
Visual Object Identification ◽  
Neuronal Computation

AbstractVisual object identification requires both selectivity for specific visual features that are important to the object’s identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognised as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we show that the balance between selectivity and invariance in complex cells is more diverse than thought. Phase invariance is frequently partial, thus retaining sensitivity to brightness polarity, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.

scholarly journals Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex

2020 ◽  
Vol 30 (9) ◽  
pp. 5067-5087
Author(s):  
Ali Almasi ◽  
Hamish Meffin ◽  
Shaun L Cloherty ◽  
Yan Wong ◽  
Molis Yunzab ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Object Identification ◽  
Visual Object ◽  
Object Processing ◽  
Complex Cells ◽  
Feature Selectivity ◽  
Visual Object Identification ◽  
Neuronal Computation

Abstract Visual object identification requires both selectivity for specific visual features that are important to the object’s identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognized as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we quantitatively describe the balance between selectivity and invariance in complex cells. Phase invariance is frequently partial, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.

scholarly journals Influence of Contrast on Orientation and Temporal Frequency Tuning in Ferret Primary Visual Cortex

2004 ◽  
Vol 91 (6) ◽  
pp. 2797-2808 ◽  
Author(s):  
Henry J. Alitto ◽  
W. Martin Usrey
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Cortical Neurons ◽  
Frequency Tuning ◽  
Temporal Frequency ◽  
Orientation Tuning ◽  
Visual Response ◽  
Stimulus Contrast ◽  
Tuning Curves ◽  
Simple And Complex Cells

Neurons in primary visual cortex are highly sensitive to the contrast, orientation, and temporal frequency of a visual stimulus. These three stimulus properties can be varied independently of one another, raising the question of how they interact to influence neuronal responses. We recorded from individual neurons in ferret primary visual cortex to determine the influence of stimulus contrast on orientation tuning, temporal-frequency tuning, and latency to visual response. Results show that orientation-tuning bandwidth is not affected by contrast level. Thus neurons in ferret visual cortex display contrast-invariant orientation tuning. Stimulus contrast does, however, influence the structure of orientation-tuning curves as measures of circular variance vary inversely with contrast for both simple and complex cells. This change in circular variance depends, in part, on a contrast-dependent change in the ratio of null to preferred orientation responses. Stimulus contrast also has an influence on the temporal-frequency tuning of cortical neurons. Both simple and complex cells display a contrast-dependent rightward shift in their temporal frequency-tuning curves that results in an increase in the highest temporal frequency needed to produce a half-maximum response (TF50). Results show that the degree of the contrast-dependent increase in TF50 is similar for cortical neurons and neurons in the lateral geniculate nucleus (LGN) and indicate that subcortical mechanisms likely play a major role in establishing the degree of effect displayed by downstream neurons. Finally, results show that LGN and cortical neurons experience a contrast-dependent phase advance in their visual response. This phase advance is most pronounced for cortical neurons indicating a role for both subcortical and cortical mechanisms.

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