scholarly journals A Unified Computational Model of Primary Visual Cortex: Consolidation of the Scattered Literature on Simple and Complex Cells

2014 ◽  
Vol 14 (10) ◽  
pp. 1189-1189
Author(s):  
T. Sawada ◽  
A. A. Petrov
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
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.

Influence of Lateral Connections on the Structure of Cortical Maps

2004 ◽  
Vol 92 (5) ◽  
pp. 2947-2959 ◽  
Author(s):  
Miguel Á. Carreira-Perpiñán ◽  
Geoffrey J. Goodhill
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Primary Visual Cortex ◽  
Short Range ◽  
Ocular Dominance ◽  
Characteristic Structure ◽  
Interaction Function ◽  
Mexican Hat ◽  
Visual Cortical ◽  
Cortical Map

Maps of ocular dominance and orientation in primary visual cortex have a highly characteristic structure. The factors that determine this structure are still largely unknown. In particular, it is unclear how short-range excitatory and inhibitory connections between nearby neurons influence structure both within and between maps. Using a generalized version of a well-known computational model of visual cortical map development, we show that the number of excitatory and inhibitory oscillations in this interaction function critically influences map structure. Specifically, we demonstrate that functions that oscillate more than once do not produce maps closely resembling those seen biologically. This strongly suggests that local lateral connections in visual cortex oscillate only once and have the form of a Mexican hat.

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 Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0130569 ◽  
Author(s):  
Na Shu ◽  
Zhiyong Gao ◽  
Xiangan Chen ◽  
Haihua Liu
Keyword(s):  
Visual Cortex ◽  
Visual Attention ◽  
Computational Model ◽  
Action Recognition ◽  
Primary Visual Cortex

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.

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