Tangential segregation of simple and complex cells in the visual cortex and their connections. A universal neocortical module

2000 ◽  
Vol 30 (1) ◽  
pp. 89-96
Author(s):  
S. A. Chebkasov
Keyword(s):  
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

Intracortical connections are not required for oscillatory activity in the visual cortex

1997 ◽  
Vol 14 (6) ◽  
pp. 963R-979R ◽  
Author(s):  
Geoffrey M. Ghose ◽  
Ralph D. Freeman
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Close Agreement ◽  
Power Spectra ◽  
Spike Trains ◽  
Cortical Layer ◽  
Oscillatory Activity ◽  
Complex Cells ◽  
Firing Rates ◽  
Simple And Complex Cells

Abstractarises from the integration of signals from strongly oscillatory cells within the LGN. The model also predicts the incidence of 50-Hz oscillatory cells within the cortex. Oscillatory discharge around 30 Hz is explained in a second model by the presence of intrinsically oscillatory cells within cortical layer 5. Both models generate spike trains whose power spectra and mean firing rates are in close agreement with experimental observations of simple and complex cells. Considered together, the two models can largely account for the nature and incidence of oscillatory discharge in the cat's visual cortex. The validity of these models is consistent with the possibility that oscillations are generated independently of intracortical interactions. Because these models rely on intrinsic stimulus-independent oscillators within the retina and cortex, the results further suggest that oscillatory activity within the cortex is not necessarily associated with the processing of high-order visual information.

scholarly journals Retinal origin of simple and complex cells in visual cortex

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S281
Author(s):  
Gwangsu Kim ◽  
Jaeson Jang ◽  
Se-Bum Paik
Keyword(s):  
Visual Cortex ◽  
Complex Cells ◽  
Simple And Complex Cells

Intracortical connections are not required for oscillatory activity in the visual cortex

1997 ◽  
Vol 14 (5) ◽  
pp. 963-979 ◽  
Author(s):  
Geoffrey M. Ghose ◽  
Ralph D. Freeman
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Close Agreement ◽  
Power Spectra ◽  
Cortical Layer ◽  
Oscillatory Activity ◽  
Complex Cells ◽  
Firing Rates ◽  
Cortical Oscillations ◽  
Simple And Complex Cells

AbstractSynchronized oscillatory discharge in the visual cortex has been proposed to underlie the linking of retinotopically disparate features into perceptually coherent objects. These proposals have largely relied on the premise that the oscillations arise from intracortical circuitry. However, strong oscillations within both the retina and the lateral geniculate nucleus (LGN) have been reported recently. To evaluate the possibility that cortical oscillations arise from peripheral pathways, we have developed two plausible models of single cell oscillatory discharge that specifically exclude intracortical networks. In the first model, cortical oscillatory discharge near 50 Hz in frequency arises from the integration of signals from strongly oscillatory cells within the LGN. The model also predicts the incidence of 50-Hz oscillatory cells within the cortex. Oscillatory discharge around 30 Hz is explained in a second model by the presence of intrinsically oscillatory cells within cortical layer 5. Both models generate spike trains whose power spectra and mean firing rates are in close agreement with experimental observations of simple and complex cells. Considered together, the two models can largely account for the nature and incidence of oscillatory discharge in the cat's visual cortex. The validity of these models is consistent with the possibility that oscillations are generated independently of intracortical interactions. Because these models rely on intrinsic stimulus-independent oscillators within the retina and cortex, the results further suggest that oscillatory activity within the cortex is not necessarily associated with the processing of high-order visual information.

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 Simultaneous development and periodic clustering of simple and complex cells in visual cortex

2019 ◽  
Author(s):  
Gwangsu Kim ◽  
Jaeson Jang ◽  
Se-Bum Paik
Keyword(s):  
Visual Cortex ◽  
Periodic Variation ◽  
Orientation Tuning ◽  
List Type ◽  
Simple Complex ◽  
Single Class ◽  
Complex Cells ◽  
Orientation Maps ◽  
Simple And Complex Cells ◽  
Retinal Afferents

AbstractNeurons in the primary visual cortex (V1) are often classified as simple or complex cells, but it is debated whether they are discrete hierarchical classes of neurons developing sequentially, or if they represent a continuum of variation within a single class of cells developing simultaneously. Herein, we show that simple and complex cells may arise simultaneously from the universal process of retinal development. From analysis of the cortical receptive fields in cats, we show evidence that simple and complex cells originate from the periodic variation of ON-OFF segregation in the feedforward projection of retinal mosaics, by which they organize into periodic clusters in V1. Our key prediction that clusters of simple and complex cells correlate topographically with orientation maps was confirmed by data in cats. Our results suggest that simple and complex cells are not two distinct neural populations but arise from common retinal afferents, simultaneous with orientation tuning.HighlightsSimple and complex cells arise simultaneously from retinal afferents.Simple/complex cells are organized into periodic clusters across visual cortex.Simple/complex clusters are topographically correlated with orientation maps.Development of clustered cells in V1 is explained by the Paik-Ringach model.

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