Stochastic constancy, variability and adaptation of spike generation: Performance of a giant neuron in the visual system of the fly

1980 ◽  
Vol 38 (1) ◽  
pp. 31-40 ◽  
Author(s):  
G. Gestri ◽  
H. A. K. Mastebroek ◽  
W. H. Zaagman
Keyword(s):  
Visual System ◽  
Spike Generation ◽  
Giant Neuron

Axonal spike generation near the giant neuron soma computed by the Hodgkin-Huxley equation

1976 ◽  
Vol 7 (4) ◽  
pp. 328-332
Author(s):  
I. Z. Zamekhovskii ◽  
A. N. Degtyarenko
Keyword(s):  
Spike Generation ◽  
Giant Neuron ◽  
Huxley Equation ◽  
Neuron Soma

Author response for "The brain of the African wild dog. IV . The visual system"

2020 ◽  
Author(s):  
Samson Chengetanai ◽  
Adhil Bhagwandin ◽  
Mads F. Bertelsen ◽  
Therese Hård ◽  
Patrick R. Hof ◽  
...  
Keyword(s):  
Visual System ◽  
Author Response ◽  
African Wild Dog ◽  
Wild Dog ◽  
The Brain

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Newborn's Preference for Faces

1996 ◽  
Vol 1 (3) ◽  
pp. 200-205 ◽  
Author(s):  
Carlo Umiltà ◽  
Francesca Simion ◽  
Eloisa Valenza
Keyword(s):  
Visual System ◽  
Structural Properties ◽  
Sensory Properties ◽  
Human Face ◽  
System Experiment ◽  
Face Experiment

Four experiments were aimed at elucidating some aspects of the preference for facelike patterns in newborns. Experiment 1 showed a preference for a stimulus whose components were located in the correct arrangement for a human face. Experiment 2 showed a preference for stimuli that had optimal sensory properties for the newborn visual system. Experiment 3 showed that babies directed their attention to a facelike pattern even when it was presented simultaneously with a non-facelike stimulus with optimal sensory properties. Experiment 4 showed the preference for facelike patterns in the temporal hemifield but not in the nasal hemifield. It was concluded that newborns' preference for facelike patterns reflects the activity of a subcortical system which is sensitive to the structural properties of the stimulus.

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