A Nonlinear Receptive Field Model of the Visual System

1978 ◽  
Vol BME-25 (1) ◽  
pp. 76-83 ◽  
Author(s):  
Tomozo Furukawa ◽  
Shiro Hagiwara
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Field Model

scholarly journals A population receptive field model of the magnetoencephalography response

NeuroImage ◽  
2021 ◽  
Vol 244 ◽  
pp. 118554
Author(s):  
Eline R. Kupers ◽  
Akhil Edadan ◽  
Noah C. Benson ◽  
Wietske Zuiderbaan ◽  
Maartje C. de Jong ◽  
...  
Keyword(s):  
Receptive Field ◽  
Field Model

scholarly journals Distinguishing Hemodynamics from Function in the Human LGN Using a Temporal Response Model

Vision ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 27 ◽  
Author(s):  
Kevin DeSimone ◽  
Keith A. Schneider
Keyword(s):  
Receptive Field ◽  
Visual Processing ◽  
Field Model ◽  
Ventral Surface ◽  
Impulse Response Functions ◽  
Response Model ◽  
Response Functions ◽  
Temporal Response ◽  
Point Of Entry ◽  
Temporal Properties

We developed a temporal population receptive field model to differentiate the neural and hemodynamic response functions (HRF) in the human lateral geniculate nucleus (LGN). The HRF in the human LGN is dominated by the richly vascularized hilum, a structure that serves as a point of entry for blood vessels entering the LGN and supplying the substrates of central vision. The location of the hilum along the ventral surface of the LGN and the resulting gradient in the amplitude of the HRF across the extent of the LGN have made it difficult to segment the human LGN into its more interesting magnocellular and parvocellular regions that represent two distinct visual processing streams. Here, we show that an intrinsic clustering of the LGN responses to a variety of visual inputs reveals the hilum, and further, that this clustering is dominated by the amplitude of the HRF. We introduced a temporal population receptive field model that includes separate sustained and transient temporal impulse response functions that vary on a much short timescale than the HRF. When we account for the HRF amplitude, we demonstrate that this temporal response model is able to functionally segregate the residual responses according to their temporal properties.

The Study of Spatial Frequency Channels for Human Visual System

2019 ◽  
Vol 33 (06) ◽  
pp. 1955007
Author(s):  
Xiangyang Xu ◽  
Qiao Chen ◽  
Ruixin Xu
Keyword(s):  
Receptive Field ◽  
Spatial Frequency ◽  
Visual System ◽  
Human Visual System ◽  
Frequency Tuning ◽  
Tuning Curve ◽  
Vision System ◽  
Human Vision ◽  
Psychophysical Experiment ◽  
Human Vision System

Similar to auditory perception of sound system, color perception of the human visual system also presents a multi-frequency channel property. In order to study the multi-frequency channel mechanism of how the human visual system processes color information, the paper proposed a psychophysical experiment to measure the contrast sensitivities based on 17 color samples of 16 spatial frequencies on CIELAB opponent color space. Correlation analysis was carried out on the psychophysical experiment data, and the results show obvious linear correlations of observations for different spatial frequencies of different observers, which indicates that a linear model can be used to model how human visual system processes spatial frequency information. The results of solving the model based on the experiment data of color samples show that 9 spatial frequency tuning curves can exist in human visual system with each lightness, R–G and Y–B color channel and each channel can be represented by 3 tuning curves, which reflect the “center-around” form of the human visual receptive field. It is concluded that there are 9 spatial frequency channels in human vision system. The low frequency tuning curve of a narrow-frequency bandwidth shows the characteristics of lower level receptive field for human vision system, the medium frequency tuning curve shows a low pass property of the change of medium frequent colors and the high frequency tuning curve of a width-frequency bandwidth, which has a feedback effect on the low and medium frequency channels and shows the characteristics of higher level receptive field for human vision system, which represents the discrimination of details.

scholarly journals Synergistic center-surround receptive field model of monkey H1 horizontal cells

2005 ◽  
Vol 5 (11) ◽  
pp. 9 ◽  
Author(s):  
Orin S. Packer ◽  
Dennis M. Dacey
Keyword(s):  
Receptive Field ◽  
Field Model ◽  
Horizontal Cells

Direct and Indirect Cooperation between Temporal and Parietal Networks for Invariant Visual Recognition

1992 ◽  
Vol 4 (1) ◽  
pp. 35-57 ◽  
Author(s):  
Isabelle Otto ◽  
Philippe Grandguillaume ◽  
Latifa Boutkhil ◽  
Yves Burnod ◽  
Emmanuel GuigonBurnod
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Visual Recognition ◽  
Single Cells ◽  
Central Zone ◽  
Object Identification ◽  
Population Coding ◽  
Significant Shift ◽  
Learning Session ◽  
Population Vector

A new type of biologically inspired multilayered network is proposed to model the properties of the primate visual system with respect to invariant visual recognition (IVR). This model is based on 10 major neurobiological and psychological constraints. The first five constraints shape the architecture and properties of the network. 1. The network model has a Y-like double-branched multilayered architecture, with one input (the retina) and two parallel outputs, the “What” and the “Where,” which model, respectively, the temporal pathway, specialized for “object” identification, and the parietal pathway specialized for “spatial” localization. 2. Four processing layers are sufficient to model the main functional steps of primate visual system that transform the retinal information into prototypes (object-centered reference frame) in the “What” branch and into an oculomotor command in the “Where” branch. 3. The distribution of receptive field sizes within and between the two functional pathways provides an appropriate tradeoff between discrimination and invariant recognition capabilities. 4. The two outputs are represented by a population coding: the ocular command is computed as a population vector in the “Where” branch and the prototypes are coded in a “semidistributed” way in the “What” branch. In the intermediate associative steps, processing units learn to associate prototypes (through feedback connections) to component features (through feedforward ones). 5. The basic processing units of the network do not model single cells but model the local neuronal circuits that combine different information flows organized in separate cortical layers. Such a biologically constrained model shows shift-invariant and size-invariant capabilities that resemble those of humans (psychological constraints): 6. During the Learning session, a set of patterns (26 capital letters and 2 geometric figures) are presented to the network: a single presentation of each pattern in one position (at the center) and with one size is sufficient to learn the corresponding prototypes (internal representations). These patterns are thus presented in widely varying new sizes and positions during the Recognition session: 7. The “What” branch of the network succeeds in immediate recognition for patterns presented in the central zone of the retina with the learned size. 8. The recognition by the “What” branch is resistant to changes in size within a limited range of variation related to the distribution of receptive field (RF) sizes in the successive processing steps of this pathway. 9. Even when ocular movements are not allowed, the recognition capabilities of the “What” branch are unaffected by changing positions around the learned one. This significant shift-invariance of the “What” branch is also related to the distribution of RF sizes. 10. When varying both sizes and locations, the “What” and the “Where” branches cooperate for recognition: the location coding in the “Where” branch can command, under the control of the “What” branch, an ocular movement efficient to reset peripheral patterns toward the central zone of the retina until successful recognition. This model results in predictions about anatomical connections and physiological interactions between temporal and parietal cortices.

Infrared target detection method based on the receptive field and lateral inhibition of human visual system

2017 ◽  
Vol 56 (30) ◽  
pp. 8555 ◽  
Author(s):  
Shangnan Zhao ◽  
Yong Song ◽  
Yufei Zhao ◽  
Yun Li ◽  
Lin Li ◽  
...  
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Target Detection ◽  
Lateral Inhibition ◽  
Human Visual System ◽  
Detection Method

Two-Stroke Apparent Motion

2017 ◽  
Author(s):  
George Mather
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Apparent Motion ◽  
Motion Detection ◽  
Human Visual System ◽  
Motion Aftereffect ◽  
Temporal Response ◽  
Motion Sensing ◽  
Receptive Field Properties ◽  
Directional Motion

“Two-stroke” apparent motion is a powerful illusion of directional motion generated by alternating just two animation frames, which occurs when a brief blank interframe interval is inserted at alternate frame transitions. This chapter discusses this illusion, which can be explained in terms of the receptive field properties of motion-sensing neurons in the human visual system. The temporal response of these neurons contains both an excitatory phase and an inhibitory phase; when the timing of the interframe interval just matches the switch in response sign, the illusion occurs. Concepts covered in this chapter include four-stroke as well as two-stroke apparent motion, motion aftereffect, and motion detection.

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