Population coding of motion patterns in the early visual system

2001 ◽  
Vol 187 (7) ◽  
pp. 549-558 ◽  
Author(s):  
Stefan Wilke ◽  
Andreas Thiel ◽  
Christian Eurich ◽  
Martin Greschner ◽  
Markus Bongard ◽  
...  
Keyword(s):  
Visual System ◽  
Population Coding ◽  
Motion Patterns ◽  
Early Visual System

scholarly journals Timing Precision in Population Coding of Natural Scenes in the Early Visual System

PLoS Biology ◽  
2008 ◽  
Vol 6 (12) ◽  
pp. e324 ◽  
Author(s):  
Gaëlle Desbordes ◽  
Jianzhong Jin ◽  
Chong Weng ◽  
Nicholas A Lesica ◽  
Garrett B Stanley ◽  
...  
Keyword(s):  
Visual System ◽  
Population Coding ◽  
Natural Scenes ◽  
Timing Precision ◽  
Early Visual System

scholarly journals A silicon early visual system as a model animal

2004 ◽  
Vol 44 (17) ◽  
pp. 2083-2089 ◽  
Author(s):  
Tobi Delbrück ◽  
Shih-Chii Liu
Keyword(s):  
Visual System ◽  
Model Animal ◽  
Early Visual System

scholarly journals Computing Optical Flow in the Primate Visual System

1989 ◽  
Vol 1 (1) ◽  
pp. 92-103 ◽  
Author(s):  
H. Taichi Wang ◽  
Bimal Mathur ◽  
Christof Koch
Keyword(s):  
Visual System ◽  
Optical Flow ◽  
Difficult Problem ◽  
Population Coding ◽  
Spatial Integration ◽  
Local Motion ◽  
Temporal Area ◽  
Second Stage ◽  
Vector Sum ◽  
Cooperative Algorithm

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We show how gradient models, a well-known class of motion algorithms, can be implemented within the magnocellular pathway of the primate's visual system. Our cooperative algorithm computes optical flow in two steps. In the first stage, assumed to be located in primary visual cortex, local motion is measured while spatial integration occurs in the second stage, assumed to be located in the middle temporal area (MT). The final optical flow is extracted in this second stage using population coding, such that the velocity is represented by the vector sum of neurons coding for motion in different directions. Our theory, relating the single-cell to the perceptual level, accounts for a number of psychophysical and electrophysiological observations and illusions.

Efficient coding matters in the organization of the early visual system

2018 ◽  
Vol 105 ◽  
pp. 218-226 ◽  
Author(s):  
Qingqun Kong ◽  
Jiuqi Han ◽  
Yi Zeng ◽  
Bo Xu
Keyword(s):  
Visual System ◽  
Efficient Coding ◽  
Early Visual System

Processing of Natural Time Series of Intensities in the Early Visual System of the Blowfly

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 24-24 ◽  
Author(s):  
J H van Hateren
Keyword(s):  
Time Series ◽  
Visual System ◽  
Visual Processing ◽  
Time Course ◽  
Temporal Frequency ◽  
Power Spectra ◽  
Calliphora Vicina ◽  
Intensity Measurements ◽  
Natural Time ◽  
Early Visual System

The first steps of processing in the visual system of the blowfly are well suited for studying the relationship between the properties of the environment and the function of visual processing (eg Srinivasan et al, 1982 Proceedings of the Royal Society, London B216 427; van Hateren, 1992 Journal of Comparative Physiology A171 157). Although the early visual system appears to be linear to some extent, there are also reports on functionally significant nonlinearities (Laughlin, 1981 Zeitschrift für Naturforschung36c 910). Recent theories using information theory for understanding the early visual system perform reasonably well, but not quite as well as the real visual system when confronted with natural stimuli [eg van Hateren, 1992 Nature (London)360 68]. The main problem seems to be that they lack a component that adapts with the right time course to changes in stimulus statistics (eg the local average light intensity). In order to study this problem of adaptation with a relatively simple, yet realistic, stimulus I recorded time series of natural intensities, and played them back via a high-brightness LED to the visual system of the blowfly ( Calliphora vicina). The power spectra of the intensity measurements and photoreceptor responses behave approximately as 1/ f, with f the temporal frequency, whilst those of second-order neurons (LMCs) are almost flat. The probability distributions of the responses of LMCs are almost gaussian and largely independent of the input contrast, unlike the distributions of photoreceptor responses and intensity measurements. These results suggest that LMCs are in effect executing a form of contrast normalisation in the time domain.

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