MST野細胞集団の活動プロフィルによるvisual flowおよびその運動残効認知の情報表現 Representation of the Information for the Perception of Visual Flow and Its Aftereffect by the Activity-Profile of a Population of MST Cells





We propose a hypothesis 'population representation of visual flow by the activity profile of MST cells', which assumes that the perception of all kinds of visual flow (uniform translation, rotation, approaching, receding, etc.) and of motion aftereffects caused by them would be directly related to the activity-profile of the cells in the dorsal part of the MST area. In order to test this hypothesis, we investigated properties of both visual flow perception and the motion aftereffect (by psychophysical experiments on human subjects), and compared with MST cells' activities measured during and after giving various flow stimuli (by single cell recordings from anaesthetized monkeys). We have confirmed that we perceive characteristic motion aftereffect the direction of which was opposite to the adapting stimuli; for example, perception of counterclockwise rotation against a stationary texture-field after receiving a flow of clockwise rotation. MST cells, which responded selectively to the particular mode of visual flow, reduced their activities after receiving the prolonged flow stimulus moving in the preferred direction, whereas increased their activities after receiving the flow moving in the opposite direction. These response properties strongly support proposed hypothesis. We can interpret the properties of the perception of a transparent visual flow (superposition of two translations of different directions) and of its motion aftereffect with the 'population representation hypothesis', consistently, by assuming the existence of two sub-types, named 'component type' and 'integration type', in MST cells responding selectively to the translational flow. The existence of the two sub-types was verified in our present experiments.


  • 日本神経回路学会誌 = The Brain & neural networks

    日本神経回路学会誌 = The Brain & neural networks 9(1), 4-15, 2002-03-05

    Japanese Neural Network Society

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