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Journal Articles eNeuro Year : 2022

Speed estimation for visual tracking emerges dynamically from nonlinear frequency interactions

Abstract

Research Article: New Research speed axis, respectively. Sampling frequency space, MCs elicited stronger, less variable, and speed-tuned responses. DGs yielded weaker and more frequency-tuned responses. Second, we measured responses to patterns made of two or three components covering a range of orientations within Fourier space. Early tracking initiation of the patterns was best predicted by a linear combination of components before nonlinear interactions emerged to shape later dynamics. Inputs are supralinearly integrated along an iso-velocity line and sublinearly integrated away from it. A dynamical probabilistic model characterizes these interactions as an excitatory pooling along the iso-velocity line and inhibition along the orthogonal "scale" axis. Such crossed patterns of interaction would appropriately integrate or segment moving objects. This study supports the novel idea that speed estimation is better framed as a dynamic channel interaction organized along speed and scale axes.
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hal-03668551 , version 1 (15-05-2022)

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Andrew Meso, Nikos Gekas, Pascal Mamassian, Guillaume Masson. Speed estimation for visual tracking emerges dynamically from nonlinear frequency interactions. eNeuro, 2022, 9 (3), pp.ENEURO.0511 - 21.2022. ⟨10.1523/eneuro.0511-21.2022⟩. ⟨hal-03668551⟩
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