Computational analysis of inhibitory motifs reveals distinct motion prediction mechanisms in retinal circuits.
Motion is an important feature of visual scenes and retinal neuronal circuits selectively signal different motion features. It has been shown that the retina can extrapolate the position of a moving object, thereby compensating sensory transmission delays and enabling signal processing in real-time. Amacrine cells, the inhibitory interneurons of the retina, play essential roles in such computations although their precise function remain unclear. Here, we computationally explore the potential effects of two different inhibitory connectivity motifs on the retina’s response to moving objects, in a simplified model of the retina: feed-forward and recurrent feed-back inhibition. In this model, both motifs can account for motion anticipation with two different mechanisms. Feed-forward inhibition truncates motion responses and shifts peak responses forward via subtractive inhibition, whereas recurrent feed-back coupling evokes excitatory and inhibitory waves with different phases that interfere and shift the response peak. A key difference between the two mechanisms is how the anticipatory peak shift scales with the speed of a moving object. Motion prediction with feed-forward circuits monotonically decreases with increasing speeds, while recurrent feed-back coupling induces tuning curves that exhibit a preferred speed for which motion prediction is maximal.
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Ebert et al. (2026) studied this question.
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