Information-theoretic interpretation of tuning curves for multiple motion directions
This work addresses how neural populations efficiently represent complex visual motion, but it is incremental as it builds on existing information-theory and network models.
The authors tackled the problem of understanding optimal tuning curve shapes for sensory neurons in population coding of visual motion with multiple directions, and found that their information-maximization method produces diverse symmetric and asymmetric tuning curves that match experimental observations, suggesting this heterogeneity is optimal.
We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model. When applied to the problem of population coding of visual motion with multiple directions, our method yields several types of tuning curves with both symmetric and asymmetric shapes that resemble what have been found in the visual cortex. Our result suggests that the diversity or heterogeneity of tuning curve shapes as observed in neurophysiological experiment might actually constitute an optimal population representation of visual motions with multiple components.