Topic: multiplicative interactions

Causal inference (CI) has been proposed as a universal computational motif in the brain [Shams & Beierholm 2020]. However, its neural implementation is unclear. Likewise, Divisive Normalization (DN) has been proposed as a canonical circuit motif [Heeger 2011], but there are competing theories about the computations that DN implements (gain control, attention). In this work, we unified both by showing how an extended DN model can account for neural predictions made by a CI model [Shivkumar et al. 2022] in the context of hierarchical motion perception. Specifically, we generated CI predicti

A fundamental goal of visual perception is to generate informative representations that support downstream tasks. Doing so requires disentangling the raw image stream into representations that reflect its underlying causes - i.e., form and motion. Therefore, we propose a generative model containing separate latent variables representing form and motion. The model is related to prior work on sparse coding and slow feature analysis, but goes further by positing multiplicative interactions between latent variables for invariant object representations and for equivariant transformations representi