Table 1 - Fusing multisensory signals across channels and time

A table noting: each model’s parameter scaling, each model’s number of learnable parameters here, and how each model combines information across sensory channels (linear or nonlinear) and time. Linear and nonlinear fusion (LF and NLF), treat each timestep independently. NLF2/3 fuse information across short temporal windows. RNNs combine incoming signals with their prior hidden states. For the parameter scaling functions: O - the number of possible observations, Nc - the number of sensory channels, w - the temporal integration window. For our RNN models the number of parameters scales as: , where denote the number of input, hidden and output units and Nb is the number of bias parameters per hidden unit.