Figure 8:

Hierarchical models (cNVAE, cNAE) are more aligned with MT neurons since they enable sparse latent-to-neuron relationships. (a) Alignment score measures the sparsity of permutation feature importances. $a_i=0$ when all latents are equally important in predicting neuron $i$; and, $a_i=1$ when a single latent predicts the neuron. (b) Feature importances are plotted for an example neuron (same as in Fig. 6b). cNVAE $(\beta =0.01)$ predicts this neuron’s response in a much sparser manner compared to non-hierarchical VAE $(\beta =5)$. Supplementary section 9.5 contains a discussion of our rationale in choosing these $\beta$ values. (c) Alignment across $\beta$ values, and autoencoders (ae).