The field of neural representation and brain-computer interfaces is rapidly advancing, with a focus on developing more accurate and efficient models of brain function. Recent research has highlighted the importance of abstract, form-independent representations of meaning in the language cortex, and has demonstrated the ability of neural networks to represent beauty and aesthetic judgment. Additionally, new methods for comparing model activations to brain responses have been proposed, enabling more accurate predictions of neural activity and mechanism identification. Noteworthy papers in this area include: Representing Beauty: Towards a Participatory but Objective Latent Aesthetics, which explores the capacity of neural networks to represent beauty despite the immense formal diversity of objects for which the term applies. Model-brain comparison using inter-animal transforms, which proposes a comparison methodology based on the Inter-Animal Transform Class (IATC) to map bidirectionally between a candidate model's responses and brain data.