  This paper presents BioNeRF, a biologically plausible architecture that
models scenes in a 3D representation and synthesizes new views through radiance
fields. Since NeRF relies on the network weights to store the scene's
3-dimensional representation, BioNeRF implements a cognitive-inspired mechanism
that fuses inputs from multiple sources into a memory-like structure, improving
the storing capacity and extracting more intrinsic and correlated information.
BioNeRF also mimics a behavior observed in pyramidal cells concerning
contextual information, in which the memory is provided as the context and
combined with the inputs of two subsequent neural models, one responsible for
producing the volumetric densities and the other the colors used to render the
scene. Experimental results show that BioNeRF outperforms state-of-the-art
results concerning a quality measure that encodes human perception in two
datasets: real-world images and synthetic data.
