Brain-optimized inference improves reconstructions of fMRI brain activity

The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from a base decoding method, then iteratively refine these reconstructions using a brain-optimized encoding model that maps images to brain activity. At each iteration, we sample a small library of images from an image distribution (a diffusion model) conditioned on a seed reconstruction from the previous iteration. We select those that best approximate the measured brain activity when passed through our encoding model, and use these images for structural guidance during the generation of the small library in the next iteration. We reduce the stochasticity of the image distribution at each iteration, and stop when a criterion on the "width" of the image distribution is met. We show that when this process is applied to recent decoding methods, it outperforms the base decoding method as measured by human raters, a variety of image feature metrics, and alignment to brain activity. These results demonstrate that reconstruction quality can be significantly improved by explicitly aligning decoding distributions to brain activity distributions, even when the seed reconstruction is output from a state-of-the-art decoding algorithm. Interestingly, the rate of refinement varies systematically across visual cortex, with earlier visual areas generally converging more slowly and preferring narrower image distributions, relative to higher-level brain areas. Brain-optimized inference thus offers a succinct and novel method for improving reconstructions and exploring the diversity of representations across visual brain areas.

Second Sight: Using brain-optimized encoding models to align image distributions with human brain activity

Two recent developments have accelerated progress in image reconstruction from human brain activity: large datasets that offer samples of brain activity in response to many thousands of natural scenes, and the open-sourcing of powerful stochastic image-generators that accept both low- and high-level guidance. Most work in this space has focused on obtaining point estimates of the target image, with the ultimate goal of approximating literal pixel-wise reconstructions of target images from the brain activity patterns they evoke. This emphasis belies the fact that there is always a family of images that are equally compatible with any evoked brain activity pattern, and the fact that many image-generators are inherently stochastic and do not by themselves offer a method for selecting the single best reconstruction from among the samples they generate. We introduce a novel reconstruction procedure (Second Sight) that iteratively refines an image distribution to explicitly maximize the alignment between the predictions of a voxel-wise encoding model and the brain activity patterns evoked by any target image. We show that our process converges on a distribution of high-quality reconstructions by refining both semantic content and low-level image details across iterations. Images sampled from these converged image distributions are competitive with state-of-the-art reconstruction algorithms. Interestingly, the time-to-convergence varies systematically across visual cortex, with earlier visual areas generally taking longer and converging on narrower image distributions, relative to higher-level brain areas. Second Sight thus offers a succinct and novel method for exploring the diversity of representations across visual brain areas.

Reconstructing seen images from human brain activity via guided stochastic search

Visual reconstruction algorithms are an interpretive tool that map brain activity to pixels. Past reconstruction algorithms employed brute-force search through a massive library to select candidate images that, when passed through an encoding model, accurately predict brain activity. Here, we use conditional generative diffusion models to extend and improve this search-based strategy. We decode a semantic descriptor from human brain activity (7T fMRI) in voxels across most of visual cortex, then use a diffusion model to sample a small library of images conditioned on this descriptor. We pass each sample through an encoding model, select the images that best predict brain activity, and then use these images to seed another library. We show that this process converges on high-quality reconstructions by refining low-level image details while preserving semantic content across iterations. Interestingly, the time-to-convergence differs systematically across visual cortex, suggesting a succinct new way to measure the diversity of representations across visual brain areas.