Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?
Authors: David Mayo, Christopher Wang, Asa Harbin, Abdulrahman Alabdulkareem, Albert Shaw, Boris Katz, Andrei Barbu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce Brain Bits, a method that uses a bottleneck to quantify the amount of signal extracted from neural recordings that is actually necessary to reproduce a method s reconstruction fidelity. We find that it takes surprisingly little information from the brain to produce reconstructions with high fidelity. |
| Researcher Affiliation | Collaboration | 1MIT CSAIL, CBMM 2MIT Lincoln Laboratory 3Google Deep Mind |
| Pseudocode | No | The paper describes methods and processes in text and diagrams but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/czlwang/Brain Bits. Correspondence to DM and CW at EMAIL and AH at EMAIL. We release our code, which builds on the publicly available code in [17]. |
| Open Datasets | Yes | This family of methods has been facilitated by the growth of f MRI datasets containing pairs of stimuli and recorded neural data, the current largest of which is the publicly available Natural Scenes Dataset (NSD) [1]. The Natural Scenes Dataset contains f MRI recordings of multiple subjects cumulatively viewing tens of thousands of samples from the Microsoft Co Co dataset [14]. As a result, it is a popular choice for many recent methods [6, 29], and we select it for our analysis. |
| Dataset Splits | No | We train for 100 epochs and use the weights with the best validation loss at test time. (Appendix A.1) |
| Hardware Specification | Yes | All mappings and image generations were computed on two Nvidia Titan RTXs over the course of a week. |
| Software Dependencies | No | The paper mentions using "Adam W optimizer [15]" but does not specify version numbers for any software, libraries, or programming languages. |
| Experiment Setup | Yes | We train our network with a batch size b = 128, an Adam W optimizer [15], a weight decay of wd = 0.1 and a learning rate of lr = 0.01. We train for 100 epochs and use the weights with the best validation loss at test time. (Appendix A.1) |