Inter-individual and inter-site neural code conversion without shared stimuli

TL;DR

This paper introduces a neural code conversion method that enables inter-individual and inter-site brain activity translation without shared stimuli, facilitating scalable brain data analysis and potential brain-to-brain communication.

Contribution

It presents a novel neural code conversion technique that does not require shared stimuli, leveraging hierarchical DNN features to improve cross-individual brain activity decoding.

Findings

Conversion accuracy comparable to shared-stimuli methods

High-quality visual reconstructions from converted brain activity

Effective across different sites and limited training data

Abstract

Inter-individual variability in fine-grained functional brain organization poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitigate these individual differences but typically require paired brain data with the same stimuli between individuals, which is often unavailable. We present a neural code conversion method that overcomes this constraint by optimizing conversion parameters based on the discrepancy between the stimulus contents represented by original and converted brain activity patterns. This approach, combined with hierarchical features of deep neural networks (DNNs) as latent content representations, achieves conversion accuracy comparable to methods using shared stimuli. The converted brain activity from a source subject can be accurately decoded using the target's pre-trained decoders, producing high-quality visual image reconstructions that rival within-individual decoding, even with data across different sites and limited training samples. Our approach offers a promising framework for scalable neural data analysis and modeling and a foundation for brain-to-brain communication.