A Flow Matching Framework for Neural Representational Dissimilarity
Quick summary
arXiv:2609.31544v1 Announce Type: new Abstract: Neural representational dissimilarity quantifies differences between neural response distributions, and is essential for comparing neural codes across stimuli, brain areas, tasks, and models. Commonly used distance metrics involve different assumptions and are estimated with separate methods. Here, we show that a variety of distance metrics can be unified under a flow matching framework developed in deep generative models. That is, these distances arise as Jeffreys divergences under different velocity constraints. We find that flow matching has a
Key takeaways
- arXiv:2609.31544v1 Announce Type: new Abstract: Neural representational dissimilarity quantifies differences between neural response distributions, and is essential for comparing neural codes across stimuli, brain areas, tasks, and models.
- Commonly used distance metrics involve different assumptions and are estimated with separate methods.
- Here, we show that a variety of distance metrics can be unified under a flow matching framework developed in deep generative models.
Why it matters
The importance of “A Flow Matching Framework for Neural Representational Dissimilarity” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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