@neuralreckoning@neuromatch.social
Post #1409880
2025-01-14 13:02 UTC
New preprint! With Swathi Anil and @marcusghosh.
If you want to get the most out of a multisensory signal, you should take it's temporal structure into account. But which neural architectures do this best? 🧵👇
https://www.biorxiv.org/content/10.1101/2024.12.19.629348v1
#neuroscience #computationalneuroscience #compneuro
Replies (1)
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@neuralreckoning@neuromatch.social 2025-01-14 13:04
@marcusghosh In previous work, we found that when multimodal information arrives sparsely in time (e.g. prey hiding from predator), nonlinear fusion of different modalities gives a big improvement over linear fusion. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012246 In this paper, we looked at what happens when, in addition to being sparse, information arrives in contiguous bursts (e.g. prey scurrying from hiding spot to hiding spot). In general, the optimal algorithm is computationally intractable, so how far can you get with simple neural architectures?