Leveraging unlabelled data for generalizable neural population decoding
Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance. However, current spike-based models are restricted to supervised learning (SL), limiting training to datasets with paired behavioural labels. To address this limitation, we introduce MOJO (Masked autOencoder-based JOint training), a training framework for spike-tokenizing models that jointly leverages self-
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 49%A unifying framework from neural superposition to sparse interpretable codes - Nature →
- PossiblePossibly related (embedding) · 49%Algorithm–hardware co-design of neuromorphic networks with dual memory pathways →
- LinkedLinked via arxiv author · 85%Ximeng Mao →
“Leveraging unlabelled data for generalizable neural population decoding”
- LinkedLinked via arxiv author · 85%Nanda H. Krishna →
“Leveraging unlabelled data for generalizable neural population decoding”
- LinkedLinked via arxiv author · 85%Avery Hee-Woon Ryoo →
“Leveraging unlabelled data for generalizable neural population decoding”
- LinkedLinked via arxiv author · 85%Matthew G. Perich →
“Leveraging unlabelled data for generalizable neural population decoding”
- LinkedLinked via arxiv author · 85%Guillaume Lajoie →
“Leveraging unlabelled data for generalizable neural population decoding”