The lab had two papers accepted at NeurIPS this year! We are excited to attend the meeting in New Orleans!
EIT – In this work, we introduce a new space-time separable transformer architecture for building representations of dynamics called Embedded Interaction Transformer (EIT). When applied to the activity from populations of neurons where size and ordering may not be consistent across datasets, we show how EIT can be used to unlock across-animal transfer for neural decoders!
MTNeuro – In this work, we introduce a new multi-task benchmark for evaluating models of brain structure across multiple spatial scales and at different levels of abstraction. We provide new baseline models and ways to extract representations from 3D high-resolution (~1 um) neuroimaging data spanning many regions of interest with diverse anatomy in the mouse brain.
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