Neural Embeddings Rank: Aligning 3D latent dynamics with movements
–Neural Information Processing Systems
Aligning neural dynamics with movements is a fundamental goal in neuroscience and brain-machine interfaces. However, there is still a lack of dimensionality reduction methods that can effectively align low-dimensional latent dynamics with movements. To address this gap, we propose Neural Embeddings Rank (NER), a technique that embeds neural dynamics into a 3D latent space and contrasts the embeddings based on movement ranks. NER learns to regress continuous representations of neural dynamics (i.e., embeddings) on continuous movements. We apply NER and six other dimensionality reduction techniques to neurons in the primary motor cortex (M1), dorsal premotor cortex (PMd), and primary somatosensory cortex (S1) as monkeys perform reaching tasks.
Neural Information Processing Systems
May-25-2025, 21:48:31 GMT
- Country:
- North America > United States > Utah (0.14)
- Genre:
- Research Report > Experimental Study (1.00)
- Industry:
- Health & Medicine > Therapeutic Area > Neurology (1.00)
- Technology: