Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.

T5) to generate an auxiliary directional stimulus prompt for each input instance.

For eachstimulus,ourextractedcomputational mechanisms areconsistent withprior scientific literature, and in one case yields a new mechanistic hypothesis.

Neural Information Processing Systems http://nips.cc/

A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities.

Neural Information Processing Systems http://nips.cc/

Emergence of grid-like representations by training recurrent neural networks to perform spatial localization.

Here, we propose a hybrid model, the Hyper-HMM, that simultaneously aligns both temporal and spatial features across brains.