A Compositional Code for Speech in Premotor Cortex

Compositionality has been proposed as an organizing principle for how neural dynamical primitives are combined to produce movements in simple motor control tasks, such as reaching. Here, we extend this idea to speech by proposing a mechanistic model of how premotor cortex constructs sentence level dynamics from reusable neural trajectories across multiple linguistic levels: phonemes, biphonemes, syllables, and words. We show that the neural activity generated from our model reproduces key temporal and spectral properties of the recorded data and preserves speech relevant information: a decoder trained on real neural recordings achieves comparable phoneme decoding performance on synthetic and held out real activity. Finally, we demonstrate that augmenting real datasets with model generated activity improves speech decoding in low data regimes. This is particularly relevant for brain computer interfaces (BCIs), where neural recordings are scarce, while modern decoding models are increasingly data hungry.