Spatio-temporal transformers for decoding neural movement control
Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results. To this end, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. We test our model on multi electrodes recordings from the dorsal premotor cortex (PMd) of non-human primates while performing a motor inhibition task. The proposed architecture provides a very early prediction of the correct movement direction – no later than 230ms after the Go signal presentation across animals – and can accurately forecast whether the movement will be generated or withheld before a Stop signal, unattended, is actually presented. We also analyze the internal dy