Distributed Feedforward and Feedback Processing across Perisylvian Cortex Supports Human Speech

Abstract

Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and posterior cortical networks, but the degree and timing of their recruitment and dynamics remain unknown. We present a novel deep learning architecture that translates neural signals recorded directly from cortex to an interpretable representational space that can reconstruct speech. We leverage state-of-the-art learnt decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.