Circuit-level modeling of prediction error computation of multi-dimensional features in voluntary actions

Predictive processing posits that the brain minimizes discrepancies between internal predictions and sensory inputs, offering a unifying account of perception, cognition, and action. In voluntary actions, it is thought to suppress self-generated sensory outcomes. Although sensory mismatch signals have been extensively investigated and modeled, mechanistic insights into the neural computation of predictive processing in voluntary actions remain limited.

We developed a computational model comprising two-compartment excitatory pyramidal cells (PCs) and three major types of inhibitory interneurons with biologically realistic connectivity. The model incorporates experience-dependent inhibitory plasticity and feature selectivity to shape excitation-inhibition (E/I) balance. We then extended it to a two-dimensional prediction-error (PE) circuit in which each PC has two segregated, top-down modulated dendrites-each bell-tuned to a distinct feature-enabling combination selectivity.

The model reveals that top-down predictions can selectively suppress PCs with matching feature selectivity via experience-dependent inhibitory plasticity. This suppression depends on the response selectivity of inhibitory interneurons and on balanced excitation and inhibition across multiple pathways. The framework also accommodates predictions involving two independent features.

By combining biological connectivity data with computational modeling, this study provides insights into the neural circuits and computations underlying the active suppression of sensory responses in voluntary actions. These findings contribute to understanding how the brain generates and processes predictions to guide behavior.