  Top-down feedback in cortex is critical for guiding sensory processing, which
has prominently been formalized in the theory of hierarchical predictive coding
(hPC). However, experimental evidence for error units, which are central to the
theory, is inconclusive, and it remains unclear how hPC can be implemented with
spiking neurons. To address this, we connect hPC to existing work on efficient
coding in balanced networks with lateral inhibition, and predictive computation
at apical dendrites. Together, this work points to an efficient implementation
of hPC with spiking neurons, where prediction errors are computed not in
separate units, but locally in dendritic compartments. The implied model shows
a remarkable correspondence to experimentally observed cortical connectivity
patterns, plasticity and dynamics, and at the same time can explain hallmarks
of predictive processing, such as mismatch responses, in cortex. We thus
propose dendritic predictive coding as one of the main organizational
principles of cortex.
