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Abstract:
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 effi-
cient coding in balanced networks with lateral inhibition and predictive computa-
tion at apical dendrites. Together, this work points to an efficient implementation
of hPC with spiking neurons, where prediction errors are computed not in sepa-
rate units, but locally in dendritic compartments. We then discuss the correspon-
dence of this model to experimentally observed connectivity patterns, plasticity,
and dynamics in cortex.
