  One of the most striking features of neocortical anatomy is the presence of
extensive top-down projections into primary sensory areas. Notably, many of
these top-down projections impinge on the distal apical dendrites of pyramidal
neurons, where they exert a modulatory effect, altering the gain of responses.
It is thought that these top-down projections carry contextual information that
can help animals to resolve ambiguities in sensory data. However, it has yet to
be demonstrated how such modulatory connections to the distal apical dendrites
can serve this computational function. Here, we develop a computational model
of pyramidal cells that integrates contextual information from top-down
projections to apical compartments with sensory representations driven by
bottom-up projections to basal compartments. When input stimuli are ambiguous
and relevant contextual information is available, the apical feedback modulates
the basal signals to recover unambiguous sensory representations. Importantly,
when stimuli are unambiguous, contextual information which is irrelevant or
opposes sensory evidence is appropriately ignored by the model. By generalizing
the task to temporal sequences, we further show that our model can learn to
integrate contextual information across time. Using layer-wise relevance
propagation, we extract the importance of individual neurons to the prediction
of each category, revealing that neurons that are most relevant for the overlap
of categories receive the largest magnitude of top-down signals, and are
necessary for solving the task. This work thus provides a proof-of-concept
demonstrating how the top-down modulatory inputs to apical dendrites in sensory
regions could be used by the cortex to handle the ambiguities that animals
encounter in the real world.
