English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the most striking features of neocortical anatomy is the presence ofextensive top-down projections into primary sensory areas. Notably, many ofthese top-down projections impinge on the distal apical dendrites of pyramidalneurons, where they exert a modulatory effect, altering the gain of responses.It is thought that these top-down projections carry contextual information thatcan help animals to resolve ambiguities in sensory data. However, it has yet tobe demonstrated how such modulatory connections to the distal apical dendritescan serve this computational function. Here, we develop a computational modelof pyramidal cells that integrates contextual information from top-downprojections to apical compartments with sensory representations driven bybottom-up projections to basal compartments. When input stimuli are ambiguousand relevant contextual information is available, the apical feedback modulatesthe basal signals to recover unambiguous sensory representations. Importantly,when stimuli are unambiguous, contextual information which is irrelevant oropposes sensory evidence is appropriately ignored by the model. By generalizingthe task to temporal sequences, we further show that our model can learn tointegrate contextual information across time. Using layer-wise relevancepropagation, we extract the importance of individual neurons to the predictionof each category, revealing that neurons that are most relevant for the overlapof categories receive the largest magnitude of top-down signals, and arenecessary for solving the task. This work thus provides a proof-of-conceptdemonstrating how the top-down modulatory inputs to apical dendrites in sensoryregions could be used by the cortex to handle the ambiguities that animalsencounter in the real world.

Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites