Event:

Computational neuroscience currently discusses two competing hypotheses to explain hierarchical learning in the neocortex: deep learning inspired approximations of the backpropagation algorithm, where neurons adjust synapses to minimize an error, and target learning algorithms, where neurons learn by reducing the feedback needed to achieve a desired target activity.
We test these hypotheses in the mouse neocortex by analyzing in vivo data from pyramidal neurons, finding that the target learning hypothesis more accurately predicts the neural activity during learning.
On a second stage, we conduct additional in vitro experiments that clarify the relationship between algorithmic learning signals and synaptic plasticity.
By combining in vivo and in vitro data to we reveal a critical discrepancy between neocortical hierarchical learning and canonical machine learning.