Challenging Backpropagation: Evidence for Target Learning in the Cortex

Computational neuroscience currently debates 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. While both hypotheses have been supported by theoretical studies, there is currently no empirical test that compares them directly. Here we provide such test 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. To consolidate our in vivo findings, we conduct additional in vitro experiments that clarify the relationship between algorithmic learning signals and synaptic plasticity within individual pyramidal neurons. By combining in vivo and in vitro data we reveal a critical discrepancy between neocortical hierarchical learning and canonical machine learning.