Challenging Backpropagation: Evidence for Target Learning in the Neocortex

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 tests in the mouse neocortex by evaluating both hypotheses against experimental data at the single cell level and at the population level. At the single cell level, we conduct in vitro experiments that clarify the relationship between algorithmic learning signals and synaptic plasticity within individual pyramidal neurons. At the population level, we analyze in vivo calcium imaging data in the lateral visual cortex. By combining in vivo and in vitro data we reveal a critical discrepancy between neocortical hierarchical learning and canonical machine learning.