CTN: Benjamin Grewe

Title: Target Learning rather than Backpropagation Explains Learning in the Mammalian Neocortex

Abstract: Modern computational neuroscience presents two competing hypotheses for hierarchical learning in the neocortex: (1) deep learning-inspired approximations of the backpropagation algorithm, where neurons adjust synapses to minimize error, and (2) target learning algorithms, where neurons reduce the feedback required to achieve a desired activity. In this talk, I will explore this fundamental question by examining the relationship between synaptic plasticity and the somatic activity of pyramidal neurons. Using a combination of single-neuron modeling, in vitro experiments, and deep learning theory, we predict distinct neuronal dynamics for each hypothesis. We then test these predictions using in vivo data from the mouse visual cortex. Our results reveal that cortical learning aligns more closely with target learning, underscoring a significant discrepancy between conventional deep learning approaches and the mechanisms underlying cortical hierarchical learning. This work provides new insights into the neural processes that drive learning in the brain and challenges current models inspired by deep learning.