Hierarchical Target Learning in the Mammalian Neocortex: A Pyramidal Neuron Perspective

The mammalian neocortex possesses the remarkable ability to translate complex sensory inputs into abstract representations through the coordinated activity of large neuronal ensembles across the sensory hierarchy. While cortical hierarchies are anatomically well described, how learning is or-chestrated across the spatial scales ranging from large neuronal networks to pyramidal neurons and their individual synapses is unknown. Here we address this gap from the ground up by modeling the membrane potential and calcium dynamics of individual pyramidal neuron synapses while working upward toward network learning. Starting at the lowest level, we adopt a calcium-dependent synaptic plasticity rule consistent with a wide range of molecular and electrophysiological findings and implement this rule in a synaptic model. We then embed our synaptic model into a pyramidal cell model with apical and dendritic compartments, and integrate various experimental observations such as bursts, calcium plateaus, and somato-apical coupling. We validate the predictions of our neuron model through direct in vitro electrophysiology experiments on layer 5 (L5) pyramidal neurons from the mouse prefrontal cortex and demonstrate that inputs arriving at apical dendrites guide plasticity at basal synapses. Finally, we investigate the algorithmic principles of hierarchical credit assignment in the mammalian neocortex by embedding our pyramidal neuron model in various biologically-plausible deep learning architectures that have been proposed to explain learning in cortex. We find that our model seamlessly aligns with target learning architectures, where top-down feedback arriving at the apical dendrite modifies pyramidal neuron activities to align with desired higher-level neural activity. Importantly, supported by our biological data, this cortical target learning cannot be easily cast into the backpropagation algorithm. By providing a cross-scale framework for cortical hierarchical learning, our work reveals a potential discrepancy between learning in biological neural networks and conventional deep learning.