Topic: Spike-Time-Dependent Plasticity

A key driver of mammalian intelligence is the ability to represent sensory inputs at different levels of abstraction. In deep learning, hierarchical neural networks learn such abstractions through top-down signals that drive synaptic plasticity. This is commonly done with backpropagation, an algorithm that is considered biologically implausible. In this work, we present a novel dynamic target propagation framework for hierarchical learning that uses Differential Hebbian (DH) updates, a rate-based version of Spike-Time-Dependent Plasticity. Our framework is based on the Deep Feedback Control (D