Synaptic Plasticity Dynamics for Deep Continuous Local Learning


Understanding and deriving neural and synaptic plasticity rules that can enable hidden weights to learn is an ongoing quest in neuroscience and neuromorphic engineering. From a machine learning perspective, locality and differentiability are key issues of the spiking neuron model operations. In this poster presentation, it is shown that deep learning algorithms that locally approximate the gradient backpropagation updates using locally synthesized gradients overcome this challenge.