PC-SNN: Predictive Coding-based Local Hebbian Plasticity Learning in Spiking Neural Networks

Understand

Spiking Neural Networks (SNNs), regarded as the third generation of neural networks, emulate the brain's information processing with unparalleled biological plausibility compared to traditional neural networks.

• However, their non-linear, event-driven dynamics pose significant challenges for training, and existing methods often deviate from neuroscientific principles of cortical learning.
• Drawing inspiration from predictive coding theory-a leading model of brain information processing-we propose PC-SNN, a novel learning framework that integrates predictive coding with SNNs to enable biologically plausible, local Hebbian plasticity without reliance on backpropagation.
• Unlike conventional SNN training approaches, PC-SNN leverages only local computations, aligning with the brain's distributed processing and overcoming the biological implausibility of global error propagation.