BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks

Understand

Spiking neural networks (SNN) are delivering energy-efficient, massively parallel, and low-latency solutions to AI problems, facilitated by the emerging neuromorphic chips.

• To harness these computational benefits, SNN need to be trained by learning algorithms that adhere to brain-inspired neuromorphic principles, namely event-based, local, and online computations.
• Yet, the state-of-the-art SNN training algorithms are based on backprop that does not follow the above principles.
• Due to its limited biological plausibility, the application of backprop to SNN requires non-local feedback pathways for transmitting continuous-valued errors, and relies on gradients from future timesteps.