Local Plasticity

Local plasticity in neural networks focuses on developing efficient learning rules that modify synaptic weights based solely on pre- and post-synaptic neuron activity, mimicking biological learning mechanisms. Current research emphasizes the integration of local plasticity with global neuromodulation or context gating within spiking neural networks (SNNs) and neuromorphic hardware, aiming for energy-efficient, real-time learning in applications like edge computing and robotics. This approach promises significant advancements in creating adaptive, low-power systems capable of one-shot learning and lifelong learning, impacting fields ranging from AI to embedded systems.