Spike Timing Dependent Plasticity

Spike-timing-dependent plasticity (STDP) is a biologically inspired learning rule for spiking neural networks (SNNs) that modifies synaptic strength based on the precise timing of pre- and postsynaptic spikes. Current research focuses on developing efficient STDP-based learning algorithms for SNNs, including exploring various architectures like convolutional SNNs and recurrent SNNs, and integrating STDP with other plasticity mechanisms like short-term plasticity and homeostasis to improve learning performance and energy efficiency. This work aims to create more biologically plausible and computationally efficient artificial intelligence systems, with applications ranging from neuromorphic hardware to advanced signal processing and pattern recognition. The ultimate goal is to better understand biological neural learning and translate these insights into improved AI technologies.