Dependent Plasticity

Dependent plasticity, encompassing phenomena where a system's response depends on its history, is a focus of current research across diverse fields. Researchers are exploring how recurrent neural networks, including Long Short-Term Memory (LSTM) networks and spiking neural networks (SNNs) employing Spike-Timing Dependent Plasticity (STDP), can effectively model this path-dependent behavior in materials science, time series prediction, and even biological learning. This work aims to improve the accuracy and efficiency of predicting complex, history-dependent systems, with applications ranging from material design to AI algorithms. The development of more biologically plausible learning rules within artificial neural networks is a key driver of this research.