Synaptic Weight

Synaptic weight, representing the strength of connections between neurons, is a central focus in understanding neural computation and building artificial neural networks. Current research emphasizes optimizing synaptic weight dynamics for improved learning efficiency and robustness, exploring diverse approaches including spiking neural networks (SNNs) with various learning rules (e.g., spike-timing-dependent plasticity, surrogate gradient methods), and investigating the impact of weight quantization and distribution on network performance and energy efficiency. These advancements hold significant implications for both neuroscience, by providing mechanistic insights into biological learning, and artificial intelligence, by enabling the development of more efficient and powerful neuromorphic computing systems.