Spiking Neural Networks (SNNs) offer a biologically inspired approach to neural computation, where information is processed through discrete spikes, mimicking the way neurons communicate in the brain. This talk explores the role of SNNs in modeling memory storage mechanisms, with a focus on how temporal dynamics, synaptic plasticity, and network topology contribute to memory formation and retrieval. We will discuss key theoretical models, computational frameworks, and recent advancements in leveraging SNNs for understanding neural memory encoding. Additionally, potential applications in neuromorphic computing and biologically plausible AI architectures will be highlighted.
Additional resources: