Neuromorphic-inspired approaches seek to replicate the brain’s capabilities to create more efficient and intelligent AI systems. Within the context of natural language processing (NLP), in particular, these brain-like mechanisms can offer better cognitive modelling, energy consumption efficiency, and real-time processing. However, current NLP paradigms, primarily models based on traditional deep learning, have significant large computational processing overhead, adaptability, and efficiency concerns in terms of energy consumption, and are difficult to scale to edge and resource-constrained devices. To overcome these issues, we develop a Spiking Neural Networks-Based Language Encoding (SNN-LE) framework for brain-like NLP. In our SNN-LE framework, textual data is encoded into spike trains, which enable at least temporal, and possibly event-based processing, similar to neuronal activity in the human brain. SNN-LE has the capabilities to provide synaptic plasticity mechanisms where the learning of language patterns and usage can emerge without needing as significant amounts of paired labelled data. This neuromorphic-inspired approach allows for the possibility of avoiding redundant computations and enables real-time inference capabilities. With our SNN-LE framework we will demonstrate ideas on implementing it in a context-aware dialogue system, where it can show some or all selective efficiency and accuracy in processing and generating the response in a natural language. Consequently, we confirmed that these will lead to using less energy, faster inference speeds, and greater levels of adaptability than traditional deep learning NLP methods. These findings highlight the potential of neuromorphic approaches for developing scalable, brain-like AI systems in emerging applications.
Prerna Dusi (2026) studied this question.