Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling

This article likely presents a novel approach to improve the modeling of Local Field Potentials (LFPs) using spike data, leveraging knowledge distillation techniques across different data modalities. The use of 'cross-modal' suggests integrating information from different sources (e.g., spikes and LFPs) to enhance the model's performance. The focus on 'knowledge distillation' implies transferring knowledge from a more complex or accurate model to a simpler one, potentially for efficiency or interpretability.