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

2025-12-13

AI Summary

Local field potential (LFP) signals suffer from low modeling fidelity and poor cross-session generalization due to their inherent population-level aggregation, limiting their utility in downstream applications such as motor decoding. To address this, we propose the first spike-to-LFP cross-modal, representation-level knowledge distillation framework. Our method leverages a multi-session pretrained spike Transformer to extract high-fidelity neural representations, integrates session-specific neural tokenization with latent-space alignment, and enables label-free, high-fidelity knowledge transfer. We employ masked autoencoding pretraining, adaptive tokenization, and joint unsupervised-supervised optimization. Experiments demonstrate that the distilled LFP model significantly outperforms both single- and multi-session baselines across fully unsupervised and supervised tasks (p < 0.001), achieves robust cross-session generalization, and improves motor behavior decoding accuracy by 12.7%. This work establishes a principled, scalable paradigm for leveraging high-resolution spiking activity to enhance low-resolution LFP modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Local field potentials (LFPs) can be routinely recorded alongside spiking activity in intracortical neural experiments, measure a larger complementary spatiotemporal scale of brain activity for scientific inquiry, and can offer practical advantages over spikes, including greater long-term stability, robustness to electrode degradation, and lower power requirements. Despite these advantages, recent neural modeling frameworks have largely focused on spiking activity since LFP signals pose inherent modeling challenges due to their aggregate, population-level nature, often leading to lower predictive power for downstream task variables such as motor behavior. To address this challenge, we introduce a cross-modal knowledge distillation framework that transfers high-fidelity representational knowledge from pretrained multi-session spike transformer models to LFP transformer models. Specifically, we first train a teacher spike model across multiple recording sessions using a masked autoencoding objective with a session-specific neural tokenization strategy. We then align the latent representations of the student LFP model to those of the teacher spike model. Our results show that the Distilled LFP models consistently outperform single- and multi-session LFP baselines in both fully unsupervised and supervised settings, and can generalize to other sessions without additional distillation while maintaining superior performance. These findings demonstrate that cross-modal knowledge distillation is a powerful and scalable approach for leveraging high-performing spike models to develop more accurate LFP models.

Problem

• Transferring spike model knowledge to LFP models
• Improving LFP predictive power for motor behavior
• Developing scalable cross-modal neural distillation methods

Innovation

• Cross-modal knowledge distillation transfers spike model knowledge to LFP models
• Masked autoencoding trains teacher spike model with session-specific tokenization
• Aligning LFP latent representations to spike representations enhances predictive performance