# Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling arXiv: https://arxiv.org/abs/2512.12461 ยท HTML: https://mlsanity.com/paper/2512.12461/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Distillation for spiking neural networks, one of 12 directions in Knowledge distillation. - Builds on 31 of its 86 references that are in the map. - First posted on arXiv 13 Dec 2025. ## Placed in - Knowledge distillation / [Distillation for spiking neural networks](https://mlsanity.com/distill/distillation-for-spiking-neural-networks/) ## Builds on (most cited first, 30 of 31) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021) - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019) - [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022) - [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022) - [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019) - [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014) - [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016) - [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016) - [Cross Modal Distillation for Supervision Transfer](https://mlsanity.com/paper/1507.00448/) (2015) - [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022) - [BrainBERT: Self-supervised representation learning for intracranial recordings](https://mlsanity.com/paper/2302.14367/) (2023) - [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023) - [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021) - [TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders](https://mlsanity.com/paper/2303.00320/) (2023) - [Linear dynamical neural population models through nonlinear embeddings](https://mlsanity.com/paper/1605.08454/) (2016) - [Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution](https://mlsanity.com/paper/2407.14668/) (2024) - [Population Transformer: Learning Population-level Representations of Neural Activity](https://mlsanity.com/paper/2406.03044/) (2024) - [STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer](https://mlsanity.com/paper/2206.04727/) (2022) - [Targeted Neural Dynamical Modeling](https://mlsanity.com/paper/2110.14853/) (2021) - [Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity](https://mlsanity.com/paper/2111.02338/) (2021) - [Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers](https://mlsanity.com/paper/2206.06131/) (2022) - [Neural decoding from stereotactic EEG: accounting for electrode variability across subjects](https://mlsanity.com/paper/2411.10458/) (2024) - [Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series](https://mlsanity.com/paper/2111.02922/) (2021) - [Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks](https://mlsanity.com/paper/1907.00650/) (2019) - [Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data](https://mlsanity.com/paper/2310.03111/) (2023) - [BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data](https://mlsanity.com/paper/2509.18627/) (2025) - [Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1901.00708/) (2019) - [Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data](https://mlsanity.com/paper/2509.18507/) (2025) ## Built on by (most cited first, 3 of 3) - [Cross-Subject Modeling for Widefield Calcium Imaging via Atlas-Aligned Spatiotemporal Tokenization](https://mlsanity.com/paper/2607.09754/) (2026) - [Nonlinear Dynamical Modeling of Human Intracranial Brain Activity with Flexible Inference](https://mlsanity.com/paper/2512.22785/) (2025) - [REALM: Retrospective Encoder Alignment for LFP Modeling](https://mlsanity.com/paper/2605.14867/) (2026) ## Abstract (arXiv) 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.