# TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling

arXiv: https://arxiv.org/abs/2308.13250 · HTML: https://mlsanity.com/paper/2308.13250/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2023.

## Built on by (most cited first, 21 of 21)

- [Rethinking Spiking Neural Networks from an Ensemble Learning Perspective](https://mlsanity.com/paper/2502.14218/) (2025)
- [Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks](https://mlsanity.com/paper/2406.00405/) (2024)
- [Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks](https://mlsanity.com/paper/2411.04760/) (2024)
- [TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting](https://mlsanity.com/paper/2503.05108/) (2025)
- [Efficient Spiking Point Mamba for Point Cloud Analysis](https://mlsanity.com/paper/2504.14371/) (2025)
- [Memory-Free and Parallel Computation for Quantized Spiking Neural Networks](https://mlsanity.com/paper/2503.00040/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [AR-LIF: Adaptive reset leaky integrate-and-fire neuron for spiking neural networks](https://mlsanity.com/paper/2507.20746/) (2025)
- [Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling](https://mlsanity.com/paper/2509.17186/) (2025)
- [FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning](https://mlsanity.com/paper/2504.01257/) (2025)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning](https://mlsanity.com/paper/2510.04098/) (2025)
- [Error Amplification Limits ANN-to-SNN Conversion in Continuous Control](https://mlsanity.com/paper/2601.21778/) (2026)
- [Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients](https://mlsanity.com/paper/2605.27412/) (2026)
- [Neural Network Modeling of Microstructure Complexity Using Digital Libraries](https://mlsanity.com/paper/2501.18189/) (2025)
- [Spiking Neural Network for Intra-cortical Brain Signal Decoding](https://mlsanity.com/paper/2504.09213/) (2025)
- [Revisiting Reset Mechanisms in Spiking Neural Networks for Sequential Modeling: Specialized Discretization for Binary Activated RNN](https://mlsanity.com/paper/2504.17751/) (2025)
- [Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields](https://mlsanity.com/paper/2507.23033/) (2025)
- [SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization](https://mlsanity.com/paper/2508.10913/) (2025)
- [Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks](https://mlsanity.com/paper/2603.20687/) (2026)
- [DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement](https://mlsanity.com/paper/2606.05911/) (2026)

## Abstract (arXiv)

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. To address this challenge, we propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF. The proposed model incorporates carefully designed somatic and dendritic compartments that are tailored to facilitate learning long-term temporal dependencies. Furthermore, a theoretical analysis is provided to validate the effectiveness of TC-LIF in propagating error gradients over an extended temporal duration. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, and high energy efficiency of the proposed TC-LIF model. Therefore, this work opens up a myriad of opportunities for solving challenging temporal processing tasks on emerging neuromorphic computing systems. Our code is publicly available at https://github.com/ZhangShimin1/TC-LIF.