# Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2005.11633 · HTML: https://mlsanity.com/paper/2005.11633/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 30 of 43)

- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Temporal-wise Attention Spiking Neural Networks for Event Streams Classification](https://mlsanity.com/paper/2107.11711/) (2021)
- [Sparse Spiking Gradient Descent](https://mlsanity.com/paper/2105.08810/) (2021)
- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks](https://mlsanity.com/paper/2308.06787/) (2023)
- [STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks](https://mlsanity.com/paper/2210.05241/) (2022)
- [Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics](https://mlsanity.com/paper/2209.11741/) (2022)
- [SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation](https://mlsanity.com/paper/2308.15122/) (2023)
- [Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles](https://mlsanity.com/paper/2302.11618/) (2023)
- [Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation](https://mlsanity.com/paper/2410.11488/) (2024)
- [Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate](https://mlsanity.com/paper/2310.14982/) (2023)
- [MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition](https://mlsanity.com/paper/2404.10210/) (2024)
- [DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement](https://mlsanity.com/paper/2408.07388/) (2024)
- [Relational representation learning with spike trains](https://mlsanity.com/paper/2205.09140/) (2022)
- [SpikeCLIP: A Contrastive Language-Image Pretrained Spiking Neural Network](https://mlsanity.com/paper/2310.06488/) (2023)
- [Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons](https://mlsanity.com/paper/2311.11390/) (2023)
- [The Role of Temporal Hierarchy in Spiking Neural Networks](https://mlsanity.com/paper/2407.18838/) (2024)
- [Stabilizing Spiking Neuron Training](https://mlsanity.com/paper/2202.00282/) (2022)
- [GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL](https://mlsanity.com/paper/2404.15597/) (2024)
- [GazeSCRNN: Event-based Near-eye Gaze Tracking using a Spiking Neural Network](https://mlsanity.com/paper/2503.16012/) (2025)
- [Dual-Path Enhancements in Event-Based Eye Tracking: Augmented Robustness and Adaptive Temporal Modeling](https://mlsanity.com/paper/2504.09960/) (2025)
- [A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation](https://mlsanity.com/paper/2404.17335/) (2024)
- [Modular Growth of Hierarchical Networks: Efficient, General, and Robust Curriculum Learning](https://mlsanity.com/paper/2406.06262/) (2024)
- [Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing](https://mlsanity.com/paper/2407.06452/) (2024)
- [Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment](https://mlsanity.com/paper/2501.15925/) (2025)
- [Encrypted Internet traffic classification using a supervised Spiking Neural Network](https://mlsanity.com/paper/2101.09818/) (2021)
- [Astrocytes as a mechanism for meta-plasticity and contextually-guided network function](https://mlsanity.com/paper/2311.03508/) (2023)
- [Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks](https://mlsanity.com/paper/2404.14024/) (2024)
- [Energy-Efficient Spiking Recurrent Neural Network for Gesture Recognition on Embedded GPUs](https://mlsanity.com/paper/2408.12978/) (2024)

## Abstract (arXiv)

The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in compelling areas. Here, for sequential and streaming tasks, we demonstrate how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity. From this, we calculate a >100x energy improvement for our SRNNs over classical RNNs on the harder tasks. To achieve this, we model standard and adaptive multiple-timescale spiking neurons as self-recurrent neural units, and leverage surrogate gradients and auto-differentiation in the PyTorch Deep Learning framework to efficiently implement backpropagation-through-time, including learning of the important spiking neuron parameters to adapt our spiking neurons to the tasks.