# Co-learning synaptic delays, weights and adaptation in spiking neural networks

arXiv: https://arxiv.org/abs/2311.16112 · HTML: https://mlsanity.com/paper/2311.16112/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks](https://mlsanity.com/paper/1910.07407/) (2019)
- [Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks](https://mlsanity.com/paper/2303.04347/) (2023)
- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Attention Spiking Neural Networks](https://mlsanity.com/paper/2209.13929/) (2022)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks](https://mlsanity.com/paper/2302.13939/) (2023)
- [GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2210.13768/) (2022)
- [Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings](https://mlsanity.com/paper/2306.17670/) (2023)
- [Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP](https://mlsanity.com/paper/1904.01908/) (2019)
- [Axonal Delay As a Short-Term Memory for Feed Forward Deep Spiking Neural Networks](https://mlsanity.com/paper/2205.02115/) (2022)
- [Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification](https://mlsanity.com/paper/2211.04297/) (2022)
- [Adaptive Axonal Delays in feedforward spiking neural networks for accurate spoken word recognition](https://mlsanity.com/paper/2302.08607/) (2023)
- [Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training](https://mlsanity.com/paper/2306.06237/) (2023)
- [Bioinspired Visual Motion Estimation](https://mlsanity.com/paper/1511.00096/) (2015)
- [Learning heterogeneous delays in a layer of spiking neurons for fast motion detection](https://mlsanity.com/paper/2307.14077/) (2023)

## Built on by (most cited first, 13 of 13)

- [The Role of Temporal Hierarchy in Spiking Neural Networks](https://mlsanity.com/paper/2407.18838/) (2024)
- [SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks](https://mlsanity.com/paper/2506.06374/) (2025)
- [A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2](https://mlsanity.com/paper/2510.13757/) (2025)
- [Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks](https://mlsanity.com/paper/2404.14024/) (2024)
- [Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization](https://mlsanity.com/paper/2409.15849/) (2024)
- [Three factor delay learning rules for spiking neural networks](https://mlsanity.com/paper/2601.00668/) (2026)
- [Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks](https://mlsanity.com/paper/2605.01291/) (2026)
- [Random Feature Spiking Neural Networks](https://mlsanity.com/paper/2510.01012/) (2025)
- [Delays in Spiking Neural Networks: A State Space Model Approach](https://mlsanity.com/paper/2512.01906/) (2025)
- [SpikingGamma: Surrogate-Gradient Free and Temporally Precise Online Training of Spiking Neural Networks with Smoothed Delays](https://mlsanity.com/paper/2602.01978/) (2026)
- [Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions](https://mlsanity.com/paper/2605.15355/) (2026)
- [Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition](https://mlsanity.com/paper/2608.30792/) (2026)
- [Latency and accuracy tradeoffs in Spiking Neural Networks](https://mlsanity.com/paper/2609.35260/) (2026)

## Abstract (arXiv)

Spiking neural networks (SNN) distinguish themselves from artificial neural networks (ANN) because of their inherent temporal processing and spike-based computations, enabling a power-efficient implementation in neuromorphic hardware. In this paper, we demonstrate that data processing with spiking neurons can be enhanced by co-learning the connection weights with two other biologically inspired neuronal features: 1) a set of parameters describing neuronal adaptation processes and 2) synaptic propagation delays. The former allows the spiking neuron to learn how to specifically react to incoming spikes based on its past. The trained adaptation parameters result in neuronal heterogeneity, which is found in the brain and also leads to a greater variety in available spike patterns. The latter enables to learn to explicitly correlate patterns that are temporally distanced. Synaptic delays reflect the time an action potential requires to travel from one neuron to another. We show that each of the co-learned features separately leads to an improvement over the baseline SNN and that the combination of both leads to state-of-the-art SNN results on all speech recognition datasets investigated with a simple 2-hidden layer feed-forward network. Our SNN outperforms the ANN on the neuromorpic datasets (Spiking Heidelberg Digits and Spiking Speech Commands), even with fewer trainable parameters. On the 35-class Google Speech Commands dataset, our SNN also outperforms a GRU of similar size. Our work presents brain-inspired improvements to SNN that enable them to excel over an equivalent ANN of similar size on tasks with rich temporal dynamics.