# Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks

arXiv: https://arxiv.org/abs/2408.02961 · HTML: https://mlsanity.com/paper/2408.02961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Training Deep Spiking Neural Networks using Backpropagation](https://mlsanity.com/paper/1608.08782/) (2016)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks](https://mlsanity.com/paper/2303.04347/) (2023)
- [STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition](https://mlsanity.com/paper/1710.04734/) (2017)
- [Pruning of Deep Spiking Neural Networks through Gradient Rewiring](https://mlsanity.com/paper/2105.04916/) (2021)
- [Sparse Spiking Gradient Descent](https://mlsanity.com/paper/2105.08810/) (2021)
- [AutoSNN: Towards Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2201.12738/) (2022)
- [Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines](https://mlsanity.com/paper/1511.04484/) (2015)
- [Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks](https://mlsanity.com/paper/2104.12528/) (2021)
- [Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection](https://mlsanity.com/paper/1910.09993/) (2019)
- [Making BREAD: Biomimetic strategies for Artificial Intelligence Now and in the Future](https://mlsanity.com/paper/1812.01184/) (2018)

## Abstract (arXiv)

Despite basic differences between Spiking Neural Networks (SNN) and Artificial Neural Networks (ANN), most research on SNNs involve adapting ANN-based methods for SNNs. Pruning (dropping connections) and quantization (reducing precision) are often used to improve energy efficiency of SNNs. These methods are very effective for ANNs whose energy needs are determined by signals transmitted on synapses. However, the event-driven paradigm in SNNs implies that energy is consumed by spikes. In this paper, we propose a new synapse model whose weights are modulated by Interspike Intervals (ISI) i.e. time difference between two spikes. SNNs composed of this synapse model, termed ISI Modulated SNNs (IMSNN), can use gradient descent to estimate how the ISI of a neuron changes after updating its synaptic parameters. A higher ISI implies fewer spikes and vice-versa. The learning algorithm for IMSNNs exploits this information to selectively propagate gradients such that learning is achieved by increasing the ISIs resulting in a network that generates fewer spikes. The performance of IMSNNs with dense and convolutional layers have been evaluated in terms of classification accuracy and the number of spikes using the MNIST and FashionMNIST datasets. The performance comparison with conventional SNNs shows that IMSNNs exhibit upto 90% reduction in the number of spikes while maintaining similar classification accuracy.