# Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks

arXiv: https://arxiv.org/abs/2105.14677 · HTML: https://mlsanity.com/paper/2105.14677/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 73 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 15 of 15)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Exploring Generalization in Deep Learning](https://mlsanity.com/paper/1706.08947/) (2017)
- [Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers](https://mlsanity.com/paper/1811.04918/) (2018)
- [A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks](https://mlsanity.com/paper/1901.06053/) (2019)
- [Generalization in Deep Learning](https://mlsanity.com/paper/1710.05468/) (2017)
- [The Heavy-Tail Phenomenon in SGD](https://mlsanity.com/paper/2006.04740/) (2020)
- [Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks](https://mlsanity.com/paper/2006.09313/) (2020)
- [Comparing Dynamics: Deep Neural Networks versus Glassy Systems](https://mlsanity.com/paper/1803.06969/) (2018)
- [Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise](https://mlsanity.com/paper/2002.05685/) (2020)
- [Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks](https://mlsanity.com/paper/1804.00227/) (2018)
- [STDP-based spiking deep convolutional neural networks for object recognition](https://mlsanity.com/paper/1611.01421/) (2016)
- [Can SGD Learn Recurrent Neural Networks with Provable Generalization?](https://mlsanity.com/paper/1902.01028/) (2019)
- [ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks](https://mlsanity.com/paper/1703.07655/) (2017)
- [Anomalous diffusion dynamics of learning in deep neural networks](https://mlsanity.com/paper/2009.10588/) (2020)
- [Stochastic Models of Neural Synaptic Plasticity](https://mlsanity.com/paper/2010.08195/) (2020)

## Built on by (most cited first, 8 of 8)

- [Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN](https://mlsanity.com/paper/2403.03409/) (2024)
- [Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles](https://mlsanity.com/paper/2302.11618/) (2023)
- [Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction](https://mlsanity.com/paper/2304.04697/) (2023)
- [Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2403.12462/) (2024)
- [Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing](https://mlsanity.com/paper/2407.06452/) (2024)
- [Functional Connectome: Approximating Brain Networks with Artificial Neural Networks](https://mlsanity.com/paper/2211.12935/) (2022)
- [A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks](https://mlsanity.com/paper/2412.07243/) (2024)
- [FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning](https://mlsanity.com/paper/2504.01257/) (2025)

## Abstract (arXiv)

A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning processes using the Hausdorff dimension of the trajectories of the learning algorithm. The paper analyzes the effects of STDP learning models and associated hyper-parameters on the generalizability properties of an SNN. The analysis is used to develop a Bayesian optimization approach to optimize the hyper-parameters for an STDP model for improving the generalizability properties of an SNN.