# Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning

arXiv: https://arxiv.org/abs/2010.03140 · HTML: https://mlsanity.com/paper/2010.03140/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning](https://mlsanity.com/paper/2106.07854/) (2021)
- [Constraint Breeds Generalization: Temporal Dynamics as an Inductive Bias](https://mlsanity.com/paper/2512.23916/) (2025)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles, hence are playing critical roles in bridging the gap between artificial and natural neural networks. The spikes are the sparse signals describing the above-threshold event-based firing and under-threshold dynamic computation of membrane potentials, which give us an alternative uniformed and efficient way on both information representation and computation. Inspired from the biological network, where a finite number of meta neurons integrated together for various of cognitive functions, we proposed and constructed Meta-Dynamic Neurons (MDN) to improve SNNs for a better network generalization during spatio-temporal learning. The MDNs are designed with basic neuronal dynamics containing 1st-order and 2nd-order dynamics of membrane potentials, including the spatial and temporal meta types supported by some hyper-parameters. The MDNs generated from a spatial (MNIST) and a temporal (TIDigits) datasets first, and then extended to various other different spatio-temporal tasks (including Fashion-MNIST, NETtalk, Cifar-10, TIMIT and N-MNIST). The comparable accuracy was reached compared to other SOTA SNN algorithms, and a better generalization was also achieved by SNNs using MDNs than that without using MDNs.