# Position-wise optimizer: A nature-inspired optimization algorithm

arXiv: https://arxiv.org/abs/2204.05312 · HTML: https://mlsanity.com/paper/2204.05312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (2019)
- [An Approximate Backpropagation Learning Rule for Memristor Based Neural Networks Using Synaptic Plasticity](https://mlsanity.com/paper/1511.07076/) (2015)

## Abstract (arXiv)

The human nervous system utilizes synaptic plasticity to solve optimization problems. Previous studies have tried to add the plasticity factor to the training process of artificial neural networks, but most of those models require complex external control over the network or complex novel rules. In this manuscript, a novel nature-inspired optimization algorithm is introduced that imitates biological neural plasticity. Furthermore, the model is tested on three datasets and the results are compared with gradient descent optimization.