# Convergence of a New Learning Algorithm

arXiv: https://arxiv.org/abs/2202.12829 · HTML: https://mlsanity.com/paper/2202.12829/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://mlsanity.com/paper/1711.00832/) (2017)
- [Scalable Object Detection using Deep Neural Networks](https://mlsanity.com/paper/1312.2249/) (2013)
- [Robust Face Recognition via Multimodal Deep Face Representation](https://mlsanity.com/paper/1509.00244/) (2015)

## Abstract (arXiv)

A new learning algorithm proposed by Brandt and Lin for neural network [1], [2] has been shown to be mathematically equivalent to the conventional back-propagation learning algorithm, but has several advantages over the backpropagation algorithm, including feedback-network-free implementation and biological plausibility. In this paper, we investigate the convergence of the new algorithm. A necessary and sufficient condition for the algorithm to converge is derived. A convergence measure is proposed to measure the convergence rate of the new algorithm. Simulation studies are conducted to investigate the convergence of the algorithm with respect to the number of neurons, the connection distance, the connection density, the ratio of excitatory/inhibitory synapses, the membrane potentials, and the synapse strengths.