# Training dynamically balanced excitatory-inhibitory networks

arXiv: https://arxiv.org/abs/1812.11424 · HTML: https://mlsanity.com/paper/1812.11424/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Input correlations impede suppression of chaos and learning in balanced rate networks](https://mlsanity.com/paper/2201.09916/) (2022)
- [A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning](https://mlsanity.com/paper/2304.06738/) (2023)
- [Learning fast changing slow in spiking neural networks](https://mlsanity.com/paper/2402.10069/) (2024)

## Abstract (arXiv)

The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networks composed of separate excitatory and inhibitory neurons obeying Dale's law presents a number of challenges. We show how a target-based approach, when combined with a fast online constrained optimization technique, is capable of building functional models of rate and spiking recurrent neural networks in which excitation and inhibition are balanced. Balanced networks can be trained to produce complicated temporal patterns and to solve input-output tasks while retaining biologically desirable features such as Dale's law and response variability.