# Training dynamically balanced excitatory-inhibitory networks

## TL;DR

This paper presents a method for constructing biologically plausible excitatory-inhibitory neural networks that are balanced and capable of complex temporal processing, using a target-based approach with online constrained optimization.

## Contribution

It introduces a novel training approach combining target-based methods with online constrained optimization to build balanced neural networks obeying Dale's law.

## Key findings

- Networks can produce complex temporal patterns.
- Networks successfully solve input-output tasks.
- Biological features like Dale's law are preserved.

## Abstract

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.