# Feedforward Architectures Driven by Inhibitory Interactions

## TL;DR

This paper shows that inhibitory neurons can actively drive feedforward activity in neural networks, expanding the understanding of how information transmission occurs beyond traditional excitatory-driven models.

## Contribution

It introduces a novel network architecture where inhibitory neurons actively shape feedforward dynamics, even with random excitatory connectivity.

## Key findings

- Inhibitory neurons can actively induce feedforward activity.
- Random excitatory connectivity can support directed information flow.
- Inhibitory roles are more diverse than traditionally assumed.

## Abstract

Directed information transmission is paramount for many social, physical, and biological systems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neurons play only a stabilizing role for the network dynamics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the diversity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random. This is achieved by endowing inhibitory nodes with a more active role in the network. Our findings demonstrate that apparent feedforward activity can be caused by a much broader network-architectural basis than often assumed.