# Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches

## TL;DR

This paper analyzes the electrophysiological behavior of in vitro neuronal networks to identify and manipulate their critical state, which is essential for optimizing their computational capabilities and understanding their health status.

## Contribution

It introduces a method to classify networks as critical or non-critical based on avalanche size distribution and demonstrates how to perturb networks into criticality by adjusting inhibition levels.

## Key findings

- Networks can be driven into criticality by increasing inhibition.
- Critical networks are better suited for computational tasks.
- Proper parameter selection is crucial for avalanche analysis.

## Abstract

In this work, we report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized---or supercritical---behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. This type of analysis is expected to enable the identification of networks that are well-suited for computation and the classification of networks as perturbed or healthy. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares.