Literally the first sentence in the Abstract

Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies.

However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles.

.... Reading on...

In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array.

By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework.

Spatiotemporal electrical stimulation means applying electricity to the hardware at different places in 3D space over time.

Nonlinear dynamics is an integral part of how the brain works but this term is extremely vast and makes it easy to copy.

Unsupervised learning is a type of machine learning that allows you to basically being able to categorize and filter signals without any training data to tell you how it should be done. AI is just automatic statistics at the end of the day.

For the "organoid" functional connectivity, there are statistical techniques that show what different parts of the brain are connected from making out the same electrical signal from different sensors at different parts of the brain.