Biological neuromorphic computing using brain organoids

In recent years, the demand for computing power has surged with the rapid evolution of artificial intelligence (AI), including machine learning and artificial neural network models. However, the current state of computing hardware faces challenges related to energy efficiency and processing power, particularly when tasked with running complex models. In response to these challenges, neuromorphic computing systems, drawing inspiration from the structure and function of the human brain, are undergoing development. One avenue of exploration involves leveraging human brain organoids—three-dimensional brain-like tissues derived from human pluripotent stem cells. These organoids can replicate certain aspects of the human brain's structure and function. In this context, we present the development of a biological neuromorphic computing system by integrating conventional computing hardware with a human brain organoid. Our approach involves implementing the organoid within a reservoir computing framework, a type of artificial neural network. The organoid serves as a dynamic physical reservoir, adept at capturing and processing information based on a sequence of inputs. Silicon computer hardware is employed for the input and output layers, with the output layer trained to interpret the reservoir layer's output, enabling predictions or classifications from the original input data. To showcase the versatility of this system, we demonstrated its application in speech recognition and the prediction of nonlinear equations. This innovative integration of traditional computing and organic neural elements holds promise for advancing biocomputing, brain-machine interfaces, and translational medicine. The biological neuromorphic computing system presented herein opens new avenues for exploration, offering potential insights into the synergy between artificial and biological systems.