A Modular Biological Neural Network-Based Neuro-Robotic System via Local Chemical Stimulation and Calcium Imaging

Abstract

Embodying in vitro biological neural networks (BNNs) with robots to explore the rise of intelligence in these simpler models and to endow robots with biological intelligence has been attracting increasing attention in the fields of neuroscience and robotics. However, current research suffers from unstable sensory-motor mapping due to the random wiring of neurons seeded on multi-electrode arrays (MEAs). Therefore, here we propose a modular BNN (mBNN)-based neuro-robotic system via local chemical stimulation and calcium recording. In this system, reliable evoked sensory-motor mapping (success rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt; $</tex-math></inline-formula> 89%) from the sensory to the motor area in the mBNN was demonstrated. It is achieved in the mBNNs by combining global chemical modulation (for suppressing spontaneous signal transmission) and local chemical stimulation (for inducing the evoked signal transmission). The neural signals of the motor area of the BNN are recorded by calcium imaging, analyzed, and decoded to control the motion state of the mobile robot in real-time. The sensory signals of the robot are encoded and transmitted to the sensory area of the BNN, closing the loop. This system presents a platform to investigate how information is processed and transmitted in mBNNs, and also to examine the influence of local and global chemical modulation on within-network signal transmission.