Chen, Z., Sun, T., et al. (2024).
2022 International Conference on
Advanced Robotics and Mechatronics (ICARM),
1093–1098.
Abstract
Bio-inspired construction of modular biological neural networks (BNNs) is gaining attention due to their innate stable inter-modular signal transmission ability, which is thought to underlying the emergence of biological intelligence. However, the complicated, laborious fabrication of BNNs with structural and functional connectivity of interest in vitro limits the further exploration of embodied intelligence. In this work, we propose a modular spiking neural network (SNN)-based neuro-robotic system by concurrently running SNN modeling and robot simulation. We show that the modeled mSNNs present complex calcium dynamics resembling mBNNs. In particular, spontaneous periodic network-wide bursts were observed in the mSNN, which could be further suppressed partially or completely with global chemical modulation. Moreover, we demonstrate that after complete suppression, intermodular signal transmission can still be evoked reliably via local stimulation. Therefore, the modeled mSNNs could either achieve reliable trans-modular signal transmission or add adjustable false-positive noise signals (spontaneous bursts). By interconnecting the modeled mSNNs with the simulated mobile robot, active obstacle avoidance and target tracking can be achieved. We further show that spontaneous noise impairs robot performance, which indicates the importance of suppressing spontaneous burst activities of modular networks for the reliable execution of robot tasks. The proposed neuro-robotic system embodies spiking neural networks with a mobile robot to interact with the external world, which paves the way for exploring the arising of more complex biological intelligence.
Here are some thoughts:
This paper is pretty wild. These researchers wanted to create an AI that simulates human brain activity embodied within a simulated mobile robot. The AI simulates calcium spiking in the brain, and the AI modules apparently communicate with each other. Quieting the spiking made the AI simulated robotic system more efficient. Here are some thoughts:
Cognitive neuroscience seeks to uncover how neural activity gives rise to perception, decision-making, and behavior, often by studying the dynamics of brain networks. This research contributes significantly to that goal by modeling modular spiking neural networks (mSNNs) that replicate key features of biological neural networks, including spontaneous network bursts and inter-modular communication. These modeled networks demonstrate how structured neural activity can support reliable signal transmission, a fundamental aspect of cognitive processing. Importantly, they also allow for controlled manipulation of network states—such as through global chemical modulation—which provides a way to study how noise or spontaneous activity affects information processing.
From an ethical standpoint, this research presents a valuable alternative to invasive or in vitro biological experiments. Traditional studies involving living neural tissue raise ethical concerns regarding animal use and the potential for suffering. By offering a synthetic yet biologically plausible model, this work reduces reliance on such methods while still enabling detailed exploration of neural dynamics. Furthermore, it opens new avenues for non-invasive experimentation in cognitive and clinical domains, aligning with ethical principles that emphasize minimizing harm and maximizing scientific benefit.