AUTHOR=Zhao Feifei , Zeng Yi , Xu Bo TITLE=A Brain-Inspired Decision-Making Spiking Neural Network and Its Application in Unmanned Aerial Vehicle JOURNAL=Frontiers in Neurorobotics VOLUME=12 YEAR=2018 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2018.00056 DOI=10.3389/fnbot.2018.00056 ISSN=1662-5218 ABSTRACT=

Decision-making is a crucial cognitive function for various animal species surviving in nature, and it is also a fundamental ability for intelligent agents. To make a step forward in the understanding of the computational mechanism of human-like decision-making, this paper proposes a brain-inspired decision-making spiking neural network (BDM-SNN) and applies it to decision-making tasks on intelligent agents. This paper makes the following contributions: (1) A spiking neural network (SNN) is used to model human decision-making neural circuit from both connectome and functional perspectives. (2) The proposed model combines dopamine and spike-timing-dependent plasticity (STDP) mechanisms to modulate the network learning process, which indicates more biological inspiration. (3) The model considers the effects of interactions among sub-areas in PFC on accelerating the learning process. (4) The proposed model can be easily applied to decision-making tasks in intelligent agents, such as an unmanned aerial vehicle (UAV) flying through a window and a UAV avoiding an obstacle. The experimental results support the effectiveness of the model. Compared with traditional reinforcement learning and existing biologically inspired methods, our method contains more biologically-inspired mechanistic principles, has greater accuracy and is faster.