Zhao Feifei, Zeng Yi, Xu Bo
Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Beijing, China.
Front Neurorobot. 2018 Sep 11;12:56. doi: 10.3389/fnbot.2018.00056. eCollection 2018.
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.
决策是各种在自然界中生存的动物物种至关重要的认知功能，也是智能体的一项基本能力。为了在理解类人决策的计算机制方面更进一步，本文提出了一种受大脑启发的决策脉冲神经网络（BDM-SNN），并将其应用于智能体的决策任务。本文做出了以下贡献：（1）从连接组和功能两个角度，使用脉冲神经网络（SNN）对人类决策神经回路进行建模。（2）所提出的模型结合了多巴胺和脉冲时间依赖可塑性（STDP）机制来调节网络学习过程，这体现了更多的生物学启发。（3）该模型考虑了前额叶皮质（PFC）子区域之间的相互作用对加速学习过程的影响。（4）所提出的模型可以很容易地应用于智能体的决策任务，例如无人机飞过窗户和无人机避开障碍物。实验结果支持了该模型的有效性。与传统强化学习和现有的受生物学启发的方法相比，我们的方法包含更多受生物学启发的机制原理，具有更高的准确性且速度更快。
