In this paper, we propose an autonomous spiking neural network model for decision making. The model is an expansion of the basal ganglia circuitry with automatic environment perception, which constructs environmental states automatically from image inputs. The work in this paper has the following contributions: (1) In our model, the simplified Hodgkin-Huxley computing model is developed to achieve calculation efficiency closed to the LIF model and is used to obtain and test the ionic level properties in cognition. (2) A spike based motion perception mechanism is proposed to extract key elements for learning process from raw pixels without large amount of training. We apply our model in the “flappy bird” game and it play well after dozens of trainings. The model gets similar learning performance with human at the start of training. Besides, our model simulates cognitive defects when blocking some of sodium or potassium ion channels in the Hodgkin-Huxley model and this is an exploration of cognition deep into ionic level