Robot Learning Method Based Spike Neuron Model

With the development of robot technology gradually extends from the industry to the services, compared with the structured environment of industrial robots, the environment of service robots is more complex, so the requirements of service robots is higher. Based on the National Natural Science Foundation project background, this paper learns from biology to study the methods of intelligent robots, with high research value. The main contents include:(1) Based on the analysis and comparison of the third generation and the previous neuron model, select the spike neuron model which is suitable for this paper. Simulate three representative neuronal spike neuron models and compare their advantages and disadvantages, select the Izhikevich model to achieve a higher degree of authenticity in the biology with less complexity. Focused on the Izhikevich model, simulate six common spike models, study the role of each parameter in each model.(2) study the integration of neural population spikes in time and space, analyze the simulation results to find out how the neuron number effects on the postsynaptic neuron. By changing the presynaptic neuron stimulation interval, produce a peak with a large input (current), study the effects on postsynaptic neurons of the space of external stimuli time,simulation results show that that the time interval is too large,postsynaptic neurons can not produce spikes. Study on the characteristics of dopamine activity in the conditioned reflex process and the effect on the regulation of synaptic plasticity. Investigate the regulation of LTD dopamine LTP with using the Izhikevich model, research the role of dopamine in the learning process with the simulation, explore the relationship between synaptic strength with the time delay in dopamine release.(3) In simbad simulation platform, using the thought of reinforcement learning to apply the spike neuron model to the robot learning method. Design the Robot maze experiment, comply the simulation program, improve the learning ability of the robot with multiple simulation training, at last,The simulation results show the effectiveness of this method..(4) Study the factors influencing the robot learning ability by changing the complexity of the maze and the number of neurons constructed the sensors and controllers. Verify the impact of the time delay on the robot learning ability by using this experiment.The relevant research results in this paper has a positive significance for the future development of robot with higher intelligence and biological basis.