Learning of 2 DOF robotic arm using integrated architecture of neural network and Spike Timing Dependent Plasticity

Abstract

In this paper, an integrated architecture of spiking neural network is used for learning of 2 DOF robotic arm. Brain architecture is consisting of 6 different areas that have different tasks. Two sensors are used to detect the target position and send the signals to sensory neurons. As an integrated architecture, all of the sensory neurons are connected to all motor neurons at the beginning of the process. The neural network is trained to learn a specific task using spike timing-dependent plasticity. Simultaneous sensing of target and movement of robot toward target result in unsupervised learning which is useful for learning of robots in unknown environments. To remove the effects of random inputs, the experiment is repeated six times. Through the learning process, the synaptic weights are changed. The connection between left sensory neurons and Flexor motor neurons also the connection between right sensory neurons and Extensor motor neurons are increased while other connections are weakened. The results show that the spiking neural network is effective in controlling the robot's motion.