Neuromodulation based control of an autonomous robot in a safe cloud environment

In recent years, the advancement of neurobiological accurate simulation models and computer networking has resulted in new ways of implementing control systems on robotic structures. In this paper, we present a simulation of the mammalian brain based on the Izhikevich spiking neuron model that is implemented on a cloud computing platform. This model allows for description of neuron activity that is biologically realistic but computationally efficient to allow for large-scale simulation of thousands of neurons. The simulation can be used with graphics processing units (GPUs) using the concept of parallel computing which allow multiple calculations to be done simultaneously, greatly reducing simulation time. The spiking neuron simulation is used to add complexity and biological realism to a neuromodulation program in which the reward seeking properties of dopamine, the risk-adverse effects of serotonin, and the attention-focusing effects of the cholinergic (ACh) and noradrenergic (NE) systems are applied to a mobile robotic platform as it moves autonomously throughout an environment. In order to increase the amount of available computational power available to the robotic platform and to introduce the possibility of multiple networked platforms using the same code simulation base, a cloud computing structure is used to contain the spiking neural network simulation. This cloud computing platform is based on the open-source Robot Operating System (ROS) framework.¹.