We present a universal building block for cognitive machines, called NeuroNavigator, inspired by theories of the hippocampus. The module is designed to fit both biological plausibility and constraints of forthcoming neuromorphic hardware. Its functions may range from spatial navigation to episodic memory retrieval. The goal of the present study of NeuroNavigator is to show the scalability of the model. The core of the architecture is based on our previously described model of hippocampal function and includes 3 layers (DG, CA3, CA1) of spiking neurons with noisy STDP synaptic connections among neighboring layers. The model is applied to a spatial navigation paradigm in a hierarchical virtual environment, the metrics of which need to be learned by exploration. The goal in each trial is set arbitrarily as any one of the previously seen objects or features. In order to navigate toward the goal, the agent needs to “imagine” previously performed available moves at the current location and select one of them, using the acquired spatial knowledge. This process controlled by NeuroNavigator is repeated until the goal is reached. Overall, the simulation results show robustness and scalability of the solution based on a biologically-inspired network of spiking neurons and STDP synapses.