In this paper a new methodology for landmark navigation will be introduced. Either for animals or for artificial
agents, the whole problem of landmark navigation can be divided into two parts: first, the agent has to recognize,
from the dynamic environment, space invariant objects which can be considered as suitable landmarks for driving
the motion towards a goal position; second, it has to use the information on the landmarks to effectively navigate
within the environment. Here, the problem of determining landmarks has been addressed by processing the
external information through a spiking network with dynamic synapses plastically tuned by an STDP algorithm.
The learning processes establish correlations between the incoming stimuli, allowing the system to extract from
the scenario important features which can play the role of landmarks. Once established the landmarks, the
agent acquires geometric relationships between them and the goal position. This process defines the parameters
of a recurrent neural network (RNN). This in turn drives the agent navigation, filtering the information about
landmarks given within an absolute reference system (e.g the North). When the absolute reference is not available,
a safety mechanism acts to control the motion maintaining a correct heading. Simulation results showed the
potentiality of the proposed architecture: this is able to drive an agent towards the desired position in presence
of stimuli subject to noise and also in the case of partially obscured landmarks.