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.
In this paper a new methodology for action-oriented perception will be introduced. It is based on a previous
method that used Turing Patterns in CNNs for the arousal of "perceptual states" as representation of the
environmental condition. The emerging patterns were associated to codes which gave rise to learnable actions on
a moving robot. Recently the new paradigm of Winnerless Competition (WLC) was taken into consideration to
represent a suitable, bioinspired and efficient method to generate sequences of neural activations, strictly related
to the spatial-temporal activity of input sensors. This fascinating property was recently peculiarly measured
in the olfactory system, in particular in groups of neurons belonging to the insects' Antennal Lobe and to the
mammalians' Olfactory Bulb. Taking inspiration from these experimental results and from the analytical model
of the WLC, a cellular nonlinear model generating sequences of cell activation, representing the input pattern
at the sensory level, will be used in an action-oriented perception framework. In fact simulation results showed
the potentiality of the WLC approach to design dynamic networks for discrimination and classification, with
a potentially huge memory capacity. In the present manuscript the WLC principle, implemented in a network
of FitzHugh Nagumo neurons will be used within the whole framework for action-oriented perception, and the
results will be applied to a roving robot.
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