  A model of an Ant System where ants are controlled by a spiking neural
circuit and a second order pheromone mechanism in a foraging task is presented.
A neural circuit is trained for individual ants and subsequently the ants are
exposed to a virtual environment where a swarm of ants performed a resource
foraging task. The model comprises an associative and unsupervised learning
strategy for the neural circuit of the ant. The neural circuit adapts to the
environment by means of classical conditioning. The initially unknown
environment includes different types of stimuli representing food and obstacles
which, when they come in direct contact with the ant, elicit a reflex response
in the motor neural system of the ant: moving towards or away from the source
of the stimulus. The ants are released on a landscape with multiple food
sources where one ant alone would have difficulty harvesting the landscape to
maximum efficiency. The introduction of a double pheromone mechanism yields
better results than traditional ant colony optimization strategies. Traditional
ant systems include mainly a positive reinforcement pheromone. This approach
uses a second pheromone that acts as a marker for forbidden paths (negative
feedback). This blockade is not permanent and is controlled by the evaporation
rate of the pheromones. The combined action of both pheromones acts as a
collective stigmergic memory of the swarm, which reduces the search space of
the problem. This paper explores how the adaptation and learning abilities
observed in biologically inspired cognitive architectures is synergistically
enhanced by swarm optimization strategies. The model portraits two forms of
artificial intelligent behaviour: at the individual level the spiking neural
network is the main controller and at the collective level the pheromone
distribution is a map towards the solution emerged by the colony.
