evolutionary algorithm

We show how a multi-agent system (ant colony) steered by Spiking Neural Networks establishes self-organization while foraging for food. We evolve the networks using a genetic algorithm and learning to learn. Our results depict emergent behavior, which we investigate.

Experimental evidence suggests that brain-state-specific neural mechanisms play a crucial role in integrating past and contextual knowledge with current sensory information, operating across multiple spatial and temporal scales. A new two-compartment spiking neuron model has been developed to support brain-state-specific learning, incorporating features such as apical amplification and isolation, and utilizing a piece-wise linear transfer function to make it suitable for large-scale bio-inspired artificial intelligence systems.