Guided Self-organization in Indirectly Encoded and Evolving Topographic Maps

An important phenomenon seen in many areas of biological brains and recently in deep learning architectures is a process known as self-organization. For example, in the primary visual cortex, color and orientation maps develop based on lateral inhibitory connec-tivity patterns and Hebbian learning dynamics. These topographic maps, which are found in all sensory systems, are thought to be a key factor in enabling abstract cognitive representations. This pa-per shows for the first time that the Hypercube-based NeuroEvo-lution of Augmenting Topologies (HyperNEAT) method can be seeded to begin evolution with such lateral connectivity, enabling genuine self-organizing dynamics. The proposed approach draws on HyperNEAT’s ability to generate a pattern of weights across the connectivity of an artificial neural network (ANN) based on a function of its geometry. Validating this approach, the afferent weights of an ANN self-organize in this paper to form a genuine to-pographic map of the input space for a simple line orientation task. Most interestingly, this seed can then be evolved further, providing a method to guide the self-organization of weights in a specific way, much as evolution likely guided the self-organizing trajectories of biological brains