An activity-dependent hierarchical clustering method for sensory organization

Biological and artificial sensory systems share many features and functionalities in common. One shared challenge is the management setup and maintenance of sensory topological information. In the case of a massive artificial sensory receptor array, this is an extremely complex problem. Biological sensory receptor arrays, such as the visual or tactile system, face the same problem and have found excellent solutions by implementing processes of sensory organization. Not only can biological sensory organization initiate the topological data construction, it can deal with growing systems and repair damaged ones. Importantly, it can use the patterned activity of sensory receptors to extract topological relationships. Using inspiration from these biological processes, we propose an activity-dependent clustering method for organizing large arrays of artificial sensory receptors. We present an algorithm that proceeds hierarchically by building a quadtree description of sensory organization and possesses many qualities of its biological counterpart, namely it can operate autonomously, it uses the patterned activity of sensory receptors and it is capable of supporting growth and repair.