Learning and recognition are a fundamental process performed by animals, humans, robots and intelligent systems. Humans, for example, continually learn and recognize where they are in the world (place recognition), who is there with them (facial recognition) and what things are around them (object recognition). Recognition also plays a significant role in technology like smartphones, whether it be recognizing what you are saying (voice recognition) or what the consumer item in front of you is when using Google Goggles (object recognition). Google and other information aggregators perform recognition at a vast scale, recognizing and classifying billions of images in the cloud and house numbers in millions of kilometres of Google Streetview imagery. In security and surveillance, task-specific signatures (such as a specific person’s voice, a bomb-carrying back pack or a person’s face) must be automatically recognized amongst vast amounts of data. Because data storage and collection is expensive, it is necessary to build new techniques to store and process data.
This project combines modelling of the spatial memory encoding system in the mammalian brain with machine learning techniques to achieve new levels of data storage compression for learning and recall systems.
We combine neuroscience and robotic algorithms to create new technologies for compressing and storing data such as navigational maps.