Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation.
This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task.
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.
The brain uses population codes to form distributed, noise-tolerant representations of sensory and motor variables.