Ultimately the development of the brain is guided genetically, by instructions from the DNA. However once the basic wiring pattern is in place, the brain continues to organize itself on the basis of the information flowing through it. The data-driven organization is essential for memory as well as feature extraction and a host of other important human capabilities.

Much of our knowledge of neural self-organization comes from studies in machine learning. Machines give us a friendly environment in which to test mathematical theories of data organization, because we can test various algorithms quickly and efficiently without the complex preparation involved in electrode penetration.

Currently, we understand only a little about human memory. Most of what we know about self organization pertains to the geometrical and statistical development of network wiring. What we know for sure, is that the Hebbian models of synaptic plasticity are inadequate to describe the human memory system. Memory is complex, different brain pathways subserve different functions and even the nature of episodic storage is poorly understood. However in the context of a neural information timeline, a lot of what we know about memory and self-organization starts to make sense.