The nervous system might 'orthogonalize' to discriminate.

It is still unclear how information is actually stored in biological neural networks. We propose here that information could be first orthogonalized and then stored. This could happen in a manner similar to how a set of vectors is transformed into a set of orthogonalized (i.e. mutually perpendicular) vectors. Orthogonalization may overcome the limits of conventional artificial networks, particularly the catastrophic interference caused by interference between stored inputs. The features needed to allow orthogonalization are common to biological networks, suggesting that it may be a common network mechanism. To illustrate this hypothesis, we characterize the underlying features that an archetypal biological network must have in order to perform orthogonalization, and point out that a number of actual networks show this archetypal network organization.