Abstract
Many cognitive processes rely on the ability of the brain to hold sequences of
events in short-term memory. Recent studies have revealed that such memory can
be read out from the transient dynamics of a network of neurons. However, the
memory performance of such a network in buffering past information has been
rigorously estimated only in networks of linear neurons. When signal gain is
kept low, so that neurons operate primarily in the linear part of their response
nonlinearity, the memory lifetime is bounded by the square root of the network
size. In this work, I demonstrate that it is possible to achieve a memory
lifetime almost proportional to the network size, “an extensive memory
lifetime,” when the nonlinearity of neurons is appropriately used. The
analysis of neural activity revealed that nonlinear dynamics prevented the
accumulation of noise by partially removing noise in each time step. With this
error-correcting mechanism, I demonstrate that a memory lifetime of order 
can be achieved.