  Neural activity in the brain exhibits correlated fluctuations that may
strongly influence the properties of neural population coding. However, how
such correlated neural fluctuations may arise from the intrinsic neural circuit
dynamics and subsequently affect the computational properties of neural
population activity remains poorly understood. The main difficulty lies in
resolving the nonlinear coupling between correlated fluctuations with the
overall dynamics of the system. In this study, we investigate the emergence of
synergistic neural population codes from the intrinsic dynamics of correlated
neural fluctuations in a neural circuit model capturing realistic nonlinear
noise coupling of spiking neurons. We show that a rich repertoire of spatial
correlation patterns naturally emerges in a bump attractor network and further
reveals the dynamical regime under which the interplay between differential and
noise correlations leads to synergistic codes. Moreover, we find that negative
correlations may induce stable bound states between two bumps, a phenomenon
previously unobserved in firing rate models. These noise-induced effects of
bump attractors lead to a number of computational advantages including enhanced
working memory capacity and efficient spatiotemporal multiplexing and can
account for a range of cognitive and behavioral phenomena related to working
memory. This study offers a dynamical approach to investigating realistic
correlated neural fluctuations and insights to their roles in cortical
computations.
