Waves, bumps, and patterns in neural field theories
Biological Cybernetics
Stationary Bumps in Networks of Spiking Neurons
Neural Computation
The high-conductance state of cortical networks
Neural Computation
Journal of Computational Neuroscience
Dynamics of feature categorization
Neural Computation
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We investigate the generation and annihilation of persistent localized activity states, so-called bumps, in response to transient spatiotemporal external input in a two-population neural-field model of the Wilson-Cowan type. Such persistent cortical states have been implicated as a biological substrate for short-term working memory, that is, the ability to store stimulus-related information for a few seconds and discard it once it is no longer relevant. In previous studies of the same model it has been established that the stability of bump states hinges on the relative inhibitory constant @t, i.e., the ratio of the time constants governing the dynamics of the inhibitory and excitatory populations: persistent bump states are typically only stable for values of @t smaller than a critical value @t"c"r. We find here that @t is also a key parameter determining whether a transient input can generate a persistent bump state (in the regime where @t