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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The authors present a model of persistent activity in neural networks as typically observed in working memory tasks and modeled as attractor dynamics. The authors note the shortcomings of existing models, namely the reliance on implausible global excitatory or inhibitory signals to reset the network dynamics after settling into an attractor state and the superfluousness of such a stable attractor, since in working memory tasks, the dynamics need only persistent as long as the task requires, not indefinitely. In the proposed model, these issues are confronted by incorporating short term synaptic facilitation and depression into a network model. Using a mean-field approach, the authors identify stable fixed points of the rate dynamics and how these fixed-points change as a function of network connectivity and timescale parameters.