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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 auto-associative memory in a rate-based neural network subject to a battery of biological plausible constraints. Previous models of auto-associative memory have failed to include several key features of real biological networks, namely an adherence to Dale's Law that neurons have a strictly excitatory or inhibitory effect on their projections and the observation that networks can encode memories without relying on units that simply respond at their saturation rate or respond in a binary manner. Memories are encoded in the network via synaptic modifications based on a gradient descent procedure, constrained using a recently published method for ensuring that the linearization of the dynamics around a dynamical system's fixed point is stable. The authors illustrate the effectiveness of their training procedure with simulations, noting that the trained fixed points exhibit slow network dynamics (i.e. they are close to being, but are not exactly, fixed points) and are stable, as desired.