  Understanding how brain networks learn and manage multiple tasks
simultaneously is of interest in both neuroscience and artificial intelligence.
In this regard, a recent research thread in theoretical neuroscience has
focused on how recurrent neural network models and their internal dynamics
enact multi-task learning. To manage different tasks requires a mechanism to
convey information about task identity or context into the model, which from a
biological perspective may involve mechanisms of neuromodulation. In this
study, we use recurrent network models to probe the distinctions between two
forms of contextual modulation of neural dynamics, at the level of neuronal
excitability and at the level of synaptic strength. We characterize these
mechanisms in terms of their functional outcomes, focusing on their robustness
to context ambiguity and, relatedly, their efficiency with respect to packing
multiple tasks into finite size networks. We also demonstrate distinction
between these mechanisms at the level of the neuronal dynamics they induce.
Together, these characterizations indicate complementarity and synergy in how
these mechanisms act, potentially over multiple time-scales, toward enhancing
robustness of multi-task learning.
