Download "Optimal structure of metaplasticity for adaptive learning"

Learning from reward feedback in a changing environment requires a high degree of adaptability,
yet the precise estimation of reward information demands slow updates. In the
framework of estimating reward probability, here we investigated how this tradeoff between
adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do
not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we
identified ‘superior’ metaplastic models that can substantially overcome the adaptability-precision
tradeoff. These models can achieve both adaptability and precision by forming two
separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do
not change their efficacy upon reward feedback, whereas those in buffer meta-states can
change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers,
creating a bottleneck that reduces noise without significantly decreasing adaptability. In
contrast, more-populated reservoirs can generate a strong signal without manifesting any
observable plasticity. By comparing the behavior of our model and a few competing models
during a dynamic probability estimation task, we found that superior metaplastic models perform
close to optimally for a wider range of model parameters. Finally, we found that metaplastic
models are robust to changes in model parameters and that metaplastic transitions
are crucial for adaptive learning since replacing them with graded plastic transitions (transitions
that change synaptic efficacy) reduces the ability to overcome the adaptability-precision
tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes
evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff
between adaptability and precision and thus adaptive learning.

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