Download "Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty"

Value-based decision making often involves integration
of reward outcomes over time, but this becomes
considerably more challenging if reward assignments
on alternative options are probabilistic and
non-stationary. Despite the existence of various
models for optimally integrating reward under uncertainty,
the underlying neural mechanisms are still
unknown. Here we propose that reward-dependent
metaplasticity (RDMP) can provide a plausible mechanism
for both integration of reward under uncertainty
and estimation of uncertainty itself. We show
that a model based on RDMP can robustly perform
the probabilistic reversal learning task via dynamic
adjustment of learning based on reward feedback,
while changes in its activity signal unexpected uncertainty.
The model predicts time-dependent and
choice-specific learning rates that strongly depend
on reward history. Key predictions from this model
were confirmed with behavioral data from non-human
primates. Overall, our results suggest that metaplasticity
can provide a neural substrate for adaptive
learning and choice under uncertainty