  How do humans and animals perform trial-and-error learning when the space of
possibilities is infinite? In a previous study, we used an interval timing
production task and discovered an updating strategy in which the agent adjusted
the behavioral and neuronal noise for exploration. In the experiment, human
subjects proactively generated a series of timed motor outputs. We found that
the sequential motor timing varied at two temporal scales: long-term
correlation around the target interval due to memory drifts and short-term
adjustments of timing variability according to feedback. We have previously
described these features of timing variability with an augmented Gaussian
process, termed reward sensitive Gaussian process (RSGP). Here we provide a
mechanistic model and simulate the process by borrowing the architecture of
recurrent neural networks. While recurrent connection provided the long-term
serial correlation in motor timing, to facilitate reward-driven short-term
variations, we introduced reward-dependent variability in the network
connectivity, inspired by the stochastic nature of synaptic transmission in the
brain. Our model was able to recursively generate an output sequence
incorporating the internal variability and external reinforcement in a Bayesian
framework. We show that the model can learn the key features of human behavior.
Unlike other neural network models that search for unique network connectivity
for the best match between the model prediction and observation, this model can
estimate the uncertainty associated with each outcome and thus did a better job
in teasing apart adjustable task-relevant variability from unexplained
variability. The proposed artificial neural network model parallels the
mechanisms of information processing in neural systems and can extend the
framework of brain-inspired reinforcement learning in continuous state control.
