A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice.

Both in humans and in animals, different individuals may learn the same task with strikingly different speeds; however, the sources of this variability remain elusive. In standard learning models, interindividual variability is often explained by variations of the learning rate, a parameter indicating how much synapses are updated on each learning event. Here, we theoretically show that the initial connectivity between the neurons involved in learning a task is also a strong determinant of how quickly the task is learned, provided that connections are updated in a multiplicative manner. To experimentally test this idea, we trained mice to perform an auditory Go/NoGo discrimination task followed by a reversal to compare learning speed when starting from naive or already trained synaptic connections. All mice learned the initial task, but often displayed sigmoid-like learning curves, with a variable delay period followed by a steep increase in performance, as often observed in operant conditioning. For all mice, learning was much faster in the subsequent reversal training. An accurate fit of all learning curves could be obtained with a reinforcement learning model endowed with a multiplicative learning rule, but not with an additive rule. Surprisingly, the multiplicative model could explain a large fraction of the interindividual variability by variations in the initial synaptic weights. Altogether, these results demonstrate the power of multiplicative learning rules to account for the full dynamics of biological learning and suggest an important role of initial wiring in the brain for predispositions to different tasks.