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

ABSTRACT: 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 con

SUBMITTER: Bathellier B 

PROVIDER: S-EPMC3856837 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature