Computational optimization of associative learning experiments.

ABSTRACT: With computational biology striving to provide more accurate theoretical accounts of biological systems, use of increasingly complex computational models seems inevitable. However, this trend engenders a challenge of optimal experimental design: due to the flexibility of complex models, it is difficult to intuitively design experiments that will efficiently expose differences between candidate models or allow accurate estimation of their parameters. This challenge is well exemplified in associative learning research. Associative learning theory has a rich tradition of computational modeling, resulting in a growing space of increasingly complex models, which in turn renders manual design of informative experiments difficult. Here we propose a novel method for computational optimization of a

SUBMITTER: Melinscak F 

PROVIDER: S-EPMC6964915 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature