CCNLab: A Benchmarking Framework for Computational Cognitive Neuroscience

Nikhil Xie Bhattasali, Momchil Tomov, Samuel Gershman

Keywords: benchmark, computational neuroscience, cognitive neuroscience, classical conditioning

Abstract: CCNLab is a benchmark for evaluating computational cognitive neuroscience models on empirical data. As a starting point, its focus is classical conditioning, which studies how animals predict reward and punishment in the environment. CCNLab includes a collection of simulations of seminal experiments expressed under a common API, as wells as tools for visualizing and comparing simulated data with empirical data. CCNLab is broad, incorporating representative experiments from different categories of phenomena; flexible, allowing the straightforward addition of new experiments; and easy-to-use, so researchers can focus on developing better models. We envision CCNLab as a testbed for unifying computational theories of learning in the brain. We also hope that it can broadly accelerate neuroscience research and facilitate interaction between the fields of neuroscience, psychology, and artificial intelligence.

URL: https://github.com/nikhilxb/ccnlab