PT  - JOURNAL ARTICLE
AU  - Mu Qiao
AU  - Tony Zhang
AU  - Cristina Segalin
AU  - Sarah Sam
AU  - Pietro Perona
AU  - Markus Meister
TI  - Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning
AID  - 10.1101/467878
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 467878
4099  - http://biorxiv.org/content/early/2018/11/16/467878.short
4100  - http://biorxiv.org/content/early/2018/11/16/467878.full
AB  - Progress in understanding how individual animals learn will require high-throughput standardized methods for behavioral training but also advances in the analysis of the resulting behavioral data. In the course of training with multiple trials, an animal may change its behavior abruptly, and capturing such events calls for a trial-by-trial analysis of the animal’s strategy. To address this challenge, we developed an integrated platform for automated animal training and analysis of behavioral data. A low-cost and space-efficient apparatus serves to train entire cohorts of mice on a decision-making task under identical conditions. A generalized linear model (GLM) analyzes each animal’s performance at single-trial resolution. This model infers the momentary decision-making strategy and can predict the animal’s choice on each trial with an accuracy of ~80%. We also introduce automated software to assess the animal’s detailed trajectories and body poses within the apparatus. Unsupervised analysis of these features revealed unusual trajectories that represent hesitation in the response. This integrated hardware/software platform promises to accelerate the understanding of animal learning.