FixGrower: An efficient and robust curriculum for shaping fixation behavior in rodents
Breda, J. R.; Charlton, J. A.; Willock, J. M.; Kopec, C. D.; Brody, C. D.
Show abstract
Center-port fixation is a common prerequisite for many freely-moving rodent tasks in neuroscience and psychology. However, typical protocols for shaping this behavior are non-standardized and inefficient. Moreover, motor errors in fixation termed violations often account for a significant fraction of experimental trials, leading to notable data loss during experiments. In light of this, we developed FixGrower, a standardized protocol for center-port fixation training. FixGrower (1) requires a longer initial fixation requirement, (2) increases the required fixation duration at session boundaries customized to each animals performance, and (3) delays the introduction of violation penalties until the end of training. We demonstrate FixGrower decreases training time by 61%, yields low violation rates, and generalizes across rodent species and task difficulty. Moreover, the success of this curriculum is well supported by theories of operant conditioning and reinforcement learning. Our findings establish FixGrower as an efficient and broadly applicable curriculum for training fixation behavior in rodents, thereby accelerating training of many tasks in the field.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A Dual Reward-Place Association Task to Study the Preferential Retention of relevant Memories in Rats 96%
- Multi-domain touchscreen-based cognitive assessment of C57BL/6J female mice shows whole body exposure to 56Fe particle space radiation in maturity improves discrimination learning yet impairs stimulus-response habit learning 95%
- A novel weight lifting task for investigating effort and persistence in rats 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.