Auto-trainer System
Auto-trainer System - Related Videos
00:05
Evaluating Skilled Prehension in Mice Using an Auto-Trainer
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Cited by 1 •
2019
Method to assess the impact of training on motor skills is a useful tool. Unfortunately, most behavioral assessments can be labor intensive and/or expensive.We describe here a robotic method of assessing prehension (reach-to-grasp) skill in mice.
05:06
Tumor Allotransplantation in Drosophila melanogaster with a Programmable Auto-Nanoliter Injector
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Cited by 10 •
2021
This protocol provides detailed guidance for the initial and continued generational allotransplantation of Drosophila tumors into the abdomen of adult hosts for studying various aspects of neoplasia. Using an autoinjector apparatus, researchers can achieve improved efficiency and tumor yields compared to those achieved by traditional, manual methods.
11:45
Creation of a High-Fidelity, Low-Cost, Intraosseous Line Placement Task Trainer via 3D Printing
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2022
We describe a procedure to process computed tomography (CT) scans into high-fidelity, reclaimable, and low-cost procedural task trainers. The CT scan identification processes, export, segmentation, modeling, and 3D printing are all described, along with the issues and lessons learned in the process.
08:02
Mouse Model of Surgically-induced Endometriosis by Auto-transplantation of Uterine Tissue
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Cited by 62 •
2012
A description of the surgical induction of endometriosis in mice and rats by auto-transplantation of uterine tissue to the arterial cascade of the intestinal mesentery.
08:20
Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
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Cited by 6 •
2023
Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.
FAQs
The system links each trial by presenting a programmed cue, monitoring the animal’s response with sensors, and applying a preset consequence, such as food reinforcement, according to conditioning rules. This sequence makes the relationship between stimulus, behavior, and outcome explicit. Because the same logic can operate across trials, researchers can examine response patterns under controlled experimental conditions rather than relying on uninterrupted manual intervention.
Preset rules determine when a response is followed by a consequence, while programmed stimuli establish the conditions under which behavior is measured. Varying these elements allows a study to test how reinforcement schedules or stimulus conditions influence learning and response patterns. The system therefore supports comparisons across experimental conditions using a defined sequence instead of changing contingencies informally between trials.
Automated recording captures performance across trials and sessions using the same programmed procedures. This consistency can reduce differences caused by experimenter timing or intervention and makes longitudinal testing more practical. Researchers can then evaluate changes in learning, response patterns, or task performance over time, provided that the programmed cues, response detection, and consequences remain aligned with the experimental design.
A typical workflow begins by programming the behavioral task, including the cues, response conditions, reinforcement rules, and recording settings. The apparatus then presents trials, detects responses through its sensors, delivers the specified consequence, and stores performance data. Researchers can review the recorded trials to assess how the animal performed under the selected conditioning arrangement and experimental conditions.
Researchers may choose the system when they need standardized operant conditioning, repeated measurements, or longitudinal testing across multiple sessions. Automation reduces the need for continuous intervention while preserving programmed control over cues and consequences. This is especially useful when the study compares stimuli, reinforcement schedules, or other experimental conditions and requires performance data collected consistently across trials.
Recorded performance can support evaluation of learning, response patterns, and changes in task performance across trials or sessions. By relating these measurements to programmed stimuli, reinforcement schedules, and experimental conditions, researchers can examine how specific contingencies influence behavior. In behavior research, the resulting data provide a structured basis for comparing conditioning outcomes and tracking performance over time.