Active Learning
Active Learning - Related Videos
06:03
The Active Place Avoidance (APA) Test, an Effective, Versatile and Repeatable Spatial Learning Task for Mice
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Cited by 3 •
2024
Here, we present a protocol for the active place avoidance test, a hippocampus-dependent spatial learning paradigm designed for rodents. Altering key parameters allows for re-testing of animals before and after treatments or over time.
09:22
A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
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Cited by 9 •
2015
This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while rats are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different components of learning.
07:43
Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders
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Cited by 2 •
2015
We describe an immunohistochemistry protocol to study the profile of hippocampal neuron activation following exposure to a spatial learning task in a mouse model characterized by cognitive deficits of neurodevelopmental origin. This protocol can be applied to both genetic or pharmacological mouse models characterized by cognitive deficits.
10:06
An Introduction to Learning and Memory
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2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
11:18
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Cited by 18 •
2015
A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.
FAQs
Active learning prioritizes examples whose labels are expected to be most informative, often by estimating prediction uncertainty on unlabeled data. Samples with greater uncertainty can reveal where the current model lacks reliable guidance, so adding them may improve the next training cycle more efficiently than labeling arbitrary cases. This principle focuses data collection on model-relevant evidence.
Prediction uncertainty provides a practical signal for deciding which unlabeled cases deserve attention. High-uncertainty examples may indicate regions where the model's current predictions are less dependable, while selected informative cases can supply evidence for the next iteration. These choices determine where new labels are obtained and how efficiently the training set expands.
The initial labeled set gives the model a starting point for evaluating the remaining unlabeled data. After the model identifies promising or uncertain examples, their labels are added and the model is trained again. This repeated exchange between prediction, selection, labeling, and retraining lets data collection evolve as the model learns.
Unlike a workflow that depends on labeling an entire dataset before learning, active learning concentrates labeling effort on a selected subset. The benefit is not simply fewer labels; it is more deliberate allocation of annotation or experimental effort when labels are expensive. This makes the approach especially relevant to engineering studies with limited labeled data.
An engineering workflow begins with a small labeled set and an unlabeled collection. The model is trained on the available labeled examples, evaluates the unlabeled cases, and selects samples using informativeness measures such as prediction uncertainty. After those cases receive labels, they join the training set, and the process repeats to guide subsequent data collection.
Engineering teams may favor active learning when obtaining labels requires substantial annotation work or physical experimentation. Instead of spending effort equally across all available cases, they can use the model's current assessment to direct work toward selected examples. This is useful when labeled data are limited and the cost of expanding the dataset is an important constraint.
By directing labels or experiments toward informative cases, active learning can support models for design optimization, fault detection, materials discovery, and quality inspection. Its value lies in improving the usefulness of the model while limiting unnecessary data collection. The specific outcome depends on the engineering task and the quality of the selected examples.