
The acquisition of knowledge or skills through experience, modeled here with few examples.Learning is the process by which a system — biological or artificial — acquires knowledge or skills through experience, often by adjusting internal parameters to improve performance on a task. In the context of machine learning, it involves training models on data so they can generalize to unseen examples. The titles in the corpus suggest learning is studied both in neuroscience (e.g., memory consolidation during sleep, synaptic efficiency) and in artificial intelligence (e.g., embodied cognition, network architectures).
The formal study of learning spans centuries, but the modern computational framework began with Donald Hebb's 1949 postulate on synaptic plasticity and the development of the perceptron by Frank Rosenblatt in 1958, later evolving into deep learning.
In artificial neural networks, learning typically involves an optimization algorithm, such as stochastic gradient descent, that minimizes a loss function by updating weights based on the error between predicted and actual outputs. In biological systems, learning occurs through synaptic plasticity — the strengthening or weakening of connections between neurons, often summarized by Hebbian rules. Both processes rely on feedback signals that guide adjustments, whether from explicit labels or from environmental interactions.
Learning algorithms power speech recognition, image classification, recommendation systems, and autonomous vehicles, while understanding biological learning informs treatments for neurological conditions and brain-computer interfaces.
The brain actively consolidates memories during sleep, and this process is so efficient that a brief nap can improve performance on a newly learned task by up to 20%.
























