Related Experiment Video
Updated: Jul 7, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
A dynamical system perspective of structural learning with forgetting
1Department of Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI 49008, USA.
Abstract:
Structural learning with forgetting is an established method of using Laplace regularization to generate skeletal artificial neural networks. In this paper we develop a continuous dynamical system model of regularization in which the associated regularization parameter is generalized to be a time-varying function. Analytic results are obtained for a Laplace regularizer and a quadratic error surface by solving a different linear system in each region of the weight space. This model also enables a comparison of Laplace and Gaussian regularization. Both of these regularizers have a greater effect in weight space directions which are less important for minimization of a quadratic error function. However, for the Gaussian regularizer, the regularization parameter modifies the associated linear system eigenvalues, in contrast to its function as a control input in the Laplace case. This difference provides additional evidence for the superiority of the Laplace over the Gaussian regularizer.
More Related Videos
Related Concept Videos
Forgetting
Encoding...
Interference and Decay
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
Structuralism
Titchener's approach to structuralism was unique. He employed introspection, a method...
Observational Learning
Storage
Associative Learning
Classical conditioning, also known...
