Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Gaussian Elimination: Problem Solving

Gaussian Elimination: Problem Solving

Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...

Orthogonal Trajectories

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...

Stability of Equilibrium Configuration: Problem Solving

Stability of Equilibrium Configuration: Problem Solving

The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

How Might a 'Philosopher's Toolkit' Help Advance Neuroscience? Let's Ask Some Neuroscientists.

The European journal of neuroscience·2025

Same author

Editorial: Mathematical, Computational, and Empirical Approaches to Exploring Neuronal Mechanisms Underlying Cognitive Functions.

Frontiers in human neuroscience·2022

Same author

On stability and associative recall of memories in attractor neural networks.

PloS one·2020

Same author

Life Cycle Greenhouse Gas Emissions from Uranium Mining and Milling in Canada.

Environmental science & technology·2016

Same author

Dynamic systems approaches and levels of analysis in the nervous system.

Frontiers in physiology·2013

Same author

Use of the harmonic scalpel in thyroidectomy.

ANZ journal of surgery·2009

Search research articles

Related Experiment Video

Updated: Apr 24, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Overcoming catastrophic interference in connectionist networks using Gram-Schmidt orthogonalization.

Vipin Srivastava¹, Suchitra Sampath², David J Parker³

¹School of Physics, University of Hyderabad, Hyderabad, India; Centre for Neural and Cognitive Sciences, University of Hyderabad, Hyderabad, India.

|September 3, 2014

Summary

This study introduces a novel method to prevent catastrophic interference in connectionist memory models. By combining Gram-Schmidt orthogonalization with the Hebb-Hopfield model, researchers eliminated information loss in artificial neural networks.

More Related Videos

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Related Experiment Videos

Last Updated: Apr 24, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Area of Science:

Computational neuroscience
Artificial intelligence
Cognitive modeling

Background:

Connectionist models aim to simulate brain memory mechanisms.
Catastrophic interference (CI) causes sudden information loss in these models as storage capacity is exceeded.
Existing solutions often restrict model or input parameters, unlike biological memory's gradual information decay.

Purpose of the Study:

To investigate the underlying causes of catastrophic interference in connectionist memory systems.
To develop an intrinsic method for connectionist models to overcome catastrophic interference.
To enable artificial memory systems to store information without critical capacity limits or data loss.

Main Methods:

Analyzed pattern interference leading to catastrophic effects beyond a critical storage limit.
Integrated Gram-Schmidt orthogonalization with the Hebb-Hopfield model.
Evaluated the model's ability to store patterns without imposing restrictions on input or encoding/retrieval processes.

Main Results:

Demonstrated that catastrophic interference is linked to pattern interference exceeding a storage threshold.
The combined Gram-Schmidt orthogonalization and Hebb-Hopfield model successfully eliminated catastrophic interference.
This approach avoids CI in fixed-size networks without limiting pattern encoding or separating learning and recall.

Conclusions:

Gram-Schmidt orthogonalization effectively prevents catastrophic interference in Hebb-Hopfield networks.
This method offers a biologically plausible mechanism for robust memory storage in artificial systems.
The approach allows for associative learning between new and stored patterns, enhancing memory functionality.