Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Machines: Problem Solving II01:30

Machines: Problem Solving II

297
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
297
Machines: Problem Solving I01:22

Machines: Problem Solving I

302
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
302
Cognitive Learning01:21

Cognitive Learning

222
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
222
Introduction to Learning01:18

Introduction to Learning

337
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
337
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

100
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
100
Associative Learning01:27

Associative Learning

300
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
300

You might also read

Related Articles

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

Sort by
Same author

Intrinsic annealing in a hybrid memristor-magnetic tunnel junction Ising machine.

Nature communications·2026
Same author

Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement.

Scientific reports·2026
Same author

Memristors for Bayesian in-memory computing.

Nature materials·2025
Same author

Bayesian continual learning and forgetting in neural networks.

Nature communications·2025
Same author

A ferroelectric-memristor memory for both training and inference.

Nature electronics·2025
Same author

Training of physical neural networks.

Nature·2025
Same journal

Electrically Detected Magnetic Resonance Study of Barium and Nitric Oxide Treatments of 4H-SiC Metal-Oxide-Semiconductor Field-Effect Transistors.

Journal of applied physics·2026
Same journal

Graphene's Role in Advancing Quantum Electrical Standards.

Journal of applied physics·2026
Same journal

High crystallinity and polar-phase content in electrospun P(VDF-TrFE) nanofibers with low molecular weight.

Journal of applied physics·2025
Same journal

Comparison of the spin-transfer torque mechanisms in a three-terminal spin-torque oscillator.

Journal of applied physics·2025
Same journal

Weighing unequal parameter importance and measurement expense in adaptive quantum sensing.

Journal of applied physics·2025
Same journal

A technique to measure spin dependent trapping events at the metal-oxide-semiconductor field-effect transistor interface: near zero field spin dependent charge pumping.

Journal of applied physics·2025
See all related articles

Related Experiment Video

Updated: Jun 9, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.2K

Overcoming device unreliability with continuous learning in a population coding based computing system.

Alice Mizrahi1,2, Julie Grollier3, Damien Querlioz4

  • 1National Institute of Standards and Technology, Gaithersburg, USA.

Journal of Applied Physics
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

Inspired by the brain

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

10.3K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

Related Experiment Videos

Last Updated: Jun 9, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.2K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

10.3K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

Area of Science:

  • Neuromorphic computing
  • Materials science
  • Artificial intelligence

Background:

  • The brain exhibits robustness to component unreliability through redundancy and continuous learning.
  • Building reliable computing systems from unreliable nanodevices is a significant challenge.

Purpose of the Study:

  • To demonstrate a brain-inspired computing system using population coding and magnetic tunnel junctions.
  • To investigate the role of continuous learning in enhancing system robustness and enabling the use of unreliable components.

Main Methods:

  • Developed a computing system utilizing population coding with magnetic tunnel junctions for neurons and synaptic weights.
  • Implemented continuous learning algorithms to enable adaptation and recovery from component failure.
  • Analyzed the trade-off between power consumption, precision, and memory characteristics.

Main Results:

  • The system demonstrated recovery from neuron loss through continuous learning.
  • Unreliable synaptic weights, specifically low energy barrier magnetic memories, were successfully utilized.
  • Identified an optimal balance between the number of neurons and weight energy barrier for minimizing power consumption at a given precision.

Conclusions:

  • Brain-inspired architectures with continuous learning offer a viable path to robust neuromorphic computing.
  • The use of unreliable magnetic memories is feasible in such systems, with careful optimization.
  • Achieving low-power, high-precision computing requires balancing system parameters like neuron count and synaptic properties.