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

Encoding01:19

Encoding

867
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
867
Velocity of an Object01:18

Velocity of an Object

207
Understanding how an object moves along a path requires distinguishing between motion over a time span and motion at a precise moment. A useful example is a vehicle traveling along a straight and level path, where its position at any given time is known. The initial step in analyzing this motion is to measure how far the vehicle travels over a fixed time period. This measurement, called average velocity, is computed by dividing the total change in position by the duration over which the change...
207
Potential Due to a Polarized Object01:29

Potential Due to a Polarized Object

803
A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
803
Potential Due to a Magnetized Object01:24

Potential Due to a Magnetized Object

817
Magnetic dipoles in magnetic materials are aligned when placed under an external magnetic field. For paramagnets and ferromagnets, dipole alignment occurs in the direction of the magnetic field. However, the dipoles align opposite to the field in the case of diamagnets. This state of magnetic polarization due to the external field is called magnetization. Magnetization is defined as the dipole moment per unit volume. It plays a similar role to polarization in electrostatics.
The vector...
817
COPD: Pathogenesis and Clinical Features01:20

COPD: Pathogenesis and Clinical Features

1.9K
Chronic obstructive pulmonary disease (COPD) is a group of lung conditions that progressively worsen over time, including chronic bronchitis and emphysema. This cluster of diseases collectively leads to a gradual and irreversible decline in lung function over time.
The primary cause for the onset of COPD is cigarette smoking and exposure to air pollution. These hazardous factors initiate a chain reaction within the lungs, resulting in chronic inflammation, damage to the airways, and a...
1.9K
Moment of Inertia of Compound Objects01:07

Moment of Inertia of Compound Objects

7.6K
The moment of inertia is a quantitative measure of the rotational inertia of an object. It is defined as the sum of the products obtained by multiplying the mass of each particle of matter in a given body by the square of its distance from the axis. The total moment of inertia for compound objects can be found by determining and adding the moment of inertia of individual components together.
Consider a child of mass (mc) 25 kg standing at a distance (rc) of 1 m from the axis of a rotating...
7.6K

You might also read

Related Articles

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

Sort by
Same author

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same author

Enhancing X-ray Image Classification through Heterogeneous Federated Learning with Natural Image-Augmented Models.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Developing and Testing a Brief Mindfulness Just-in-Time Adaptive Intervention to Reduce Stress Among Caregivers of People With Dementia: Quasi-Experimental Study.

JMIR aging·2026
Same author

Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion.

Nature communications·2026
Same author

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same author

scBIT: Integrating Single-Cell Transcriptomic Data Into fMRI-Based Prediction for Alzheimer's Disease Diagnosis.

IEEE transactions on medical imaging·2026

Related Experiment Video

Updated: Feb 8, 2026

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
08:52

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

77.6K

Sparse Temporal Encoding of Visual Features for Robust Object Recognition by Spiking Neurons.

Yajing Zheng, Shixin Li, Rui Yan

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary

    This study introduces a novel spiking neural system for robust object recognition. The system uses sparse temporal encoding and a temporal classifier, achieving high accuracy in neuromorphic computing.

    More Related Videos

    Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
    05:57

    Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

    Published on: November 20, 2018

    59.1K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.2K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
    08:52

    Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

    Published on: August 30, 2017

    77.6K
    Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
    05:57

    Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

    Published on: November 20, 2018

    59.1K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.2K

    Area of Science:

    • Neuromorphic Computing
    • Computational Neuroscience
    • Artificial Intelligence

    Background:

    • Spiking neural systems face challenges in robust object recognition due to difficulties in sensory information encoding and integration with learning neurons.
    • Current methods often struggle to effectively process temporal dynamics inherent in spiking neural activity.

    Purpose of the Study:

    • To develop a spiking neural system that addresses the challenges of effective sensory information encoding and integration for robust object recognition.
    • To propose a novel sparse temporal encoding algorithm tailored for spiking neural networks.

    Main Methods:

    • Developed a spiking neural system incorporating sparse temporal encoding and a temporal classifier.
    • Proposed a sparse temporal encoding algorithm leveraging spatial and temporal information from a spike-timing-dependent plasticity-based HMAX feature extraction process.
    • Integrated the temporal feature representation with a spiking neuron-based temporal classifier.

    Main Results:

    • Validated the algorithm on two benchmark datasets, demonstrating high recognition accuracy.
    • The proposed temporal feature encoding and learning-based method significantly improved performance.
    • The system effectively integrates feature representation and recognition within a consistent temporal learning framework.

    Conclusions:

    • The developed spiking neural system offers an efficient approach for feature representation and recognition in neuromorphic computing.
    • The temporal learning framework is well-suited for neuromorphic implementations.
    • This work advances robust object recognition capabilities in spiking neural networks.