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

State Space Representation01:27

State Space Representation

590
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
590
Convolution Properties II01:17

Convolution Properties II

589
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
589
Convolution Properties I01:20

Convolution Properties I

611
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
611
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

220
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
220
Control Volume and System Representations01:16

Control Volume and System Representations

1.6K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.6K
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

553
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
553

You might also read

Related Articles

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

Sort by
Same author

Deep Learning for the Detection of Corneal Perforation on Anterior-Segment Optical Coherence Tomography in Microbial Keratitis.

Bioengineering (Basel, Switzerland)·2026
Same author

Deep Learning for Detection of Corneal Perforation on Anterior Segment Optical Coherence Tomography in Microbial Keratitis.

medRxiv : the preprint server for health sciences·2026
Same author

Recovering Pulse Waves From Video Using Deep Unrolling and Deep Equilibrium Models.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

The use of AI in psychology: A historical perspective.

British journal of psychology (London, England : 1953)·2026
Same author

Innovation in geriatrics: what this series means for care.

Innovation in aging·2025
Same author

A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.

European radiology·2025
Same journal

Misinformation as strategy: Epistemic consequences and the undermining of shared truth.

Trends in cognitive sciences·2026
Same journal

Geographical psychology: Spatial variation in psychological phenomena and their consequences.

Trends in cognitive sciences·2026
Same journal

Multi-brain neurofeedback: what are we training for?

Trends in cognitive sciences·2026
Same journal

The developing vocal self.

Trends in cognitive sciences·2026
Same journal

Searching beyond decrements: Attentional guidance across the adult lifespan.

Trends in cognitive sciences·2026
Same journal

Looking into working memory through micro eye movements.

Trends in cognitive sciences·2026
See all related articles

Related Experiment Video

Updated: Feb 6, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Face Space Representations in Deep Convolutional Neural Networks.

Alice J O'Toole1, Carlos D Castillo2, Connor J Parde1

  • 1School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA.

Trends in Cognitive Sciences
|August 12, 2018
PubMed
Summary
This summary is machine-generated.

Deep convolutional neural networks (DCNNs) show promise for generalized face recognition, mimicking primate vision. However, the internal visual representations, or

Keywords:
convolutional neural networksdeep learningface recognitionvisual cortex

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

881

Related Experiment Videos

Last Updated: Feb 6, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

881

Area of Science:

  • Computer Vision
  • Neuroscience
  • Artificial Intelligence

Background:

  • Deep convolutional neural networks (DCNNs) are inspired by primate visual systems and excel at face recognition tasks.
  • Generalized face recognition, a key human ability, remains a challenge for AI despite computational advances.
  • The internal visual representations (face codes) learned by DCNNs are not well understood.

Purpose of the Study:

  • To review and understand the visual nature of face codes that emerge in DCNNs.
  • To contextualize DCNN face representations within the framework of traditional face recognition algorithms using the 'face space' metaphor.
  • To determine if DCNN face representations constitute a fundamentally new class of visual representation.

Main Methods:

  • Review of existing literature on DCNNs and face recognition.
  • Analysis of DCNN internal representations using the 'face space' concept.
  • Comparison of DCNN representations with previous face recognition algorithms.

Main Results:

  • DCNN face representations are a novel class of visual representation.
  • These representations enable, but do not guarantee, generalized face recognition.
  • The 'face space' metaphor provides a useful, though incomplete, framework for understanding DCNN codes.

Conclusions:

  • DCNNs offer a new paradigm for artificial face recognition, inspired by biological systems.
  • Understanding DCNN 'face codes' is crucial for advancing AI capabilities in visual perception.
  • While powerful, DCNNs do not fully replicate the robustness of human face recognition.