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

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Frames: Problem Solving II

Frames: Problem Solving II

Consider a hydraulic hoist supporting a load of 1 kN. Assuming a simplified schematic representation of this frame structure, the force acting on BD and BF members can be determined.

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Frames: Problem Solving I

Frames: Problem Solving I

Consider a jib crane with an external load suspended from the pulley. The dimensions of the crane members are shown in the figure. A systematic analysis of the frame structure is required to determine the reaction forces at the pin joints, assuming that the pulleys are frictionless.

Frames

Frames

Frames are essential components of various mechanical and structural systems used daily. These structures are known for their stability and ability to bear heavy loads. A frame is constructed using two-force and multi-force members, interconnected using pin joints. In contrast, trusses are made entirely of two-force members.
Frames are versatile and widely used in various applications such as structural supports for beams and columns, automobile chassis construction, and in the construction...

You might also read

Related Articles

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

Sort by

Same author

PROPTIMUS LIVE: local constrained α-carbon optimization of proteins.

Nucleic acids research·2026

Same author

Risk of Non-colorectal Malignancies in Sporadic <i>Versus</i> Lynch Syndrome-associated dMMR Colorectal Cancer.

Anticancer research·2026

Same author

Global remapping of the sensory homunculus emerges early in childhood development.

Nature communications·2026

Same author

Analysis of cyclohexane, cyclopentane, and benzene conformations in ligands for PDB X-ray structures using the Hill-Reilly approach.

Journal of cheminformatics·2026

Same author

Toward embodied and action-oriented studies and models of peripersonal space: Comment on "Computational models of peripersonal space representations" by Bertoni et al.

Physics of life reviews·2026

Same author

The body is not there to compute: Comment on "Informational embodiment: Computational role of information structure in codes and robots" by Pitti et al.

Physics of life reviews·2025

Search research articles

Related Experiment Video

Updated: Aug 4, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

PreCNet: Next-Frame Video Prediction Based on Predictive Coding.

Zdenek Straka, Tomas Svoboda, Matej Hoffmann

IEEE Transactions on Neural Networks and Learning Systems

|April 6, 2023

Summary

This summary is machine-generated.

This study introduces PreCNet, a deep learning model based on neuroscience principles for video prediction. It achieves state-of-the-art results on urban driving datasets, demonstrating the power of biologically inspired AI.

More Related Videos

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Related Experiment Videos

Last Updated: Aug 4, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Area of Science:

Computational Neuroscience
Machine Learning
Artificial Intelligence

Background:

Predictive coding is a prominent neuroscience theory.
Its application in machine learning remains limited.
Bridging this gap can yield novel AI architectures.

Purpose of the Study:

To translate the Rao and Ballard (1999) predictive coding model into a modern deep learning framework.
To evaluate the performance of this neuroscience-inspired network (PreCNet) on a real-world task.
To demonstrate the efficacy of biologically plausible models in AI.

Main Methods:

Adapted the Rao and Ballard (1999) predictive coding model into a deep learning architecture (PreCNet).
Trained and evaluated PreCNet on a next-frame video prediction benchmark using urban driving imagery.
Utilized the KITTI dataset and a larger BDD100k dataset (2M images) for training.

Main Results:

PreCNet achieved state-of-the-art performance on the next-frame video prediction task.
Performance improved significantly with a larger training dataset (BDD100k), highlighting data scale benefits.
Metrics including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) showed improvements.

Conclusions:

Neuroscience-inspired models can achieve exceptional performance in machine learning tasks.
PreCNet demonstrates the potential of translating computational neuroscience theories into practical AI.
The study underscores the importance of dataset scale for model performance.