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

Decision Making01:20

Decision Making

440
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
440
Neural Control of Respiration01:18

Neural Control of Respiration

3.8K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
3.8K
Reason and Intuition01:37

Reason and Intuition

7.2K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
7.2K
Neural Circuits01:25

Neural Circuits

2.2K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.2K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.8K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.8K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

258
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 of...
258

You might also read

Related Articles

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

Sort by
Same author

Chip-scale electrically focus-tunable VCSEL with large dynamic zoom enabled by liquid crystal phase gradients.

Optics express·2026
Same author

Influence of sample preparation methods on the spatial overlap between analytes and metasurfaces in metasurface-enhanced terahertz spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Practical Catalytic Asymmetric Synthesis of Gem-Dimethyl Bicyclic [3.1.0] Proline: A Valuable Building Block for Antiviral Agents.

The Journal of organic chemistry·2026
Same author

TRIDENT: A multi-task, triple-branch deep learning framework for EEG-based recognition, severity estimation, and future high-anger prediction in an on-road Wizard-of-Oz paradigm.

Accident; analysis and prevention·2026
Same author

β-Arrestin condensates regulate G-protein-coupled receptor function.

Nature·2026
Same author

Functional insights into dispensable genes using genome-wide loss-of-function burden tests in Arabidopsis.

The Plant cell·2026

Related Experiment Video

Updated: Nov 19, 2025

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

10.6K

A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive.

Tianjun Sun1,2, Zhenhai Gao1,2, Fei Gao1

  • 1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130012, China.

Sensors (Basel, Switzerland)
|January 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a brain-inspired linear neural network and fuzzy algorithm to classify driver behaviors. The method enhances intelligent vehicle decision-making by analyzing driving characteristics from simulator data.

Keywords:
brain-inspired decision-makingfuzzy classificationhuman-like automatic driving systemlinear neural network

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

14.0K

Related Experiment Videos

Last Updated: Nov 19, 2025

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

10.6K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

14.0K

Area of Science:

  • Artificial Intelligence
  • Bionics
  • Computer Science
  • Intelligent Transportation Systems

Background:

  • Brain-inspired intelligent decision-making is a growing trend.
  • Linear neural networks are key to achieving human-like decision-making and control.
  • Classifying driver behaviors is crucial for advancing intelligent vehicle systems.

Purpose of the Study:

  • To propose a novel method for classifying driver behaviors using a fuzzy algorithm and a brain-inspired linear neural network.
  • To enhance the decision-making capabilities of intelligent vehicles.
  • To provide a reference for human-like decision-making in autonomous systems.

Main Methods:

  • Collected driver experimental data using a driving simulator.
  • Designed an objective fuzzy classification algorithm to distinguish driving behaviors.
  • Established a brain-inspired linear neural network for decision-making and control.

Main Results:

  • Successfully classified different driving behaviors based on experimental data.
  • Verified the accuracy of the proposed method through training and testing.
  • Extracted key driving characteristics from driver data.

Conclusions:

  • The proposed fuzzy algorithm and brain-inspired linear neural network effectively classify driver behaviors.
  • This approach offers a valuable reference for human-like decision-making in intelligent vehicles.
  • The study contributes to the development of more sophisticated autonomous driving systems.