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

Neural Circuits01:25

Neural Circuits

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...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Associative Learning01:27

Associative Learning

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...
Cognitive Learning01:21

Cognitive Learning

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...
Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

You might also read

Related Articles

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

Sort by
Same author

FGFR2 Regulates Liver Injury and Repair in a Model of Obstructive Jaundice.

Frontiers in bioscience (Landmark edition)·2026
Same author

Association of combined TyG-BMI and hs-CRP with incident cardiovascular disease in an aging Chinese population: A nationwide prospective cohort study.

Clinical nutrition ESPEN·2026
Same author

Development and validation of a predictive model for postoperative delirium in patients undergoing cardiac surgery.

Frontiers in cardiovascular medicine·2026
Same author

MCL inhibits macrophage ferroptosis through the NRF2/HO-1/GPX4 axis to attenuate carotid atherosclerotic plaque formation.

International immunopharmacology·2026
Same author

Differences in Neurofilament Light Chain, Glial Fibrillary Acidic Protein, and Tau Protein Levels in Patients with Temporal Lobe Epilepsy and Comorbid Depression.

Molecular neurobiology·2026
Same author

PANoptosis in atherosclerotic plaques: Molecular mechanisms, clinical significance, and therapeutic potential.

European journal of pharmacology·2026

Related Experiment Video

Updated: May 11, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

Coupled gradient-evolutionary learning in sparse memristive neuromorphic networks for robust edge intelligence.

Yangboyu Liu1, Zilu Wang1, Zibo Chen1

  • 1School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neuromorphic computing framework for edge intelligence, enhancing driving safety with fused sensor data and energy-efficient processing. It achieves significant energy savings and faster responses for real-time decision-making in vehicles.

Keywords:
Computing-in-memoryEdge driving-safety monitoringGradient-evolutionary learningHardware-aware low-bit quantizationMemristive neuromorphic networks

Related Experiment Videos

Last Updated: May 11, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

Area of Science:

  • Neuromorphic Computing
  • Edge Intelligence
  • Artificial Intelligence

Background:

  • Fragmented multimodal perception, high energy consumption, and delayed responses hinder current edge intelligence systems in vehicles.
  • Existing systems struggle with real-time processing of diverse data streams (physiological, visual, auditory) for safety-critical applications.
  • Hardware limitations in conventional von Neumann architectures limit efficiency and speed for complex AI tasks at the edge.

Purpose of the Study:

  • To develop a robust edge intelligence framework for real-time driving safety monitoring using multimodal data.
  • To enhance energy efficiency and reduce response latency in anomalous driving scenarios.
  • To create a synergistic hardware-software solution leveraging memristive neuromorphic networks and evolutionary learning.

Main Methods:

  • A coupled gradient-evolutionary learning framework was deployed on sparse memristive neuromorphic networks.
  • The framework fused physiological, visual, and auditory data for a closed-loop perception-analysis-decision pipeline.
  • Neuromorphic processing units were built with 2D material-based memristors, utilizing in-memory computing and evolutionary training driven by memristor write noise.

Main Results:

  • The framework achieved energy-efficient classification of multimodal signals with tens-of-milliseconds inference latency and tens-of-milliwatts power consumption.
  • It demonstrated significant robustness against hardware non-idealities and quantization noise, improving classification accuracy.
  • The system achieved approximately 5.8x energy savings compared to conventional edge computing, with hundreds of microjoules per inference.

Conclusions:

  • This work presents a low-carbon, robust, and scalable edge intelligence solution for road safety decision-making.
  • The proposed neuromorphic computing approach offers substantial energy savings and improved performance for intelligent transportation systems.
  • The study highlights the potential of memristive neuromorphic networks for advancing sustainable and reliable AI at the edge.