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

Understanding Memory01:19

Understanding Memory

Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
Encoding01:19

Encoding

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...
Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...

You might also read

Related Articles

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

Sort by
Same author

Cr-Doped CuTi-Layered Double Hydroxide Nanoarchitectures Grown on 3D Ni Foam: Boosting Charge Separation for Photocatalytic Mineralization of Refractory Organics.

ACS omega·2026
Same author

WS<sub>2</sub> Optoelectronic Memristive Reservoir Enabling Ultra-Low-Power, Multi-Task, and Environmentally Stable Neuromorphic Computing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Research on Alzheimer's Disease Risk Assessment Models and Biomarker Screening Based on Bioinformatics Analysis and Machine Learning Algorithms.

Current Alzheimer research·2026
Same author

Automatic deep learning-based segmentation of cornea and lens in 2D OCT images of rabbit eyes.

Experimental eye research·2026
Same author

Sentinels or saboteurs, the Janus face of cochlear-resident macrophages in hearing loss: spatiotemporal distribution, pathophysiological functions, and translational potential.

Journal of neuroinflammation·2025
Same author

A Retina-Inspired Organic Iono-Optoelectronic Synapse.

Advanced materials (Deerfield Beach, Fla.)·2025

Related Experiment Video

Updated: Jun 20, 2026

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
08:33

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

12.6K

A neuromorphic event data interpretation approach with hardware reservoir.

Hanrui Li1, Dayanand Kumar1, Nazek El-Atab1

  • 1SAMA Labs, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

Frontiers in Neuroscience
|November 29, 2024
PubMed
Summary

This study introduces a novel hardware approach for event camera data processing using memristor-based reservoir computing. This method offers efficient, low-cost feature extraction for dynamic visual information, outperforming existing techniques.

Keywords:
SNNevent representationmemristorneuromorphic computingreservoir computing

More Related Videos

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

10.3K
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

7.7K

Related Experiment Videos

Last Updated: Jun 20, 2026

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
08:33

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

12.6K
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

10.3K
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

7.7K

Area of Science:

  • Computer Vision
  • Neuromorphic Engineering
  • Materials Science

Background:

  • Event cameras offer high temporal resolution and low latency for dynamic scene capture.
  • Current event data processing relies heavily on algorithms, hindering hardware deployment.
  • Memristors possess unique stochastic and non-linear properties suitable for efficient feature extraction.

Purpose of the Study:

  • To develop a hardware-based event data representation approach using memristors.
  • To explore the efficacy of memristor-based reservoir computing for event stream processing.
  • To demonstrate a low-cost, efficient solution for dynamic visual information extraction.

Main Methods:

  • A simplified memristor model was developed for analog computation.
  • A memristor-based reservoir circuit was designed for event data processing.
  • The proposed system was evaluated on four diverse event datasets.

Main Results:

  • The memristor-based reservoir encoder effectively extracted temporal features from event streams.
  • The proposed hardware approach achieved superior accuracy compared to existing methods.
  • The system demonstrated efficient and low-cost processing of dynamic visual information.

Conclusions:

  • Memristor-based reservoir computing presents a viable hardware solution for event camera data.
  • This approach overcomes limitations of algorithm-based methods for event data representation.
  • The study highlights the potential of memristor devices in next-generation event processing systems.