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

685
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...
685
Storage01:23

Storage

155
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...
155
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.2K
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...
1.2K
Long-Term Memory01:18

Long-Term Memory

303
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
303
Neural Circuits01:25

Neural Circuits

1.8K
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...
1.8K
System of Memory01:23

System of Memory

6.6K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.6K

You might also read

Related Articles

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

Sort by
Same author

Kinetic Monte Carlo simulation analysis of the conductance drift in Multilevel HfO<sub>2</sub>-based RRAM devices.

Nanoscale·2024
Same author

Regenerative Potential of Hydroxyapatite-Based Ceramic Biomaterial on Mandibular Cortical Bone: An <i>In Vivo</i> Study.

Biomedicines·2023
Same author

InjectMeAI-Software Module of an Autonomous Injection Humanoid.

Sensors (Basel, Switzerland)·2022
Same author

Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion.

Sensors (Basel, Switzerland)·2022
Same author

Design of an Integrated Subretinal Implant using Cellular Neural Networks for Binary Image Generation in a 130 nm BiCMOS Process.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Hypothermic treatment after computer-controlled compression in minipig: A preliminary report on the effect of epidural vs. direct spinal cord cooling.

Experimental and therapeutic medicine·2018

Related Experiment Video

Updated: Oct 12, 2025

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

8.0K

A Low-Power RRAM Memory Block for Embedded, Multi-Level Weight and Bias Storage in Artificial Neural Networks.

Stefan Pechmann1, Timo Mai2, Julian Potschka2

  • 1Chair of Communications Electronics of University of Bayreuth, 95447 Bayreuth, Germany.

Micromachines
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a novel memory block using resistive memory cells (RRAM) for artificial neural networks (ANNs). This RRAM-based memory offers efficient, non-volatile storage for ANNs, reducing power consumption and enabling adaptable computing systems.

Keywords:
ANNRRAMembedded memorylow-powermemory blockmulti-level

More Related Videos

Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi&#45;Region Brain Studies
08:51

Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi-Region Brain Studies

Published on: April 26, 2024

1.6K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.9K

Related Experiment Videos

Last Updated: Oct 12, 2025

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

8.0K
Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi&#45;Region Brain Studies
08:51

Author Spotlight: Deciphering Memory and Learning Through Neural Implants for Multi-Region Brain Studies

Published on: April 26, 2024

1.6K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.9K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Materials Science

Background:

  • Artificial neural networks (ANNs) are powerful machine learning algorithms for pattern recognition.
  • Storing weights and biases in ANNs requires efficient and reliable memory solutions.
  • Current memory technologies face challenges in integration and power consumption for embedded ANNs.

Purpose of the Study:

  • To introduce a novel memory block for ANNs utilizing resistive memory cells (RRAM).
  • To enable embedded and distributed storage of weights and biases in ANNs.
  • To enhance the energy efficiency and adaptability of ANN computing systems.

Main Methods:

  • Development of a memory block architecture using RRAM cells.
  • Implementation of power gating techniques for reduced energy consumption.
  • Leveraging the non-volatility and multi-level capabilities of RRAM technology.

Main Results:

  • Successful demonstration of embedded and distributed weight and bias storage for ANNs.
  • Significant reduction in power consumption achieved through power gating and RRAM non-volatility.
  • Preservation of data integrity without loss due to the non-volatile nature of RRAM.

Conclusions:

  • The proposed RRAM-based memory block provides an efficient solution for ANN weight and bias storage.
  • The design offers significant power savings and data retention capabilities.
  • The adaptable peripheral circuitry allows for integration with various applications and RRAM technologies.