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

The Synapse02:47

The Synapse

133.2K
Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
133.2K
Bipolar Disorder01:30

Bipolar Disorder

754
Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
754
Bipolar Junction Transistor01:22

Bipolar Junction Transistor

1.5K
Bipolar Junction Transistors (BJTs) are essential elements in electronic circuits, playing a crucial role in the functionality of amplifiers, memories, and microprocessors. These transistors can be designed as NPN or PNP based on their doping patterns. They consist of three layers: the emitter, base, and collector. The configuration of these layers and their respective doping levels—with N-type or P-type impurities—define the transistor's type and its operational...
1.5K
Electrical Synapses01:28

Electrical Synapses

10.7K
Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
10.7K
Chemical Synapses01:26

Chemical Synapses

11.6K
Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
11.6K
Chemical Synapses01:26

Chemical Synapses

4.6K
Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Error-aware probabilistic training for memristive neural networks.

Nature communications·2025
Same author

A stable monoclinic variant and resultant robust ferroelectricity in single-crystalline hafnia-based films.

Nature communications·2025
Same author

Fully memristive spiking neural network for energy-efficient graph learning.

Science advances·2025
Same author

Memristor-based feature learning for pattern classification.

Nature communications·2025
Same author

Stochastic neuro-fuzzy system implemented in memristor crossbar arrays.

Science advances·2024
Same author

Tracking ocean heat uptake during the surface warming hiatus.

Nature communications·2016

Related Experiment Video

Updated: Feb 3, 2026

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.4K

Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing.

Rui Wang1,2, Tuo Shi3,4, Xumeng Zhang5,6

  • 1Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China. wangrui@ime.ac.cn.

Materials (Basel, Switzerland)
|October 31, 2018
PubMed
Summary

Researchers developed a novel memristive device for artificial synapses, demonstrating essential synaptic functions for neuromorphic computing with low energy consumption. This device shows promise for advanced artificial neural networks.

Keywords:
artificial synapsememristorneuromorphic computing

More Related Videos

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

8.3K
Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number
18:11

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Published on: November 16, 2010

36.7K

Related Experiment Videos

Last Updated: Feb 3, 2026

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.4K
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

8.3K
Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number
18:11

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Published on: November 16, 2010

36.7K

Area of Science:

  • Materials Science and Engineering
  • Neuroscience and Neuromorphic Engineering

Background:

  • Memristive devices with bipolar analog resistive switching are crucial for building memristor-based neuromorphic computing systems.
  • Existing synaptic devices often require a forming step or rely on filamentary switching, posing challenges for integration and stability.

Purpose of the Study:

  • To report a novel, fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, non-filamentary memristive device (Pd/Al₂O₃/TaOₓ/Ta) for artificial synapse applications.
  • To demonstrate the device's capability in emulating biological synaptic functions and its suitability for artificial neural networks (ANNs).

Main Methods:

  • Fabrication of a Pd/Al₂O₃/TaOₓ/Ta memristive device compatible with CMOS processes.
  • Characterization of the device's bipolar analog resistive switching behavior.
  • Implementation and testing of synaptic functions: long-term potentiation/depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP).
  • Evaluation of conductance state accuracy and linearity for ANN applications.

Main Results:

  • The developed memristive device exhibits forming-free, non-filamentary bipolar analog switching behavior, functioning effectively as an artificial synapse.
  • Key synaptic functions including LTP/LTD, PPF, and STDP were successfully emulated with a low switching energy of approximately 50 pJ per spike.
  • The device achieved target conductance states with minimal deviation (<1%) and demonstrated a nearly linear conductance change after optimizing the training scheme, despite initial non-linear characteristics.

Conclusions:

  • The Pd/Al₂O₃/TaOₓ/Ta memristive device is a promising emulator of biological synapses, offering a robust platform for memristor-based neuromorphic computing.
  • The device's CMOS compatibility, forming-free operation, and demonstrated synaptic functionalities make it highly suitable for advanced artificial neural network applications.