Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Electrical Synapses

Electrical Synapses

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...

You might also read

Related Articles

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

Sort by

Same author

Elemental Selector for High-Density Memory Integration.

Nano-micro letters·2026

Same author

Synthesis of N-CF<sub>3</sub> Amides and Ureas from N-CF<sub>3</sub> Secondary Amines and Acyl Chlorides.

The Journal of organic chemistry·2026

Same author

PSO-Transformer for mapping soil heavy metals using UAV hyperspectral data with spectral calibration and indices.

iScience·2026

Same author

Pathological triad of perioperative acute kidney injury: renal microcirculatory hypoxia, mitochondrial damage, and immuno-metabolic reprogramming.

Frontiers in immunology·2026

Same author

Ivarmacitinib is associated with rapid clinical improvement and immune remodeling in palmoplantar pustulosis: A multicenter, single-arm clinical trial.

Journal of the American Academy of Dermatology·2026

Same author

Amphibian-inspired neuromorphic dynamic vision systems based on ferroelectric field-effect transistor.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Oct 5, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

A Reconfigurable Two-WSe₂ -Transistor Synaptic Cell for Reinforcement Learning.

Yue Zhou^1,2, Yasai Wang¹, Fuwei Zhuge³

¹Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430000, China.

Advanced Materials (Deerfield Beach, Fla.)

|February 1, 2022

Summary

Researchers developed a novel two-transistor (2T) device using WSe2 ferroelectric transistors to efficiently implement reward-modulated spike-timing-dependent plasticity (R-STDP) for reinforcement learning (RL). This breakthrough enables low-power, area-efficient hardware for artificial general intelligence.

Keywords:

2D semiconductors ferroelectric materials reinforcement learning reward-modulated spike-timing-dependent plasticity synaptic device

More Related Videos

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

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

Published on: March 9, 2019

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Oct 5, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

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

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

Published on: March 9, 2019

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Neuromorphic Engineering
Artificial Intelligence
Materials Science

Background:

Reward-modulated spike-timing-dependent plasticity (R-STDP) is a key brain-inspired rule for reinforcement learning (RL).
Existing hardware implementations of R-STDP using silicon CMOS technology suffer from high power consumption and large footprints.
There is a need for more efficient and compact hardware solutions for R-STDP to advance artificial general intelligence.

Purpose of the Study:

To demonstrate a novel two-transistor (2T) device for efficient hardware implementation of R-STDP.
To achieve reconfigurable synaptic behavior and multilevel conductance states using WSe2 ferroelectric transistors.
To validate the R-STDP learning rule for training spiking neural networks on a challenging task.

Main Methods:

Fabrication and characterization of a parallel-structured 2T unit based on WSe2 ferroelectric transistors.
Exploitation of ferroelectric polarization to achieve reconfigurable n-type and p-type channel behavior.
Implementation of R-STDP learning rules by applying reward signals to the (anti-)STDP components of the 2T cell.

Main Results:

The 2T unit demonstrated reconfigurable polarity, multilevel (>6 bit) conductance states, ultralow nonlinearity, and a large Gmax/Gmin ratio (30).
Successful realization of R-STDP learning rules for training a spiking neural network.
The trained network solved the classical cart-pole problem with ultralow power consumption (32 pJ per forward process) and high area efficiency (100 µm²).

Conclusions:

The developed WSe2 ferroelectric 2T device offers a promising pathway for low-power and area-efficient hardware implementation of R-STDP.
This technology has significant potential for advancing reinforcement learning applications and artificial general intelligence.
The demonstrated device overcomes the limitations of conventional CMOS-based R-STDP hardware.