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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...

The Role of Ion Channels in Neuronal Computation

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....

Neural Circuits

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...

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Related Experiment Video

Updated: May 5, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Published on: April 15, 2015

Recent Progress on Heterojunction-Based Memristors and Artificial Synapses for Low-Power Neural Morphological

Zhi-Xiang Yin¹, Hao Chen¹, Sheng-Feng Yin¹

¹School of Physics and Optoelectronic Engineering & Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou, Guangdong, 510006, P. R. China.

Small (Weinheim an Der Bergstrasse, Germany)

|March 19, 2025

Summary

Heterojunctions significantly enhance memristors and artificial synapses for low-power neural computing. Optimizing these devices improves energy efficiency, stability, and durability for advanced AI applications.

Keywords:

artificial synapses deep learning energy consumption heterojunction memristors neuromorphic computing

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Published on: March 9, 2019

Area of Science:

Materials Science
Neuroscience
Computer Engineering

Background:

Memristors and artificial synapses are key for neural morphological computing.
Low energy consumption is crucial for these applications.
Heterojunctions show promise in improving device performance and energy efficiency.

Purpose of the Study:

To review recent advancements in heterojunction-based memristors and artificial synapses.
To discuss their applications in neuromorphic computing and deep learning.
To identify challenges and propose solutions for future development.

Main Methods:

Summarizing working mechanisms of heterojunction memristors.
Analyzing material selection, structure design, and fabrication techniques.
Reviewing applications and performance optimization strategies.

Main Results:

Heterojunction optimization reduces energy consumption in memristors and artificial synapses.
Improved material composition, interface characteristics, and device structures enhance stability and durability.
Heterojunctions support low-power neural morphological computing systems.

Conclusions:

Heterojunctions are vital for developing energy-efficient memristors and artificial synapses.
Further research is needed to overcome existing bottlenecks.
This review provides insights for creating high-performance neuromorphic devices.