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The Synapse

The Synapse

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.

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...

Chemical Synapses

Chemical Synapses

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...

Overview of Synapses

Overview of Synapses

A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...

Synaptic Signaling

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...

Neuronal Communication

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...

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Related Experiment Video

Updated: Oct 15, 2025

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

Artificial Synapse Based on a 2D-SnO₂ Memtransistor with Dynamically Tunable Analog Switching for Neuromorphic

Chi-Hsin Huang¹, Hsuan Chang¹, Tzu-Yi Yang²

¹Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States.

ACS Applied Materials & Interfaces

|October 29, 2021

Summary

Researchers developed a novel 2D tin oxide memtransistor for neuromorphic computing. This gate-tunable device enables complex learning by precisely controlling analog switching behavior, advancing AI hardware.

Keywords:

analog switching memtransistor neuromorphic computing tin oxide two-dimensional oxide nanosheet

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Last Updated: Oct 15, 2025

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

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Published on: March 9, 2019

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

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Published on: June 24, 2015

A Method for Growing Bio-memristors from Slime Mold

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Published on: November 2, 2017

Area of Science:

Materials Science
Nanotechnology
Computer Engineering

Background:

Neuromorphic computing requires advanced devices that mimic biological synapses.
Conventional memristors lack the fine-tuning capabilities needed for complex learning.
Two-dimensional (2D) materials offer unique properties for electronic devices.

Purpose of the Study:

To demonstrate a new gate-tunable memtransistor based on 2D tin oxide (SnO2).
To explore the potential of this memtransistor for next-generation neuromorphic computing.
To investigate the device's capability for heterosynaptic analog switching and complex learning schemes.

Main Methods:

Fabrication of polycrystalline 2D SnO2 memristors using a low-temperature, vacuum-free liquid metal process.
Characterization of resistive switching properties, including digital/analog switching, multistate storage, and gate-tunability.
Demonstration of dynamic modulation of analog switching behavior via gate bias for synaptic emulation.

Main Results:

Successful demonstration of a gate-tunable 2D SnO2 memtransistor, integrating memristor and transistor functionalities.
The device exhibits excellent digital/analog resistive switching, multistate storage, and crucial gate-tunability.
Gate tunability enables heterosynaptic analog switching with good linearity and improved conductance change ratio for high recognition accuracy.

Conclusions:

The developed gate-tunable 2D oxide memtransistor is a significant advancement for neuromorphic device technology.
This device offers an extra degree of freedom for designing sophisticated learning schemes.
It opens new avenues for creating more efficient and powerful artificial intelligence hardware.