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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...

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...

Integration of Synaptic Events

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...

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.

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

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Related Experiment Video

Updated: Nov 9, 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

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing.

Hongyu Bian¹, Yi Yiing Goh^1,2, Yuxia Liu^1,3

¹Department of Chemistry, National University of Singapore, Singapore, 117543, Singapore.

Advanced Materials (Deerfield Beach, Fla.)

|April 10, 2021

Summary

This summary is machine-generated.

Memristive hardware offers energy-efficient, brain-inspired computing by mimicking neurons and synapses. Recent advances in memristive materials and strategies enable faster, reliable, and low-power neuromorphic applications.

Keywords:

artificial synapses memristive materials neurons synaptic plasticity

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Last Updated: Nov 9, 2025

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

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Published on: March 9, 2019

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Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro

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Published on: December 19, 2017

Area of Science:

Materials Science
Computer Engineering
Neuroscience

Background:

Neuromorphic computing aims for energy-efficient intelligent systems, surpassing conventional architectures.
Memristive hardware emulates biological neurons and synapses for brain-inspired computing.
Current systems face limitations in speed, power consumption, and area efficiency.

Purpose of the Study:

To highlight recent advances in memristive materials for neuromorphic computing.
To present the working principles of biological neurons and synapses and their memristive emulation.
To discuss device requirements and future challenges in memristive neuromorphic applications.

Main Methods:

Review of recent literature on memristive materials and synaptic emulation strategies.
Analysis of device structures and operational principles under various external stimuli (electric, magnetic, optical).
Examination of underlying physical mechanisms in memristive materials.

Main Results:

Memristive devices show promise for high-speed, low-power, and area-efficient neuromorphic computing.
Diverse memristive materials and physical mechanisms enable emulation of synaptic functions.
Advancements facilitate fast, reliable, and energy-efficient neuromorphic applications.

Conclusions:

Memristive hardware is a key enabler for next-generation energy-efficient intelligent systems.
Further development in memristive materials and device engineering is crucial for advancing computing science.
Overcoming current challenges will pave the way for widespread adoption of neuromorphic computing.