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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...

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...

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.

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...

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Related Experiment Video

Updated: May 11, 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

Liquid ferrofluid synapses for spike-based neuromorphic learning.

Charanraj Mohan¹, Marco Crepaldi¹, Diego Torazza²

¹Electronic Design Laboratory, Istituto Italiano di Tecnologia, Via Melen 83, Genova 16152, Liguria, Italy.

Materials Horizons

|April 17, 2025

Summary

This summary is machine-generated.

Researchers developed a novel liquid-state neuromorphic device using ferrofluids. This ferrofluid synapse offers high endurance and fault tolerance, overcoming limitations of solid-state memory for advanced computing applications.

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Area of Science:

Neuromorphic Engineering
Materials Science
Computer Science

Background:

Solid-state memory devices face challenges like limited endurance and static sensitivity for neuromorphic applications.
Existing devices often require specific conditions such as current compliance and forming procedures, hindering widespread adoption.

Purpose of the Study:

To introduce a liquid-state neuromorphic device utilizing ferrofluids as a potential alternative to solid-state synapses.
To investigate the properties and performance of this novel liquid synapse for computing applications.

Main Methods:

Fabrication of a liquid synapse using ferrofluid stabilized with oleic acid.
Characterization of the device's resistance switching behavior, endurance, and fault tolerance.
Development of a low-power inference system and demonstration of digit classification using unsupervised learning.

Main Results:

The ferrofluid synapse exhibits short-term plasticity with high endurance, fault tolerance, and deterministic switching without forming or compliance requirements.
Stabilization of nanoparticles using oleic acid improved yield and reduced resistance variance.
A low-power inference system demonstrated robustness against system errors, and the device successfully classified digits.

Conclusions:

Liquid-state ferrofluid synapses offer significant advantages over solid-state devices for neuromorphic engineering.
The developed device shows promise for scalable, low-power, and robust computing systems.