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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...

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Related Experiment Video

Updated: Dec 31, 2025

Preparation of Neuronal Co-cultures with Single Cell Precision

Preparation of Neuronal Co-cultures with Single Cell Precision

Published on: May 20, 2014

Parallel weight update protocol for a carbon nanotube synaptic transistor array for accelerating neuromorphic

Sungho Kim¹, Yongwoo Lee, Hee-Dong Kim

¹Department of Electrical Engineering, Sejong University, Seoul 05006, Korea.

|January 9, 2020

Summary

This summary is machine-generated.

This study introduces a novel weight update protocol for carbon nanotube synaptic transistor arrays, enabling selective and parallel updates for efficient neuromorphic computing and big data processing.

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Area of Science:

Materials Science
Computer Engineering
Artificial Intelligence

Background:

Conventional von Neumann architecture faces efficiency limitations.
Neuromorphic computing leverages artificial neural networks for parallel processing.
Synaptic device arrays in crossbar geometry are key for neuromorphic systems.

Purpose of the Study:

To address challenges in selective and parallel weight updates in synaptic crossbar arrays.
To demonstrate a new weight update protocol for carbon nanotube synaptic transistors.
To showcase the application of this protocol in image feature extraction.

Main Methods:

Experimental demonstration of a weight update protocol.
Utilizing a carbon nanotube synaptic transistor array.
Exploiting a localized carrier trapping mechanism with three-terminal devices.

Main Results:

Achieved selective and parallel weight updates.
Successfully trained a 9x8 synaptic array.
The array performed four simultaneous convolution operations for image feature extraction.

Conclusions:

The demonstrated protocol enables efficient manipulation of big data.
Massive parallelism and robustness are key features for neuromorphic computing.
This work advances the development of practical neuromorphic systems.