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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.

Weighted Mean

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Electrical Synapses

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Related Experiment Video

Updated: Feb 12, 2026

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

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Published on: November 16, 2010

Weighted Synapses Without Carry Operations for RRAM-Based Neuromorphic Systems.

Yan Liao¹, Ning Deng¹, Huaqiang Wu¹

¹Institute of Microelectronics, Tsinghua University, Beijing, China.

Frontiers in Neuroscience

|April 5, 2018

Summary

Weighted synapses improve RRAM-based neuromorphic systems by enhancing training speed and accuracy. This method optimizes parallel updating schemes, overcoming limitations of sign stochastic gradient descent (SGD) and RRAM device variations.

Keywords:

neural networks online learning resistive random-access memory (RRAM)sign stochastic gradient descent weighted synapses

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Area of Science:

Neuromorphic Engineering
Materials Science

Background:

Sign stochastic gradient descent (SGD) accelerates neural network training in RRAM-based systems but can reduce accuracy.
RRAM device non-idealities like variations and limited intermediate states hinder convergence in parallel updating schemes.

Purpose of the Study:

To analyze the impact of RRAM non-idealities on parallel updating schemes.
To propose an optimized method for RRAM-based analog neuromorphic systems.

Main Methods:

Introduced a weighted synapse method with major and minor synapses having different gain factors.
Analyzed the impact of weighted synapses on intermediate state resolution and SGD distortion.
Evaluated the method's performance on MNIST recognition tasks.

Main Results:

Weighted synapses exponentially increase equivalent intermediate states, improving resolution without carry operations.
The method generates a suitable Δ, mitigating sign SGD distortion and simplifying circuit overhead.
Achieved highly optimized convergence and significantly reduced error rates, demonstrating robustness against RRAM variations.

Conclusions:

Weighted synapses offer a robust and efficient solution for RRAM-based analog neuromorphic systems.
The proposed method enhances training acceleration and accuracy while being resilient to device variations.