CRBA: A Competitive Rate-Based Algorithm Based on Competitive Spiking Neural Networks

In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.

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Skyrmionic implementation of Spike Time Dependent Plasticity (STDP) enabled Spiking Neural Network (SNN) under supervised learning scheme

2018 4th IEEE International Conference on Emerging Electronics (ICEE) ◽

10.1109/icee44586.2018.8937850 ◽

2018 ◽

Author(s):

Upasana Sahu ◽

Kushaagra Goyal ◽

Utkarsh Saxena ◽

Tanmay Chavan ◽

Udayan Ganguly ◽

...

Keyword(s):

Neural Network ◽

Supervised Learning ◽

Time Dependent ◽

Spiking Neural Network ◽

Spike Time ◽

Dependent Plasticity ◽

Learning Scheme ◽

Spike Time Dependent Plasticity

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PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

The Scientific World JOURNAL ◽

10.1155/2014/468176 ◽

2014 ◽

Vol 2014 ◽

pp. 1-16 ◽

Cited By ~ 11

Author(s):

Kanokmon Rujirakul ◽

Chakchai So-In ◽

Banchar Arnonkijpanich

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Expectation Maximization ◽

High Speed ◽

Expectation Maximization Algorithm ◽

Parallel Architecture ◽

Principal Component ◽

Eigenvalue Decomposition ◽

Computational Time ◽

Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

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