scholarly journals An Interclass Margin Maximization Learning Algorithm for Evolving Spiking Neural Network

2019 ◽  
Vol 49 (3) ◽  
pp. 989-999 ◽  
Author(s):  
Shirin Dora ◽  
Suresh Sundaram ◽  
Narasimhan Sundararajan
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Spiking Neural Network ◽  
Margin Maximization ◽  
Evolving Spiking Neural Network

scholarly journals Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3276
Author(s):  
Szymon Szczęsny ◽  
Damian Huderek ◽  
Łukasz Przyborowski
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Waveform Analysis ◽  
Spiking Neural Network ◽  
Synaptic Connections ◽  
Practical Application ◽  
Accuracy Parameter ◽  
The Stability ◽  
Time Waveform ◽  
Network Mapping

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

scholarly journals Area- and Energy-Efficient STDP Learning Algorithm for Spiking Neural Network SoC

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 216922-216932
Author(s):  
Giseok Kim ◽  
Kiryong Kim ◽  
Sara Choi ◽  
Hyo Jung Jang ◽  
Seong-Ook Jung
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Learning Algorithm ◽  
Spiking Neural Network

A Biologically Inspired Evolving Spiking Neural Model with Rank-Order Population Coding and a Taste Recognition System Case Study

2011 ◽  
pp. 136-155 ◽  
Author(s):  
S. Soltic ◽  
N. Kasabov
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Rank Order ◽  
Neural Model ◽  
Recognition System ◽  
Population Coding ◽  
Spiking Neural Network ◽  
Biologically Inspired ◽  
Information Encoding ◽  
Evolving Spiking Neural Network

The human brain has an amazing ability to recognize hundreds of thousands of different tastes. The question is: can we build artificial systems that can achieve this level of complexity? Such systems would be useful in biosecurity, the chemical and food industry, security, in home automation, etc. The purpose of this chapter is to explore how spiking neurons could be employed for building biologically plausible and efficient taste recognition systems. It presents an approach based on a novel spiking neural network model, the evolving spiking neural network with population coding (ESNN-PC), which is characterized by: (i) adaptive learning, (ii) knowledge discovery and (iii) accurate classification. ESNN-PC is used on a benchmark taste problem where the effectiveness of the information encoding, the quality of extracted rules and the model’s adaptive properties are explored. Finally, applications of ESNN-PC in recognition of the increasing interest in robotics and pervasive computing are suggested.

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