CASE STUDY ON A SELF-ORGANIZING SPIKING NEURAL NETWORK FOR ROBOT NAVIGATION

2010 ◽  
Vol 20 (06) ◽  
pp. 501-508 ◽  
Author(s):  
ERIC NICHOLS ◽  
L. J. McDAID ◽  
N. H. SIDDIQUE
Keyword(s):  
Neural Network ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Spiking Neural Network ◽  
Integrate And Fire ◽  
Dynamic Synapses ◽  
Route Information ◽  
Wall Following ◽  
Self Organizing

This paper presents a Spiking Neural Network (SNN) architecture for mobile robot navigation. The SNN contains 4 layers where dynamic synapses route information to the appropriate neurons in each layer and the neurons are modeled using the Leaky Integrate and Fire (LIF) model. The SNN learns by self-organizing its connectivity as new environmental conditions are experienced and consequently knowledge about its environment is stored in the connectivity. Also a novel feature of the proposed SNN architecture is that it uses working memory, where present and previous sensor states are stored. Results are presented for a wall following application.

Control chart pattern clustering using a new self-organizing spiking neural network

2008 ◽  
Vol 222 (10) ◽  
pp. 1201-1211 ◽  
Author(s):  
D T Pham ◽  
M S Packianather ◽  
E Y A Charles
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Neural Network Model ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Pattern Clustering ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Spiking Neural Network Model ◽  
Self Organizing

This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection delays instead of the previous approaches of delay selection. Here the tuned delays compensate the differences in the input firing times of temporal patterns and enables them to coincide. The coincidence detection capability of the spiking neuron has been utilized for pattern clustering. The structure of the network is similar to that of a Kohonen self-organizing map (SOM) except that the output layer neurons are coincidence detecting spiking neurons. An input pattern is represented by the neuron that is the first to fire among all the competing spiking neurons. Clusters within the input data are identified with the location of the winning neurons and their firing times. The proposed self-organized delay adaptation spiking neural network (SODA_SNN) has been utilized to cluster control chart patterns. The trained network obtained an average clustering accuracy of 96.1 per cent on previously unseen test data. This was achieved with a network of 8 × 8 spiking neurons trained for 20 epochs containing 1000 training examples. The improvement in clustering accuracy achieved by the proposed SODA_SNN on the unseen test data was twice as much as that on the training data when compared to the SOM.

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