Mobile Robots' Wall-Following Controller Based on Probabilistic Spiking Neuron Model

2012 ◽  
Vol 588-589 ◽  
pp. 1547-1551 ◽  
Author(s):  
Xiu Qing Wang ◽  
Zeng Guang Hou ◽  
Min Tan ◽  
Yong Ji Wang ◽  
Fei Xie
Keyword(s):  
Neural Networks ◽  
Mobile Robots ◽  
Motor Neurons ◽  
Neuron Model ◽  
Hebbian Learning ◽  
The Novel ◽  
Spiking Neuron ◽  
Learning Rules ◽  
Spiking Neuron Model ◽  
Wall Following

This paper focuses on the third generation of neural networks- Spiking neural networks (SNNs), the novel Spiking neuron model- probabilistic Spiking neuron model (pSNM), and their applications. pSNM is used in mobile robots' behavior control, and a novel mobile robots' wall-following controller based on pSNM is proposed. In the pSNM controller, Spiking time-delayed coding is used for the sensory neurons of the input layer and pSNM is used for the motor neurons in the output layer. Thorpe and Hebbian learning rules are used in the controller. The experimental results show that the controller can control the mobile robots to follow the wall clockwise and counterclockwise successfully. The structure of the controller is simple, and the controller can study online.

scholarly journals The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory

2016 ◽  
Vol 9 (1) ◽  
pp. 117-134 ◽  
Author(s):  
Peter Duggins ◽  
Terrence C. Stewart ◽  
Xuan Choo ◽  
Chris Eliasmith
Keyword(s):  
Working Memory ◽  
Neuron Model ◽  
Spiking Neuron ◽  
Spiking Neuron Model

Reduced Representation by Neural Networks with Restricted Receptive Fields

1995 ◽  
Vol 7 (3) ◽  
pp. 507-517 ◽  
Author(s):  
Marco Idiart ◽  
Barry Berk ◽  
L. F. Abbott
Keyword(s):  
Neural Networks ◽  
Biological Networks ◽  
Input Data ◽  
Hebbian Learning ◽  
Receptive Fields ◽  
Principal Component ◽  
Data Sets ◽  
Reduced Representation ◽  
Learning Rules ◽  
Reduced Representations

Model neural networks can perform dimensional reductions of input data sets using correlation-based learning rules to adjust their weights. Simple Hebbian learning rules lead to an optimal reduction at the single unit level but result in highly redundant network representations. More complex rules designed to reduce or remove this redundancy can develop optimal principal component representations, but they are not very compelling from a biological perspective. Neurons in biological networks have restricted receptive fields limiting their access to the input data space. We find that, within this restricted receptive field architecture, simple correlation-based learning rules can produce surprisingly efficient reduced representations. When noise is present, the size of the receptive fields can be optimally tuned to maximize the accuracy of reconstructions of input data from a reduced representation.

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