A spiking neural network for extraction of features in colour opponent visual pathways and FPGA implementation

2017 ◽  
Vol 228 ◽  
pp. 119-132 ◽  
Author(s):  
Qi Yan Sun ◽  
Qing Xiang Wu ◽  
Xuan Wang ◽  
Lei Hou
Keyword(s):  
Neural Network ◽  
Visual Pathways ◽  
Fpga Implementation ◽  
Spiking Neural Network

scholarly journals An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

2013 ◽  
Vol 7 ◽  
Author(s):  
Runchun Wang ◽  
Gregory Cohen ◽  
Klaus M. Stiefel ◽  
Tara Julia Hamilton ◽  
Jonathan Tapson ◽  
...  
Keyword(s):  
Neural Network ◽  
Fpga Implementation ◽  
Spiking Neural Network

scholarly journals FPGA Implementation of Self-Organized Spiking Neural Network Controller for Mobile Robots

2014 ◽  
Vol 6 ◽  
pp. 180620 ◽  
Author(s):  
Fangzheng Xue ◽  
Wei Wang ◽  
Nan Li ◽  
Yuchao Yang
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Learning Algorithm ◽  
Fpga Implementation ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Original Solution ◽  
Self Organized ◽  
Receptor Modulation ◽  
Neural Network Controller

Spiking neural network, a computational model which uses spikes to process the information, is good candidate for mobile robot controller. In this paper, we present a novel mechanism for controlling mobile robots based on self-organized spiking neural network (SOSNN) and introduce a method for FPGA implementation of this SOSNN. The spiking neuron we used is Izhikevich model. A key feature of this controller is that it can simulate the process of unconditioned reflex (avoid obstacles using infrared sensor signals) and conditioned reflex (make right choices in multiple T-maze) by spike timing-dependent plasticity (STDP) learning and dopamine-receptor modulation. Experimental results show that the proposed controller is effective and is easy to implement. The FPGA implementation method aims to build up a specific network using generic blocks designed in the MATLAB Simulink environment. The main characteristics of this original solution are: on-chip learning algorithm implementation, high reconfiguration capability, and operation under real time constraints. An extended analysis has been carried out on the hardware resources used to implement the whole SOSNN network, as well as each individual component block.

scholarly journals FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

2014 ◽  
Vol 4 (4) ◽  
pp. 848-852 ◽  
Author(s):  
Pasupathi T ◽  
◽  
Arockia Bazil Raj A ◽  
Arputhavijayaselvi J ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Recognition System ◽  
Fpga Implementation ◽  
Spiking Neural Network

scholarly journals Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

2018 ◽  
Vol 145 ◽  
pp. 488-494 ◽  
Author(s):  
Aleksandr Sboev ◽  
Alexey Serenko ◽  
Roman Rybka ◽  
Danila Vlasov ◽  
Andrey Filchenkov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Spiking Neural Network ◽  
Network Parameters
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