Biologically Inspired Agent System Based on Spiking Neural Network

2010 ◽  
pp. 110-119 ◽  
Author(s):  
Bartłomiej Józef Dzieńkowski ◽  
Urszula Markowska-Kaczmar
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Biologically Inspired ◽  
Agent System

A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation

2010 ◽  
Vol 74 (1-3) ◽  
pp. 129-139 ◽  
Author(s):  
Jindong Liu ◽  
David Perez-Gonzalez ◽  
Adrian Rees ◽  
Harry Erwin ◽  
Stefan Wermter
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Sound Source ◽  
Spiking Neural Network ◽  
Auditory Midbrain ◽  
Biologically Inspired ◽  
Source Localisation ◽  
Spiking Neural Network Model

Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network

2011 ◽  
Vol 11 (2) ◽  
pp. 2035-2041 ◽  
Author(s):  
Aboul Ella Hassanien ◽  
Hameed Al-Qaheri ◽  
El-Sayed A. El-Dahshan
Keyword(s):  
Neural Network ◽  
Boundary Detection ◽  
Ultrasound Images ◽  
Spiking Neural Network ◽  
Biologically Inspired

scholarly journals Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 165 ◽  
Author(s):  
Krishnamurthy V. Vemuru
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Image Classification ◽  
Spiking Neural Network ◽  
Edge Detector ◽  
Time Step ◽  
Biologically Inspired ◽  
Total Exposure Time ◽  
Canny Edge ◽  
Low Exposure

We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 < t < T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.

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.

scholarly journals Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
A. Espinal ◽  
H. Rostro-Gonzalez ◽  
M. Carpio ◽  
E. I. Guerra-Hernandez ◽  
M. Ornelas-Rodriguez ◽  
...  
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Pattern Generator ◽  
Quadruped Robot ◽  
Spiking Neural Network ◽  
Resource Usage ◽  
Hexapod Robot ◽  
Biologically Inspired ◽  
Robot Locomotion ◽  
Locomotion System

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network

2015 ◽  
Vol 18 ◽  
pp. 91-101 ◽  
Author(s):  
Sneha Singh ◽  
Deep Gupta ◽  
R.S. Anand ◽  
Vinod Kumar
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Medical Image ◽  
Spiking Neural Network ◽  
Biologically Inspired ◽  
Medical Image Fusion
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