scholarly journals BIOLOGICALLY INSPIRED EDGE DETECTION USING SPIKING NEURAL NETWORKS AND HEXAGONAL IMAGES

2011 ◽  
Keyword(s):  
Neural Networks ◽  
Edge Detection ◽  
Spiking Neural Networks ◽  
Biologically Inspired

scholarly journals A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

2021 ◽  
Author(s):  
Daniel Auge ◽  
Julian Hille ◽  
Etienne Mueller ◽  
Alois Knoll
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Industrial Applications ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Power Efficient ◽  
Signal Encoding ◽  
Uniform Nomenclature ◽  
Theoretical Foundations ◽  
Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

Emulating Spiking Neural Networks for edge detection on FPGA hardware

2009 ◽  
Author(s):  
Brendan Glackin ◽  
Jim Harkin ◽  
Thomas M. McGinnity ◽  
Liam P. Maguire ◽  
Qingxiang Wu
Keyword(s):  
Neural Networks ◽  
Edge Detection ◽  
Spiking Neural Networks

scholarly journals The POEtic Electronic Tissue and Its Role in the Emulation of Large-Scale Biologically Inspired Spiking Neural Networks Models

Complexus ◽  
2006 ◽  
Vol 3 (1-3) ◽  
pp. 32-47 ◽  
Author(s):  
J.Manuel Moreno ◽  
Yann Thoma ◽  
Eduardo Sanchez ◽  
Jan Eriksson ◽  
Javier Iglesias ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks ◽  
Biologically Inspired

Compact Hardware Synthesis of Stochastic Spiking Neural Networks

2019 ◽  
Vol 29 (08) ◽  
pp. 1950004 ◽  
Author(s):  
Fabio Galán-Prado ◽  
Alejandro Morán ◽  
Joan Font ◽  
Miquel Roca ◽  
Josep L. Rosselló
Keyword(s):  
Neural Networks ◽  
Processing Speed ◽  
Back Propagation ◽  
High Volume ◽  
High Density ◽  
Digital Design ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Proposed Model ◽  
Technical Interest

Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation. The proposed stochastic SNN model (SSNN) is compared with previous SSNN models, achieving a higher processing speed. We also show how the proposed model can be scaled to high-volume neural networks trained by using back propagation and applied to a pattern classification task. The proposed model achieves better results compared with other recently-published SNN models configured with unsupervised STDP learning.

scholarly journals A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Jim Harkin ◽  
Fearghal Morgan ◽  
Liam McDaid ◽  
Steve Hall ◽  
Brian McGinley ◽  
...  
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Fault Tolerant ◽  
Network On Chip ◽  
Spiking Neurons ◽  
Spiking Neural Networks ◽  
Neural Network Architecture ◽  
Biologically Inspired ◽  
Field Programmable ◽  
On Chip

FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs) applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE), incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

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