Self-Organization Process in Large Spiking Neural Networks Leading to Formation of Working Memory Mechanism

2013 ◽  
pp. 510-517 ◽  
Author(s):  
Mikhail Kiselev
Keyword(s):  
Neural Networks ◽  
Working Memory ◽  
Spiking Neural Networks ◽  
Self Organization ◽  
Memory Mechanism ◽  
Organization Process

scholarly journals Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks

2020 ◽  
Vol 24 (1) ◽  
pp. 129-139
Author(s):  
Robert Kim ◽  
Terrence J. Sejnowski
Keyword(s):  
Neural Networks ◽  
Working Memory ◽  
Temporal Dynamics ◽  
Spiking Neural Networks ◽  
Inhibitory Signaling

A Developmental Method for Evolving Large-Scale Spiking Neural Networks

2012 ◽  
Vol 35 (12) ◽  
pp. 2633 ◽  
Author(s):  
Xiang-Hong LIN ◽  
Tian-Wen ZHANG ◽  
Gui-Cang ZHANG
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks

scholarly journals Spiking Neural Networks and online learning: An overview and perspectives

2020 ◽  
Vol 121 ◽  
pp. 88-100 ◽  
Author(s):  
Jesus L. Lobo ◽  
Javier Del Ser ◽  
Albert Bifet ◽  
Nikola Kasabov
Keyword(s):  
Neural Networks ◽  
Online Learning ◽  
Spiking Neural Networks

Optimizing the parameters of spiking neural networks for mobile robot implementation

2020 ◽  
Author(s):  
Vahid Azimirad ◽  
Saleh Valizadeh Sotubadi ◽  
Farrokh Janabi Sharifi
Keyword(s):  
Neural Networks ◽  
Mobile Robot ◽  
Spiking Neural Networks

DeepTempo: A Hardware-Friendly Direct Feedback Alignment Multi-Layer Tempotron Learning Rule for Deep Spiking Neural Networks

2021 ◽  
pp. 1-1
Author(s):  
Cong Shi ◽  
Tengxiao Wang ◽  
Junxian He ◽  
Jianghao Zhang ◽  
Liyuan Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Learning Rule ◽  
Spiking Neural Networks ◽  
Direct Feedback

Predicting Networks-on-Chip traffic congestion with Spiking Neural Networks

2021 ◽  
Author(s):  
Aqib Javed ◽  
Jim Harkin ◽  
Liam McDaid ◽  
Junxiu Liu
Keyword(s):  
Neural Networks ◽  
Traffic Congestion ◽  
Spiking Neural Networks ◽  
Networks On Chip ◽  
On Chip

scholarly journals Symmetry perception with spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan K. George ◽  
Cesare Soci ◽  
Mario Miscuglio ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Mirror Symmetry ◽  
Satellite Image ◽  
Image Data ◽  
Coincidence Detection ◽  
Spiking Neural Networks ◽  
Feed Forward Neural Network ◽  
Neuromorphic Computing ◽  
Symmetry Perception ◽  
High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

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