Biologically-Inspired Spiking Neural Networks with Hebbian Learning for Vision Processing

Long, Lyle N.; Gupta, Ankur

doi:10.2514/6.2008-885

Cited by 13 publications

(15 citation statements)

References 31 publications

(37 reference statements)

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“…SNN has been demonstrated to be more plausible ther traditional neural networks, not only because of its biologically inspired parameters, but also se of its use of spatial-temporal information in computation. The temporal information can simply be sed as the rate of pulsation in SNN (Long & Gupta 2008). ig.…”

Section: Neural Network-based Edge Detection Methodsmentioning

confidence: 99%

Edge Detection Method Based on Neural Networks for COMS MI Images

Lee¹,

Park²,

Woo³

2016

Journal of Astronomy and Space Sciences

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Communication, Ocean And Meteorological Satellite (COMS) Meteorological Imager (MI) images are processed for radiometric and geometric correction from raw image data. When intermediate image data are matched and compared with reference landmark images in the geometrical correction process, various techniques for edge detection can be applied. It is essential to have a precise and correct edged image in this process, since its matching with the reference is directly related to the accuracy of the ground station output images. An edge detection method based on neural networks is applied for the ground processing of MI images for obtaining sharp edges in the correct positions. The simulation results are analyzed and characterized by comparing them with the results of conventional methods, such as Sobel and Canny filters.

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Section: Neural Network-based Edge Detection Methodsmentioning

confidence: 99%

Edge Detection Method Based on Neural Networks for COMS MI Images

Lee¹,

Park²,

Woo³

2016

Journal of Astronomy and Space Sciences

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“…A typical laptop today has roughly the computational power of a cockroach nervous system [41], which is about a million times less powerful than the human brain. Assuming Moore's law remained valid, it would take about 40 years for laptops to reach human computational power.…”

Section: Animalmentioning

confidence: 99%

“…Nor are they floating point or integer processors as in a computer. They are spiking neural networks [39][40][41] with neurons that fire at roughly 50 Hz. In addition, there are roughly 150 different kinds of neurons in the human body.…”

Section: Neurosciencementioning

confidence: 99%

The Requirements and Possibilities of Creating Conscious Systems

Long

Kelley

2009

AIAA Infotech@Aerospace Conference

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This paper discusses the psychological, philosophical and neurological definitions of consciousness and the prospects for the development of a conscious machine in the foreseeable future. Various definitions of consciousness are introduced and discussed within the different fields mentioned. We conclude that a conscious machine may be within the realm of engineering possibilities if current technological developments, especially Moore's Law, continue at their current pace. Given the complexity of cognition and consciousness a hybrid architecture appears to offer the best solution for the implementation of a complex system of systems which functionally approximates a human mind. Ideally, this architecture would include traditional symbolic representations as well as distributed representations which approximate the nonlinear dynamics seen in the human brain.

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“…In order to be able to make significant advancements in answering some tough questions as discussed above, it is imperative that corresponding researches should not only address or investigate the biological but psychological/cognition conception of visual processing. Moreover, many researches support the idea that artificial neural networks can be leveraged on in the simulation of various hypothetical conceptions relating to nervous and cognition queries (Zhang et al 2011;Long and Gupta 2008;Yamazaki and Igarashi 2013); this is reasonable since neural networks are artificial learning systems motivated by biological and psychological paradigms.…”

Section: Introductionmentioning

confidence: 99%

Banknote recognition: investigating processing and cognition framework using competitive neural network

Oyedotun

Khashman

2016

Cogn Neurodyn

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Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

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Biologically-Inspired Spiking Neural Networks with Hebbian Learning for Vision Processing

Cited by 13 publications

References 31 publications

Edge Detection Method Based on Neural Networks for COMS MI Images

Edge Detection Method Based on Neural Networks for COMS MI Images

The Requirements and Possibilities of Creating Conscious Systems

Banknote recognition: investigating processing and cognition framework using competitive neural network

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