Timing by coincidence detection: What's all the noise about?

2012 ◽  
Author(s):  
Catalin V. Buhusi ◽  
Sorinel Oprisan
Keyword(s):  
Coincidence Detection

F-18-FDG Positron Imaging in Oncological Patients: Gamma Camera Coincidence Detection versus Dedicated PET

1999 ◽  
Vol 38 (04) ◽  
pp. 108-114 ◽  
Author(s):  
H.-J. Kaiser ◽  
U. Cremerius ◽  
O. Sabri ◽  
M. Schreckenberger ◽  
P. Reinartz ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Spatial Resolution ◽  
Gamma Camera ◽  
Lesion Detection ◽  
Coincidence Detection ◽  
System Sensitivity ◽  
Oncological Patients ◽  
Phantom Studies ◽  
Pet System ◽  
Dedicated Pet

Summary Aim of the present study was to investigate the feasibility of 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (FDG) imaging in oncological patients with a dual head gamma camera modified for coincidence detection (MCD). Methods: Phantom studies were done to determine lesion detection at various lesion-to-background ratios, system sensitivity and spatial resolution. Thirty-two patients with suspected or known malignant disease were first studied with a dedicated full-ring PET system (DPET) applying measured attenuation correction and subsequently with an MCD system without attenuation correction. MCD images were first interpreted without knowledge of the DPET findings. In a second reading, MCD and DPET were evaluated simultaneously. Results: The phantom studies revealed a comparable spatial resolution for DPET and MCD (5.9 × 6.3 × 4.2 mm vs. 5.9 × 6.5 × 6.0 mm). System sensitivity of MCD was less compared to DPET (91 cps/Bq/ml/cmF0V vs. 231 cps/ Bq/ml/cmFOv). At a lesion-to-background ratio of 4:1, DPET depicted a minimal phantom lesion of 1.0 cm in diameter, MCD a minimal lesion of 1.6 cm. With DPET, a total of 91 lesions in 27 patients were classified as malignant. MCD without knowledge of DPET results revealed increased FDG uptake in all patients with positive DPET findings. MCD detected 72 out of 91 DPET lesions (79.1 %). With knowledge of the DPET findings, 11 additional lesions were detected (+12%). MCD missed lesions in six patients with relevance for staging in two patients. All lesions with a diameter above 18 mm were detected. Conclusion: MCD FDG imaging yielded results comparable to dedicated PET in most patients. However, a considerable number of small lesions clearly detectable with DPET were not detected by MCD alone. Therefore, MCD cannot yet replace dedicated PET in all oncological FDG studies. Further technical refinement of this new method is needed to improve image quality (e.g. attenuation correction).

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.

Optoelectronic Coincidence Detection with Two‐Dimensional Bi 2 O 2 Se Ferroelectric Field‐Effect Transistors

2021 ◽  
pp. 2103982
Author(s):  
Jian‐Min Yan ◽  
Jing‐Shi Ying ◽  
Ming‐Yuan Yan ◽  
Zhao‐Cai Wang ◽  
Shuang‐Shuang Li ◽  
...  
Keyword(s):  
Field Effect ◽  
Field Effect Transistors ◽  
Coincidence Detection ◽  
Two Dimensional

scholarly journals A Biologically Plausible Computational Model for Auditory Object Recognition

2009 ◽  
Vol 101 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Eric Larson ◽  
Cyrus P. Billimoria ◽  
Kamal Sen
Keyword(s):  
Object Recognition ◽  
Spike Train ◽  
Nearest Neighbor ◽  
Primary Auditory Cortex ◽  
Spike Trains ◽  
Coincidence Detection ◽  
Model Based ◽  
Plausible Model ◽  
Field L ◽  
Song Discrimination

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

Modelling the sensitivity of cells in the anteroventral cochlear nucleus to spatiotemporal discharge patterns

1992 ◽  
Vol 336 (1278) ◽  
pp. 403-406 ◽  
Keyword(s):  
Cochlear Nucleus ◽  
Auditory Nerve ◽  
Cross Correlation ◽  
Low Frequency ◽  
Phase Spectrum ◽  
Coincidence Detection ◽  
Potential Mechanism ◽  
Complex Sounds ◽  
Anteroventral Cochlear Nucleus ◽  
Discharge Patterns

This study investigates a potential mechanism for the processing of acoustic information that is encoded in the spatiotemporal discharge patterns of auditory nerve (AN) fibres. Recent physiological evidence has demonstrated that some low-frequency cells in the anteroventral cochlear nucleus (AVCN) are sensitive to manipulations of the phase spectrum of complex sounds (Carney 1990 b ). These manipulations result in systematic changes in the spatiotemporal discharge patterns across groups of low-frequency an fibres having different characteristic frequencies (CFS). One interpretation of these results is that these neurons in the AVCN receive convergent inputs from AN fibres with different CFS, and that the cells perform a coincidence detection or cross-correlation upon their inputs. This report presents a model that was developed to test this interpretation.

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