A multi-subunit spatiotemporal model of local edge detector cells in the cat retina

2008 ◽  
Vol 72 (1-3) ◽  
pp. 302-312 ◽  
Author(s):  
Wang-Qiang Niu ◽  
Jing-Qi Yuan
Keyword(s):  
Edge Detector ◽  
Spatiotemporal Model ◽  
Cat Retina ◽  
Local Edge

scholarly journals Retinal Synaptic Pathways Underlying the Response of the Rabbit Local Edge Detector

2010 ◽  
Vol 103 (5) ◽  
pp. 2757-2769 ◽  
Author(s):  
Thomas L. Russell ◽  
Frank S. Werblin
Keyword(s):  
Feedback Inhibition ◽  
Receptive Fields ◽  
Cell Voltage ◽  
Retinal Ganglion ◽  
Edge Detector ◽  
Temporal Properties ◽  
Field Sensitivity ◽  
Glycinergic Inhibition ◽  
Local Edge ◽  
Feedforward Inhibition

We studied the circuitry that underlies the behavior of the local edge detector (LED) retinal ganglion cell in rabbit by measuring the spatial and temporal properties of excitatory and inhibitory currents under whole cell voltage clamp. Previous work showed that LED excitation is suppressed by activity in the surround. However, the contributions of outer and inner retina to this characteristic and the neurotransmitters used are currently unknown. Blockage of retinal inhibitory pathways (GABAA, GABAC, and glycine) eliminated edge selectivity. Inverting gratings in the surround with 50-μm stripe sizes did not stimulate horizontal cells, but suppressed on and off excitation by roughly 60%, indicating inhibition of bipolar terminals (feedback inhibition). On pharmacologic blockage, we showed that feedback inhibition used both GABAA and GABAC receptors, but not glycine. Glycinergic inhibition suppressed GABAergic feedback inhibition in the center, enabling larger excitatory currents in response to luminance changes. Excitation, feedback inhibition, and direct (feedforward) inhibition responded to luminance-neutral flipping gratings of 20- to 50-μm widths, showing they are driven by independent subunits within their receptive fields, which confers sensitivity to borders between areas of texture and nontexture. Feedforward inhibition was glycinergic, its rise time was faster than decay time, and did not function to delay spiking at the onset of a stimulus. Both the on and off phases could be triggered by luminance shifts as short in duration as 33 ms and could be triggered during scenes that already produced a high baseline level of feedforward inhibition. Our results show how LED circuitry can use subreceptive field sensitivity to detect visual edges via the interaction between excitation and feedback inhibition and also respond to rapid luminance shifts within a rapidly changing scene by producing feedforward inhibition.

Structure and function of neural networks in the retina

1988 ◽  
Vol 46 ◽  
pp. 26-27
Author(s):  
Peter Sterling
Keyword(s):  
Ganglion Cells ◽  
Three Dimensional ◽  
Structure And Function ◽  
Synaptic Connections ◽  
Visual Axis ◽  
One Dimensional ◽  
Cat Retina ◽  
Ultrathin Sections ◽  
And Function ◽  
Insight Into

The synaptic connections in cat retina that link photoreceptors to ganglion cells have been analyzed quantitatively. Our approach has been to prepare serial, ultrathin sections and photograph en montage at low magnification (˜2000X) in the electron microscope. Six series, 100-300 sections long, have been prepared over the last decade. They derive from different cats but always from the same region of retina, about one degree from the center of the visual axis. The material has been analyzed by reconstructing adjacent neurons in each array and then identifying systematically the synaptic connections between arrays. Most reconstructions were done manually by tracing the outlines of processes in successive sections onto acetate sheets aligned on a cartoonist's jig. The tracings were then digitized, stacked by computer, and printed with the hidden lines removed. The results have provided rather than the usual one-dimensional account of pathways, a three-dimensional account of circuits. From this has emerged insight into the functional architecture.

Short Hand Recognition using Canny Edge Detector

2017 ◽  
Vol 7 (5) ◽  
pp. 902-907
Author(s):  
Pramod Kumar S ◽  
◽  
Narendra T.V ◽  
Vinay N.A ◽  
◽  
...  
Keyword(s):  
Edge Detector ◽  
Canny Edge Detector ◽  
Canny Edge ◽  
Short Hand ◽  
Hand Recognition

Improved MR image denoising via low- rank approximation and Laplacian-of-Gaussian edge detector

2020 ◽  
Vol 14 (12) ◽  
pp. 2791-2798
Author(s):  
Xiaoqun Qiu ◽  
Zhen Chen ◽  
Saifullah Adnan ◽  
Hongwei He
Keyword(s):  
Image Denoising ◽  
Low Rank ◽  
Edge Detector ◽  
Low Rank Approximation ◽  
Mr Image ◽  
Rank Approximation

Application of an advanced spatiotemporal model for PM2.5 prediction in Jiangsu Province, China

Chemosphere ◽  
2020 ◽  
Vol 246 ◽  
pp. 125563 ◽  
Author(s):  
Ting Zhang ◽  
Penghui Liu ◽  
Xue Sun ◽  
Can Zhang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Jiangsu Province ◽  
Spatiotemporal Model

Forecasting tourism demand: Developing a general nesting spatiotemporal model

2021 ◽  
Vol 90 ◽  
pp. 103277
Author(s):  
Xiaoying Jiao ◽  
Jason Li Chen ◽  
Gang Li
Keyword(s):  
Tourism Demand ◽  
Spatiotemporal Model

Machine Learning Based Assembly of Fragments of Ancient Papyrus

2021 ◽  
Vol 14 (3) ◽  
pp. 1-21
Author(s):  
Roy Abitbol ◽  
Ilan Shimshoni ◽  
Jonathan Ben-Dov
Keyword(s):  
Machine Learning ◽  
Spatial Information ◽  
Real Life ◽  
Dead Sea ◽  
Machine Learning Techniques ◽  
Automated Classification ◽  
Learning Techniques ◽  
Test Batch ◽  
Validation Set ◽  
Local Edge

The task of assembling fragments in a puzzle-like manner into a composite picture plays a significant role in the field of archaeology as it supports researchers in their attempt to reconstruct historic artifacts. In this article, we propose a method for matching and assembling pairs of ancient papyrus fragments containing mostly unknown scriptures. Papyrus paper is manufactured from papyrus plants and therefore portrays typical thread patterns resulting from the plant’s stems. The proposed algorithm is founded on the hypothesis that these thread patterns contain unique local attributes such that nearby fragments show similar patterns reflecting the continuations of the threads. We posit that these patterns can be exploited using image processing and machine learning techniques to identify matching fragments. The algorithm and system which we present support the quick and automated classification of matching pairs of papyrus fragments as well as the geometric alignment of the pairs against each other. The algorithm consists of a series of steps and is based on deep-learning and machine learning methods. The first step is to deconstruct the problem of matching fragments into a smaller problem of finding thread continuation matches in local edge areas (squares) between pairs of fragments. This phase is solved using a convolutional neural network ingesting raw images of the edge areas and producing local matching scores. The result of this stage yields very high recall but low precision. Thus, we utilize these scores in order to conclude about the matching of entire fragments pairs by establishing an elaborate voting mechanism. We enhance this voting with geometric alignment techniques from which we extract additional spatial information. Eventually, we feed all the data collected from these steps into a Random Forest classifier in order to produce a higher order classifier capable of predicting whether a pair of fragments is a match. Our algorithm was trained on a batch of fragments which was excavated from the Dead Sea caves and is dated circa the 1st century BCE. The algorithm shows excellent results on a validation set which is of a similar origin and conditions. We then tried to run the algorithm against a real-life set of fragments for which we have no prior knowledge or labeling of matches. This test batch is considered extremely challenging due to its poor condition and the small size of its fragments. Evidently, numerous researchers have tried seeking matches within this batch with very little success. Our algorithm performance on this batch was sub-optimal, returning a relatively large ratio of false positives. However, the algorithm was quite useful by eliminating 98% of the possible matches thus reducing the amount of work needed for manual inspection. Indeed, experts that reviewed the results have identified some positive matches as potentially true and referred them for further investigation.

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