{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:42:12Z","timestamp":1785292932353,"version":"3.55.0"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T00:00:00Z","timestamp":1594252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1     &lt; t &lt; T    \/4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.<\/jats:p>","DOI":"10.3390\/a13070165","type":"journal-article","created":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T10:45:19Z","timestamp":1594291519000},"page":"165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons"],"prefix":"10.3390","volume":"13","author":[{"given":"Krishnamurthy V.","family":"Vemuru","sequence":"first","affiliation":[{"name":"Riverside Research, 2900 Crystal Dr., Arlington, VA 22202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3389\/fninf.2018.00079","article-title":"Recurrent Spiking Neural Network Learning Based on a Competitive Maximization of Neuronal Activity","volume":"12","author":"Demin","year":"2018","journal-title":"Front. Neuroinform."},{"key":"ref_2","unstructured":"Bellec, G., Salaj, D., Subramoney, A., Legenstein, R.A., and Maass, W. (2018, January 3\u20138). Long short-term memory and learning-to-learn in networks of spiking neurons. Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, QC, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1162\/neco.2007.19.6.1468","article-title":"Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity","volume":"19","author":"Florian","year":"2007","journal-title":"Neural Comput."},{"key":"ref_4","first-page":"115","article-title":"Learning Precise Timing with LSTM Recurrent Networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. 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