{"id":391,"date":"2026-08-07T10:30:41","date_gmt":"2026-08-07T14:30:41","guid":{"rendered":"https:\/\/carleton.ca\/share\/?p=391"},"modified":"2026-08-07T10:31:55","modified_gmt":"2026-08-07T14:31:55","slug":"new-publication-5","status":"publish","type":"post","link":"https:\/\/carleton.ca\/share\/2026\/new-publication-5\/","title":{"rendered":"New Publication"},"content":{"rendered":"<p>F. Khani, Y. Khakipoor and A. Ahmadi, <a href=\"https:\/\/ieeexplore.ieee.org\/document\/11640883\">A Cost-Efficient Chaotic Neuron for Learning Enhancement in Spiking Neural Networks<\/a>, in I<em>EEE Transactions on Emerging Topics in Computational Intelligence<\/em>, doi: 10.1109\/TETCI.2026.3716875.<\/p>\n<p><strong>Abstract:<\/strong> Spiking neural networks (SNNs) are increasingly valued for their ability to emulate the temporal dynamics of biological neurons within hardware-efficient architectures. However, commonly used neuron models, such as Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF), fall short in capturing the complex chaotic spiking-bursting behaviors found in biological neurons, which can accelerate learning and improve accuracy. To address these limitations, we introduce an optimized two-dimensional map-based neuron model (called the MR model), adapted from the Rulkov model, that efficiently achieves chaotic spiking-bursting behavior while minimizing computational demands. This model overcomes typical trade-offs between accuracy and efficiency, making it suitable for digital implementation in both hardware and software contexts. As a proof of concept, an FPGA-based implementation demonstrates the model&#8217;s resource efficiency. In image classification tasks, incorporating chaotic dynamics in only 1% of neurons resulted in up to 20% higher accuracy and a 50% reduction in convergence time compared to standard neuron models. These findings indicate that chaotic dynamics in SNNs can significantly enhance learning performance, providing an effective approach for real-time neuromorphic applications.<\/p>\n<p><strong>keywords<\/strong>: <em>Modeling; Neurons; Dynamics; Hardware; Accuracy; Learning (artificial intelligence); Costing; Costs; Limit-cycles; Spiking neural networks; Pattern recognition; map-based neuron models; dynamic systems; spiking-bursting; chaotic regime; neuromorphic; FPGA; Spiking Neural Network (SNN)<\/em>,<\/p>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>F. Khani, Y. Khakipoor and A. Ahmadi, A Cost-Efficient Chaotic Neuron for Learning Enhancement in Spiking Neural Networks, in IEEE Transactions on Emerging Topics in Computational Intelligence, doi: 10.1109\/TETCI.2026.3716875. Abstract: Spiking neural networks (SNNs) are increasingly valued for their ability to emulate the temporal dynamics of biological neurons within hardware-efficient architectures. However, commonly used neuron [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>New Publication - Specific-domain Hardware Research Laboratory<\/title>\n<meta name=\"description\" content=\"F. Khani, Y. Khakipoor and A. Ahmadi, A Cost-Efficient Chaotic Neuron for Learning Enhancement in Spiking Neural Networks, in IEEE Transactions on\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/carleton.ca\/share\/2026\/new-publication-5\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Arash Ahmadi\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/carleton.ca\/share\/2026\/new-publication-5\/\",\"url\":\"https:\/\/carleton.ca\/share\/2026\/new-publication-5\/\",\"name\":\"New Publication - Specific-domain Hardware Research Laboratory\",\"isPartOf\":{\"@id\":\"https:\/\/carleton.ca\/share\/#website\"},\"datePublished\":\"2026-08-07T14:30:41+00:00\",\"dateModified\":\"2026-08-07T14:31:55+00:00\",\"author\":{\"@id\":\"https:\/\/carleton.ca\/share\/#\/schema\/person\/6d2b896d4b1d31635f6ffb65989690e5\"},\"description\":\"F. 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Khani, Y. Khakipoor and A. Ahmadi, A Cost-Efficient Chaotic Neuron for Learning Enhancement in Spiking Neural Networks, in IEEE Transactions on","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/carleton.ca\/share\/2026\/new-publication-5\/","twitter_misc":{"Written by":"Arash Ahmadi","Est. reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/carleton.ca\/share\/2026\/new-publication-5\/","url":"https:\/\/carleton.ca\/share\/2026\/new-publication-5\/","name":"New Publication - Specific-domain Hardware Research Laboratory","isPartOf":{"@id":"https:\/\/carleton.ca\/share\/#website"},"datePublished":"2026-08-07T14:30:41+00:00","dateModified":"2026-08-07T14:31:55+00:00","author":{"@id":"https:\/\/carleton.ca\/share\/#\/schema\/person\/6d2b896d4b1d31635f6ffb65989690e5"},"description":"F. 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