Recognizing intertwined patterns using a network of spiking pattern recognition platforms
Abstract Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making and cognition in today's systems. H...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-23320-8 |
| _version_ | 1811319323521712128 |
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| author | Masoud Amiri Amir Homayoun Jafari Bahador Makkiabadi Soheila Nazari |
| author_facet | Masoud Amiri Amir Homayoun Jafari Bahador Makkiabadi Soheila Nazari |
| author_sort | Masoud Amiri |
| collection | DOAJ |
| description | Abstract Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making and cognition in today's systems. Here, the concentration is on improvement the cognitive potential of artificial intelligence network with a bio-inspired structure. In this regard, four spiking pattern recognition platforms for recognizing digits and letters of EMNIST, patterns of YALE, and ORL datasets are proposed. All networks are developed based on a similar structure in the input image coding, model of neurons (pyramidal neurons and interneurons) and synapses (excitatory AMPA and inhibitory GABA currents), and learning procedure. Networks 1–4 are trained on Digits, Letters, faces of YALE and ORL, respectively, with the proposed un-supervised, spatial–temporal, and sparse spike-based learning mechanism based on the biological observation of the brain learning. When the networks have reached the highest recognition accuracy in the relevant patterns, the main goal of the article, which is to achieve high-performance pattern recognition system with higher cognitive ability, is followed. The pattern recognition network that is able to detect the combination of multiple patterns which called intertwined patterns has not been discussed yet. Therefore, by integrating four trained spiking pattern recognition platforms in one system configuration, we are able to recognize intertwined patterns. These results are presented for the first time and could be the pioneer of a new generation of pattern recognition networks with a significant ability in smart machines. |
| first_indexed | 2024-04-13T12:39:39Z |
| format | Article |
| id | doaj.art-0586179b42e44140828ad09a21f45c6a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T12:39:39Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0586179b42e44140828ad09a21f45c6a2022-12-22T02:46:32ZengNature PortfolioScientific Reports2045-23222022-11-0112111710.1038/s41598-022-23320-8Recognizing intertwined patterns using a network of spiking pattern recognition platformsMasoud Amiri0Amir Homayoun Jafari1Bahador Makkiabadi2Soheila Nazari3Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science (TUMS)Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science (TUMS)Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science (TUMS)Faculty of Electrical Engineering, Shahid Beheshti UniversityAbstract Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making and cognition in today's systems. Here, the concentration is on improvement the cognitive potential of artificial intelligence network with a bio-inspired structure. In this regard, four spiking pattern recognition platforms for recognizing digits and letters of EMNIST, patterns of YALE, and ORL datasets are proposed. All networks are developed based on a similar structure in the input image coding, model of neurons (pyramidal neurons and interneurons) and synapses (excitatory AMPA and inhibitory GABA currents), and learning procedure. Networks 1–4 are trained on Digits, Letters, faces of YALE and ORL, respectively, with the proposed un-supervised, spatial–temporal, and sparse spike-based learning mechanism based on the biological observation of the brain learning. When the networks have reached the highest recognition accuracy in the relevant patterns, the main goal of the article, which is to achieve high-performance pattern recognition system with higher cognitive ability, is followed. The pattern recognition network that is able to detect the combination of multiple patterns which called intertwined patterns has not been discussed yet. Therefore, by integrating four trained spiking pattern recognition platforms in one system configuration, we are able to recognize intertwined patterns. These results are presented for the first time and could be the pioneer of a new generation of pattern recognition networks with a significant ability in smart machines.https://doi.org/10.1038/s41598-022-23320-8 |
| spellingShingle | Masoud Amiri Amir Homayoun Jafari Bahador Makkiabadi Soheila Nazari Recognizing intertwined patterns using a network of spiking pattern recognition platforms Scientific Reports |
| title | Recognizing intertwined patterns using a network of spiking pattern recognition platforms |
| title_full | Recognizing intertwined patterns using a network of spiking pattern recognition platforms |
| title_fullStr | Recognizing intertwined patterns using a network of spiking pattern recognition platforms |
| title_full_unstemmed | Recognizing intertwined patterns using a network of spiking pattern recognition platforms |
| title_short | Recognizing intertwined patterns using a network of spiking pattern recognition platforms |
| title_sort | recognizing intertwined patterns using a network of spiking pattern recognition platforms |
| url | https://doi.org/10.1038/s41598-022-23320-8 |
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