NeuroMorse: a temporally structured dataset for neuromorphic computing
Neuromorphic engineering aims to advance computing by mimicking the brain’s efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromor...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Neuromorphic Computing and Engineering |
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| Online Access: | https://doi.org/10.1088/2634-4386/add36c |
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| author | Ben Walters Yeshwanth Bethi Taylor Kergan Binh Nguyen Amirali Amirsoleimani Jason K Eshraghian Saeed Afshar Mostafa Rahimi Azghadi |
| author_facet | Ben Walters Yeshwanth Bethi Taylor Kergan Binh Nguyen Amirali Amirsoleimani Jason K Eshraghian Saeed Afshar Mostafa Rahimi Azghadi |
| author_sort | Ben Walters |
| collection | DOAJ |
| description | Neuromorphic engineering aims to advance computing by mimicking the brain’s efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromorphic and spiking algorithms primarily focus on spatial features, neglecting the temporal dynamics that are inherent to most sequence-based tasks. This gap may lead to evaluations that fail to fully capture the unique strengths and characteristics of neuromorphic systems. In this paper, we present NeuroMorse, a temporally structured dataset designed for benchmarking spiking learning algorithms. NeuroMorse converts the top 50 words in the English language into temporal Morse code spike sequences. Despite using only two input spike channels for Morse dots and dashes, complex information is encoded through temporal patterns in the data. The proposed benchmark contains feature hierarchy at multiple temporal scales that test the capacity of spiking algorithms to decompose input patterns into spatial and temporal hierarchies. We demonstrate that our training set is challenging to categorise using a linear classifier and that identifying keywords in the test set is difficult using conventional methods. The NeuroMorse dataset is available at https://doi.org/10.5281/zenodo.12702379 , with our accompanying code at https://github.com/jc427648/NeuroMorse . |
| format | Article |
| id | doaj-art-e06e0f165ece47cd94029000e7767e17 |
| institution | Kabale University |
| issn | 2634-4386 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-e06e0f165ece47cd94029000e7767e172025-08-20T03:52:57ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862025-01-015202700110.1088/2634-4386/add36cNeuroMorse: a temporally structured dataset for neuromorphic computingBen Walters0https://orcid.org/0000-0001-5464-8468Yeshwanth Bethi1https://orcid.org/0000-0002-0713-0903Taylor Kergan2https://orcid.org/0009-0004-6277-8018Binh Nguyen3Amirali Amirsoleimani4Jason K Eshraghian5https://orcid.org/0000-0002-5832-4054Saeed Afshar6Mostafa Rahimi Azghadi7https://orcid.org/0000-0001-7975-3985College of Science and Engineering, James Cook University , Townsville, AustraliaInternational Centre for Neuromorphic Systems, Western Sydney University , Sydney, AustraliaDepartment of Electrical and Computer Engineering, University of California , Santa Cruz, The United States of AmericaDepartment of Electrical and Computer Engineering, University of California , Santa Cruz, The United States of AmericaDepartment of Electrical Engineering and Computer Science, York University , Toronto, CanadaDepartment of Electrical and Computer Engineering, University of California , Santa Cruz, The United States of AmericaInternational Centre for Neuromorphic Systems, Western Sydney University , Sydney, AustraliaCollege of Science and Engineering, James Cook University , Townsville, AustraliaNeuromorphic engineering aims to advance computing by mimicking the brain’s efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromorphic and spiking algorithms primarily focus on spatial features, neglecting the temporal dynamics that are inherent to most sequence-based tasks. This gap may lead to evaluations that fail to fully capture the unique strengths and characteristics of neuromorphic systems. In this paper, we present NeuroMorse, a temporally structured dataset designed for benchmarking spiking learning algorithms. NeuroMorse converts the top 50 words in the English language into temporal Morse code spike sequences. Despite using only two input spike channels for Morse dots and dashes, complex information is encoded through temporal patterns in the data. The proposed benchmark contains feature hierarchy at multiple temporal scales that test the capacity of spiking algorithms to decompose input patterns into spatial and temporal hierarchies. We demonstrate that our training set is challenging to categorise using a linear classifier and that identifying keywords in the test set is difficult using conventional methods. The NeuroMorse dataset is available at https://doi.org/10.5281/zenodo.12702379 , with our accompanying code at https://github.com/jc427648/NeuroMorse .https://doi.org/10.1088/2634-4386/add36cneuromorphic computingbenchmarksspiking neural networks |
| spellingShingle | Ben Walters Yeshwanth Bethi Taylor Kergan Binh Nguyen Amirali Amirsoleimani Jason K Eshraghian Saeed Afshar Mostafa Rahimi Azghadi NeuroMorse: a temporally structured dataset for neuromorphic computing Neuromorphic Computing and Engineering neuromorphic computing benchmarks spiking neural networks |
| title | NeuroMorse: a temporally structured dataset for neuromorphic computing |
| title_full | NeuroMorse: a temporally structured dataset for neuromorphic computing |
| title_fullStr | NeuroMorse: a temporally structured dataset for neuromorphic computing |
| title_full_unstemmed | NeuroMorse: a temporally structured dataset for neuromorphic computing |
| title_short | NeuroMorse: a temporally structured dataset for neuromorphic computing |
| title_sort | neuromorse a temporally structured dataset for neuromorphic computing |
| topic | neuromorphic computing benchmarks spiking neural networks |
| url | https://doi.org/10.1088/2634-4386/add36c |
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