Event-Driven Learning for Spiking Neural Networks Article Swipe
Wenjie Wei
,
Malu Zhang
,
Jilin Zhang
,
Ammar Belatreche
,
Jibin Wu
,
Zijing Xu
,
Xuerui Qiu
,
Hong Chen
,
Yang Yang
,
Haizhou Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.00270
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.00270
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on neuromorphic hardware. However, it remains an open challenge how to effectively benefit from the sparse event-driven property of SNNs to minimize backpropagation learning costs. In this paper, we conduct a comprehensive examination of the existing event-driven learning algorithms, reveal their limitations, and propose novel solutions to overcome them. Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms. These proposed algorithms leverage precise neuronal spike timing and membrane potential, respectively, for effective learning. The two methods are extensively evaluated on static and neuromorphic datasets to confirm their superior performance. They outperform existing event-driven counterparts by up to 2.51% for STD-ED and 6.79% for MPD-ED on the CIFAR-100 dataset. In addition, we theoretically and experimentally validate the energy efficiency of our methods on neuromorphic hardware. On-chip learning experiments achieved a remarkable 30-fold reduction in energy consumption over time-step-based surrogate gradient methods. The demonstrated efficiency and efficacy of the proposed event-driven learning methods emphasize their potential to significantly advance the fields of neuromorphic computing, offering promising avenues for energy-efficiency applications.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.00270
- https://arxiv.org/pdf/2403.00270
- OA Status
- green
- Cited By
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392426191
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392426191Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2403.00270Digital Object Identifier
- Title
-
Event-Driven Learning for Spiking Neural NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-01Full publication date if available
- Authors
-
Wenjie Wei, Malu Zhang, Jilin Zhang, Ammar Belatreche, Jibin Wu, Zijing Xu, Xuerui Qiu, Hong Chen, Yang Yang, Haizhou LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.00270Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2403.00270Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2403.00270Direct OA link when available
- Concepts
-
Spiking neural network, Computer science, Event (particle physics), Artificial neural network, Artificial intelligence, Machine learning, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 7Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4392426191 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2403.00270 |
| ids.doi | https://doi.org/10.48550/arxiv.2403.00270 |
| ids.openalex | https://openalex.org/W4392426191 |
| fwci | |
| type | preprint |
| title | Event-Driven Learning for Spiking Neural Networks |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10502 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9990000128746033 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Advanced Memory and Neural Computing |
| topics[1].id | https://openalex.org/T10320 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9907000064849854 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Neural Networks and Applications |
| topics[2].id | https://openalex.org/T12808 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9648000001907349 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Ferroelectric and Negative Capacitance Devices |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C11731999 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6788182258605957 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q9067355 |
| concepts[0].display_name | Spiking neural network |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6755951046943665 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2779662365 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5946342349052429 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q5416694 |
| concepts[2].display_name | Event (particle physics) |
| concepts[3].id | https://openalex.org/C50644808 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5484758019447327 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[3].display_name | Artificial neural network |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4747329652309418 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3257184624671936 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C121332964 |
| concepts[6].level | 0 |
| concepts[6].score | 0.0 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[6].display_name | Physics |
| concepts[7].id | https://openalex.org/C62520636 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[7].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/spiking-neural-network |
| keywords[0].score | 0.6788182258605957 |
| keywords[0].display_name | Spiking neural network |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6755951046943665 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/event |
| keywords[2].score | 0.5946342349052429 |
| keywords[2].display_name | Event (particle physics) |
| keywords[3].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[3].score | 0.5484758019447327 |
| keywords[3].display_name | Artificial neural network |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.4747329652309418 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.3257184624671936 |
| keywords[5].display_name | Machine learning |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2403.00270 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2403.00270 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2403.00270 |
| locations[1].id | doi:10.48550/arxiv.2403.00270 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2403.00270 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5001199797 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4105-9693 |
| authorships[0].author.display_name | Wenjie Wei |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wei, Wenjie |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5031886937 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2345-0974 |
| authorships[1].author.display_name | Malu Zhang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhang, Malu |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100624323 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0241-0727 |
| authorships[2].author.display_name | Jilin Zhang |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zhang, Jilin |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5042364148 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-1927-9366 |
| authorships[3].author.display_name | Ammar Belatreche |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Belatreche, Ammar |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5018341980 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-0135-4188 |
| authorships[4].author.display_name | Jibin Wu |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Wu, Jibin |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5102293669 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Zijing Xu |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Xu, Zijing |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5023273273 |
| authorships[6].author.orcid | https://orcid.org/0009-0000-8725-1619 |
| authorships[6].author.display_name | Xuerui Qiu |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Qiu, Xuerui |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5100420423 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-2035-6851 |
| authorships[7].author.display_name | Hong Chen |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Chen, Hong |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5100397725 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-0608-9408 |
| authorships[8].author.display_name | Yang Yang |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Yang, Yang |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5032690182 |
| authorships[9].author.orcid | https://orcid.org/0000-0001-9158-9401 |
| authorships[9].author.display_name | Haizhou Li |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Li, Haizhou |
| authorships[9].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2403.00270 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Event-Driven Learning for Spiking Neural Networks |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10502 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9990000128746033 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Advanced Memory and Neural Computing |
| related_works | https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W3046775127, https://openalex.org/W3107602296, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W4364306694, https://openalex.org/W4312192474, https://openalex.org/W4283697347, https://openalex.org/W4210805261 |
| cited_by_count | 13 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2403.00270 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2403.00270 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2403.00270 |
| primary_location.id | pmh:oai:arXiv.org:2403.00270 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2403.00270 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2403.00270 |
| publication_date | 2024-03-01 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 53, 159 |
| abstract_inverted_index.In | 48, 139 |
| abstract_inverted_index.an | 29 |
| abstract_inverted_index.by | 125 |
| abstract_inverted_index.in | 8, 163 |
| abstract_inverted_index.it | 27 |
| abstract_inverted_index.of | 11, 41, 56, 149, 176, 190 |
| abstract_inverted_index.on | 23, 110, 135, 152 |
| abstract_inverted_index.to | 15, 33, 43, 69, 115, 127, 185 |
| abstract_inverted_index.up | 126 |
| abstract_inverted_index.we | 51, 73, 141 |
| abstract_inverted_index.The | 104, 171 |
| abstract_inverted_index.and | 65, 84, 97, 112, 131, 143, 174 |
| abstract_inverted_index.are | 107 |
| abstract_inverted_index.for | 101, 129, 133, 196 |
| abstract_inverted_index.how | 32 |
| abstract_inverted_index.low | 17 |
| abstract_inverted_index.our | 150 |
| abstract_inverted_index.the | 9, 37, 57, 80, 136, 146, 177, 188 |
| abstract_inverted_index.two | 75, 105 |
| abstract_inverted_index.SNNs | 42 |
| abstract_inverted_index.They | 120 |
| abstract_inverted_index.from | 36 |
| abstract_inverted_index.have | 5 |
| abstract_inverted_index.open | 30 |
| abstract_inverted_index.over | 166 |
| abstract_inverted_index.this | 49 |
| abstract_inverted_index.2.51% | 128 |
| abstract_inverted_index.6.79% | 132 |
| abstract_inverted_index.These | 89 |
| abstract_inverted_index.field | 10 |
| abstract_inverted_index.novel | 67, 76 |
| abstract_inverted_index.owing | 14 |
| abstract_inverted_index.spike | 95 |
| abstract_inverted_index.their | 16, 63, 117, 183 |
| abstract_inverted_index.them. | 71 |
| abstract_inverted_index.(SNNs) | 4 |
| abstract_inverted_index.MPD-ED | 134 |
| abstract_inverted_index.STD-ED | 130 |
| abstract_inverted_index.costs. | 47 |
| abstract_inverted_index.during | 20 |
| abstract_inverted_index.energy | 18, 147, 164 |
| abstract_inverted_index.fields | 189 |
| abstract_inverted_index.gained | 6 |
| abstract_inverted_index.neural | 2 |
| abstract_inverted_index.paper, | 50 |
| abstract_inverted_index.reveal | 62 |
| abstract_inverted_index.sparse | 38 |
| abstract_inverted_index.static | 111 |
| abstract_inverted_index.timing | 96 |
| abstract_inverted_index.30-fold | 161 |
| abstract_inverted_index.On-chip | 155 |
| abstract_inverted_index.advance | 187 |
| abstract_inverted_index.avenues | 195 |
| abstract_inverted_index.benefit | 35 |
| abstract_inverted_index.conduct | 52 |
| abstract_inverted_index.confirm | 116 |
| abstract_inverted_index.methods | 106, 151, 181 |
| abstract_inverted_index.precise | 93 |
| abstract_inverted_index.propose | 66 |
| abstract_inverted_index.remains | 28 |
| abstract_inverted_index.spiking | 1 |
| abstract_inverted_index.(MPD-ED) | 87 |
| abstract_inverted_index.(STD-ED) | 83 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.achieved | 158 |
| abstract_inverted_index.dataset. | 138 |
| abstract_inverted_index.datasets | 114 |
| abstract_inverted_index.efficacy | 175 |
| abstract_inverted_index.existing | 58, 122 |
| abstract_inverted_index.gradient | 169 |
| abstract_inverted_index.learning | 46, 60, 78, 156, 180 |
| abstract_inverted_index.leverage | 92 |
| abstract_inverted_index.membrane | 98 |
| abstract_inverted_index.methods. | 170 |
| abstract_inverted_index.methods: | 79 |
| abstract_inverted_index.minimize | 44 |
| abstract_inverted_index.networks | 3 |
| abstract_inverted_index.neuronal | 94 |
| abstract_inverted_index.offering | 193 |
| abstract_inverted_index.overcome | 70 |
| abstract_inverted_index.property | 40 |
| abstract_inverted_index.proposed | 90, 178 |
| abstract_inverted_index.superior | 118 |
| abstract_inverted_index.validate | 145 |
| abstract_inverted_index.CIFAR-100 | 137 |
| abstract_inverted_index.addition, | 140 |
| abstract_inverted_index.challenge | 31 |
| abstract_inverted_index.computing | 13 |
| abstract_inverted_index.effective | 102 |
| abstract_inverted_index.emphasize | 182 |
| abstract_inverted_index.evaluated | 109 |
| abstract_inverted_index.hardware. | 25, 154 |
| abstract_inverted_index.inference | 22 |
| abstract_inverted_index.introduce | 74 |
| abstract_inverted_index.learning. | 103 |
| abstract_inverted_index.potential | 184 |
| abstract_inverted_index.promising | 194 |
| abstract_inverted_index.reduction | 162 |
| abstract_inverted_index.solutions | 68 |
| abstract_inverted_index.surrogate | 168 |
| abstract_inverted_index.algorithms | 91 |
| abstract_inverted_index.computing, | 192 |
| abstract_inverted_index.efficiency | 148, 173 |
| abstract_inverted_index.outperform | 121 |
| abstract_inverted_index.potential, | 99 |
| abstract_inverted_index.prominence | 7 |
| abstract_inverted_index.remarkable | 160 |
| abstract_inverted_index.algorithms, | 61 |
| abstract_inverted_index.algorithms. | 88 |
| abstract_inverted_index.consumption | 19, 165 |
| abstract_inverted_index.effectively | 34 |
| abstract_inverted_index.examination | 55 |
| abstract_inverted_index.experiments | 157 |
| abstract_inverted_index.extensively | 108 |
| abstract_inverted_index.feedforward | 21 |
| abstract_inverted_index.counterparts | 124 |
| abstract_inverted_index.demonstrated | 172 |
| abstract_inverted_index.event-driven | 39, 59, 77, 82, 86, 123, 179 |
| abstract_inverted_index.limitations, | 64 |
| abstract_inverted_index.neuromorphic | 12, 24, 113, 153, 191 |
| abstract_inverted_index.performance. | 119 |
| abstract_inverted_index.Specifically, | 72 |
| abstract_inverted_index.applications. | 198 |
| abstract_inverted_index.comprehensive | 54 |
| abstract_inverted_index.respectively, | 100 |
| abstract_inverted_index.significantly | 186 |
| abstract_inverted_index.theoretically | 142 |
| abstract_inverted_index.Brain-inspired | 0 |
| abstract_inverted_index.experimentally | 144 |
| abstract_inverted_index.backpropagation | 45 |
| abstract_inverted_index.time-step-based | 167 |
| abstract_inverted_index.energy-efficiency | 197 |
| abstract_inverted_index.spike-timing-dependent | 81 |
| abstract_inverted_index.membrane-potential-dependent | 85 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |