Time-Frequency Analysis for Feature Extraction Using Spiking Neural Network Article Swipe
Moshe Bensimon
,
Yakir Hadad
,
Yehuda Ben‐Shimol
,
Shlomo Greenberg
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.24174924.v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.36227/techrxiv.24174924.v1
Time-frequency analysis plays a crucial role in various fields, including signal processing and feature extraction. In this article, we propose an alternative and innovative method for time-frequency analysis using a biologically inspired spiking neural network (SNN), encompassing both specific spike-continuous-time-neuron (SCTN) based neural architecture and an adaptive learning rule. We aim to efficiently detect frequencies embedded in a given signal for the purpose of feature extraction. To achieve this, we suggest using an SN-based network functioning as a resonator for the detection of specific frequencies. We developed a modified supervised Spike-Timing-Dependent Plasticity (STDP) learning rule to effectively adjust the network parameters. Unlike traditional methods for time-frequency analysis, our approach obviates the need for segmenting the signal into several frames, resulting in a streamlined and more effective frequency analysis process. Simulation results demonstrate the efficiency of the proposed method, showcasing its ability to detect frequencies and generate a Spikegram akin to the Fast Fourier Transform (FFT) based spectrogram. The proposed approach is applied to analyzing EEG signals demonstrating an accurate correlation to the equivalent FFT transform.
Related Topics
Concepts
Spectrogram
Computer science
Fast Fourier transform
Time–frequency analysis
Spike (software development)
Pattern recognition (psychology)
Feature extraction
Artificial neural network
Artificial intelligence
SIGNAL (programming language)
Feature (linguistics)
Short-time Fourier transform
Speech recognition
Signal processing
Fourier transform
Algorithm
Mathematics
Fourier analysis
Filter (signal processing)
Digital signal processing
Computer vision
Mathematical analysis
Software engineering
Philosophy
Linguistics
Programming language
Computer hardware
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.36227/techrxiv.24174924.v1
- https://www.techrxiv.org/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924/1/files/42417819.pdf
- OA Status
- gold
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387104219
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387104219Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.36227/techrxiv.24174924.v1Digital Object Identifier
- Title
-
Time-Frequency Analysis for Feature Extraction Using Spiking Neural NetworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-27Full publication date if available
- Authors
-
Moshe Bensimon, Yakir Hadad, Yehuda Ben‐Shimol, Shlomo GreenbergList of authors in order
- Landing page
-
https://doi.org/10.36227/techrxiv.24174924.v1Publisher landing page
- PDF URL
-
https://www.techrxiv.org/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924/1/files/42417819.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.techrxiv.org/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924/1/files/42417819.pdfDirect OA link when available
- Concepts
-
Spectrogram, Computer science, Fast Fourier transform, Time–frequency analysis, Spike (software development), Pattern recognition (psychology), Feature extraction, Artificial neural network, Artificial intelligence, SIGNAL (programming language), Feature (linguistics), Short-time Fourier transform, Speech recognition, Signal processing, Fourier transform, Algorithm, Mathematics, Fourier analysis, Filter (signal processing), Digital signal processing, Computer vision, Mathematical analysis, Software engineering, Philosophy, Linguistics, Programming language, Computer hardwareTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387104219 |
|---|---|
| doi | https://doi.org/10.36227/techrxiv.24174924.v1 |
| ids.doi | https://doi.org/10.36227/techrxiv.24174924.v1 |
| ids.openalex | https://openalex.org/W4387104219 |
| fwci | 0.0 |
| type | preprint |
| title | Time-Frequency Analysis for Feature Extraction Using Spiking Neural Network |
| 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 | 0.9952999949455261 |
| 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/T10581 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.9900000095367432 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2805 |
| topics[1].subfield.display_name | Cognitive Neuroscience |
| topics[1].display_name | Neural dynamics and brain function |
| topics[2].id | https://openalex.org/T10429 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9898999929428101 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | EEG and Brain-Computer Interfaces |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C45273575 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7986441850662231 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q578970 |
| concepts[0].display_name | Spectrogram |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6579832434654236 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C75172450 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6479498147964478 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q623950 |
| concepts[2].display_name | Fast Fourier transform |
| concepts[3].id | https://openalex.org/C142433447 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6304942965507507 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7806653 |
| concepts[3].display_name | Time–frequency analysis |
| concepts[4].id | https://openalex.org/C2781390188 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6099811792373657 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q25203449 |
| concepts[4].display_name | Spike (software development) |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5957631468772888 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C52622490 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5566551685333252 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1026626 |
| concepts[6].display_name | Feature extraction |
| concepts[7].id | https://openalex.org/C50644808 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5521624088287354 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[7].display_name | Artificial neural network |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.5459680557250977 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C2779843651 |
| concepts[9].level | 2 |
| concepts[9].score | 0.5085967779159546 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7390335 |
| concepts[9].display_name | SIGNAL (programming language) |
| concepts[10].id | https://openalex.org/C2776401178 |
| concepts[10].level | 2 |
| concepts[10].score | 0.46601441502571106 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[10].display_name | Feature (linguistics) |
| concepts[11].id | https://openalex.org/C166386157 |
| concepts[11].level | 4 |
| concepts[11].score | 0.4381639063358307 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1477735 |
| concepts[11].display_name | Short-time Fourier transform |
| concepts[12].id | https://openalex.org/C28490314 |
| concepts[12].level | 1 |
| concepts[12].score | 0.42336398363113403 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q189436 |
| concepts[12].display_name | Speech recognition |
| concepts[13].id | https://openalex.org/C104267543 |
| concepts[13].level | 3 |
| concepts[13].score | 0.41487014293670654 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q208163 |
| concepts[13].display_name | Signal processing |
| concepts[14].id | https://openalex.org/C102519508 |
| concepts[14].level | 2 |
| concepts[14].score | 0.3411466181278229 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q6520159 |
| concepts[14].display_name | Fourier transform |
| concepts[15].id | https://openalex.org/C11413529 |
| concepts[15].level | 1 |
| concepts[15].score | 0.23316484689712524 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[15].display_name | Algorithm |
| concepts[16].id | https://openalex.org/C33923547 |
| concepts[16].level | 0 |
| concepts[16].score | 0.20622476935386658 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[16].display_name | Mathematics |
| concepts[17].id | https://openalex.org/C203024314 |
| concepts[17].level | 3 |
| concepts[17].score | 0.189207524061203 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q1365258 |
| concepts[17].display_name | Fourier analysis |
| concepts[18].id | https://openalex.org/C106131492 |
| concepts[18].level | 2 |
| concepts[18].score | 0.14106065034866333 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[18].display_name | Filter (signal processing) |
| concepts[19].id | https://openalex.org/C84462506 |
| concepts[19].level | 2 |
| concepts[19].score | 0.09479230642318726 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q173142 |
| concepts[19].display_name | Digital signal processing |
| concepts[20].id | https://openalex.org/C31972630 |
| concepts[20].level | 1 |
| concepts[20].score | 0.07860299944877625 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[20].display_name | Computer vision |
| concepts[21].id | https://openalex.org/C134306372 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[21].display_name | Mathematical analysis |
| concepts[22].id | https://openalex.org/C115903868 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q80993 |
| concepts[22].display_name | Software engineering |
| concepts[23].id | https://openalex.org/C138885662 |
| concepts[23].level | 0 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[23].display_name | Philosophy |
| concepts[24].id | https://openalex.org/C41895202 |
| concepts[24].level | 1 |
| concepts[24].score | 0.0 |
| concepts[24].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[24].display_name | Linguistics |
| concepts[25].id | https://openalex.org/C199360897 |
| concepts[25].level | 1 |
| concepts[25].score | 0.0 |
| concepts[25].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[25].display_name | Programming language |
| concepts[26].id | https://openalex.org/C9390403 |
| concepts[26].level | 1 |
| concepts[26].score | 0.0 |
| concepts[26].wikidata | https://www.wikidata.org/wiki/Q3966 |
| concepts[26].display_name | Computer hardware |
| keywords[0].id | https://openalex.org/keywords/spectrogram |
| keywords[0].score | 0.7986441850662231 |
| keywords[0].display_name | Spectrogram |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6579832434654236 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/fast-fourier-transform |
| keywords[2].score | 0.6479498147964478 |
| keywords[2].display_name | Fast Fourier transform |
| keywords[3].id | https://openalex.org/keywords/time–frequency-analysis |
| keywords[3].score | 0.6304942965507507 |
| keywords[3].display_name | Time–frequency analysis |
| keywords[4].id | https://openalex.org/keywords/spike |
| keywords[4].score | 0.6099811792373657 |
| keywords[4].display_name | Spike (software development) |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.5957631468772888 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/feature-extraction |
| keywords[6].score | 0.5566551685333252 |
| keywords[6].display_name | Feature extraction |
| keywords[7].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[7].score | 0.5521624088287354 |
| keywords[7].display_name | Artificial neural network |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.5459680557250977 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/signal |
| keywords[9].score | 0.5085967779159546 |
| keywords[9].display_name | SIGNAL (programming language) |
| keywords[10].id | https://openalex.org/keywords/feature |
| keywords[10].score | 0.46601441502571106 |
| keywords[10].display_name | Feature (linguistics) |
| keywords[11].id | https://openalex.org/keywords/short-time-fourier-transform |
| keywords[11].score | 0.4381639063358307 |
| keywords[11].display_name | Short-time Fourier transform |
| keywords[12].id | https://openalex.org/keywords/speech-recognition |
| keywords[12].score | 0.42336398363113403 |
| keywords[12].display_name | Speech recognition |
| keywords[13].id | https://openalex.org/keywords/signal-processing |
| keywords[13].score | 0.41487014293670654 |
| keywords[13].display_name | Signal processing |
| keywords[14].id | https://openalex.org/keywords/fourier-transform |
| keywords[14].score | 0.3411466181278229 |
| keywords[14].display_name | Fourier transform |
| keywords[15].id | https://openalex.org/keywords/algorithm |
| keywords[15].score | 0.23316484689712524 |
| keywords[15].display_name | Algorithm |
| keywords[16].id | https://openalex.org/keywords/mathematics |
| keywords[16].score | 0.20622476935386658 |
| keywords[16].display_name | Mathematics |
| keywords[17].id | https://openalex.org/keywords/fourier-analysis |
| keywords[17].score | 0.189207524061203 |
| keywords[17].display_name | Fourier analysis |
| keywords[18].id | https://openalex.org/keywords/filter |
| keywords[18].score | 0.14106065034866333 |
| keywords[18].display_name | Filter (signal processing) |
| keywords[19].id | https://openalex.org/keywords/digital-signal-processing |
| keywords[19].score | 0.09479230642318726 |
| keywords[19].display_name | Digital signal processing |
| keywords[20].id | https://openalex.org/keywords/computer-vision |
| keywords[20].score | 0.07860299944877625 |
| keywords[20].display_name | Computer vision |
| language | en |
| locations[0].id | doi:10.36227/techrxiv.24174924.v1 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.techrxiv.org/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924/1/files/42417819.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.36227/techrxiv.24174924.v1 |
| locations[1].id | pmh:oai:figshare.com:article/24174924 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400572 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[1].source.host_organization | https://openalex.org/I196829312 |
| locations[1].source.host_organization_name | La Trobe University |
| locations[1].source.host_organization_lineage | https://openalex.org/I196829312 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://figshare.com/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5057107210 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9291-1703 |
| authorships[0].author.display_name | Moshe Bensimon |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Moshe Bensimon |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5027159531 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4682-9415 |
| authorships[1].author.display_name | Yakir Hadad |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yakir Hadad |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5016591905 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4905-2085 |
| authorships[2].author.display_name | Yehuda Ben‐Shimol |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yehuda Ben-Shimol |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5072766500 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1385-8394 |
| authorships[3].author.display_name | Shlomo Greenberg |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Shlomo Greenberg |
| authorships[3].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.techrxiv.org/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924/1/files/42417819.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Time-Frequency Analysis for Feature Extraction Using Spiking Neural Network |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| 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 | 0.9952999949455261 |
| 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/W2120540196, https://openalex.org/W3095343173, https://openalex.org/W2288135719, https://openalex.org/W1889291648, https://openalex.org/W2533590149, https://openalex.org/W2381036744, https://openalex.org/W2323749021, https://openalex.org/W2901989338, https://openalex.org/W4381416811, https://openalex.org/W2909460703 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.36227/techrxiv.24174924.v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.techrxiv.org/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924/1/files/42417819.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.36227/techrxiv.24174924.v1 |
| primary_location.id | doi:10.36227/techrxiv.24174924.v1 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.techrxiv.org/articles/preprint/Time-Frequency_Analysis_for_Feature_Extraction_Using_Spiking_Neural_Network/24174924/1/files/42417819.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.36227/techrxiv.24174924.v1 |
| publication_date | 2023-09-27 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W4210357113, https://openalex.org/W6791592421, https://openalex.org/W2997428102, https://openalex.org/W4360979418, https://openalex.org/W4295942648, https://openalex.org/W2895594817, https://openalex.org/W3107631228, https://openalex.org/W3166644668, https://openalex.org/W2744986420, https://openalex.org/W3028490874, https://openalex.org/W2311509729, https://openalex.org/W3128999834, https://openalex.org/W1486852018, https://openalex.org/W2805087868, https://openalex.org/W2946884143, https://openalex.org/W3137731826 |
| referenced_works_count | 16 |
| abstract_inverted_index.a | 3, 29, 58, 78, 88, 123, 148 |
| abstract_inverted_index.In | 15 |
| abstract_inverted_index.To | 67 |
| abstract_inverted_index.We | 86 |
| abstract_inverted_index.an | 20, 45, 73, 169 |
| abstract_inverted_index.as | 77 |
| abstract_inverted_index.in | 6, 57, 122 |
| abstract_inverted_index.is | 162 |
| abstract_inverted_index.of | 64, 83, 136 |
| abstract_inverted_index.to | 52, 96, 143, 151, 164, 172 |
| abstract_inverted_index.we | 18, 70 |
| abstract_inverted_index.EEG | 166 |
| abstract_inverted_index.FFT | 175 |
| abstract_inverted_index.The | 159 |
| abstract_inverted_index.aim | 51 |
| abstract_inverted_index.and | 12, 22, 44, 125, 146 |
| abstract_inverted_index.for | 25, 61, 80, 106, 114 |
| abstract_inverted_index.its | 141 |
| abstract_inverted_index.our | 109 |
| abstract_inverted_index.the | 62, 81, 99, 112, 116, 134, 137, 152, 173 |
| abstract_inverted_index.Fast | 153 |
| abstract_inverted_index.akin | 150 |
| abstract_inverted_index.both | 37 |
| abstract_inverted_index.into | 118 |
| abstract_inverted_index.more | 126 |
| abstract_inverted_index.need | 113 |
| abstract_inverted_index.role | 5 |
| abstract_inverted_index.rule | 95 |
| abstract_inverted_index.this | 16 |
| abstract_inverted_index.(FFT) | 156 |
| abstract_inverted_index.based | 41, 157 |
| abstract_inverted_index.given | 59 |
| abstract_inverted_index.plays | 2 |
| abstract_inverted_index.rule. | 48 |
| abstract_inverted_index.this, | 69 |
| abstract_inverted_index.using | 28, 72 |
| abstract_inverted_index.(SCTN) | 40 |
| abstract_inverted_index.(SNN), | 35 |
| abstract_inverted_index.(STDP) | 93 |
| abstract_inverted_index.adjust | 98 |
| abstract_inverted_index.detect | 54, 144 |
| abstract_inverted_index.method | 24 |
| abstract_inverted_index.neural | 33, 42 |
| abstract_inverted_index.signal | 10, 60, 117 |
| abstract_inverted_index.Fourier | 154 |
| abstract_inverted_index.ability | 142 |
| abstract_inverted_index.achieve | 68 |
| abstract_inverted_index.applied | 163 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.feature | 13, 65 |
| abstract_inverted_index.fields, | 8 |
| abstract_inverted_index.frames, | 120 |
| abstract_inverted_index.method, | 139 |
| abstract_inverted_index.methods | 105 |
| abstract_inverted_index.network | 34, 75, 100 |
| abstract_inverted_index.propose | 19 |
| abstract_inverted_index.purpose | 63 |
| abstract_inverted_index.results | 132 |
| abstract_inverted_index.several | 119 |
| abstract_inverted_index.signals | 167 |
| abstract_inverted_index.spiking | 32 |
| abstract_inverted_index.suggest | 71 |
| abstract_inverted_index.various | 7 |
| abstract_inverted_index.SN-based | 74 |
| abstract_inverted_index.accurate | 170 |
| abstract_inverted_index.adaptive | 46 |
| abstract_inverted_index.analysis | 1, 27, 129 |
| abstract_inverted_index.approach | 110, 161 |
| abstract_inverted_index.article, | 17 |
| abstract_inverted_index.embedded | 56 |
| abstract_inverted_index.generate | 147 |
| abstract_inverted_index.inspired | 31 |
| abstract_inverted_index.learning | 47, 94 |
| abstract_inverted_index.modified | 89 |
| abstract_inverted_index.obviates | 111 |
| abstract_inverted_index.proposed | 138, 160 |
| abstract_inverted_index.specific | 38, 84 |
| abstract_inverted_index.Spikegram | 149 |
| abstract_inverted_index.Transform | 155 |
| abstract_inverted_index.analysis, | 108 |
| abstract_inverted_index.analyzing | 165 |
| abstract_inverted_index.detection | 82 |
| abstract_inverted_index.developed | 87 |
| abstract_inverted_index.effective | 127 |
| abstract_inverted_index.frequency | 128 |
| abstract_inverted_index.including | 9 |
| abstract_inverted_index.resonator | 79 |
| abstract_inverted_index.resulting | 121 |
| abstract_inverted_index.</p> | 49, 102 |
| abstract_inverted_index.Plasticity | 92 |
| abstract_inverted_index.efficiency | 135 |
| abstract_inverted_index.equivalent | 174 |
| abstract_inverted_index.innovative | 23 |
| abstract_inverted_index.processing | 11 |
| abstract_inverted_index.segmenting | 115 |
| abstract_inverted_index.showcasing | 140 |
| abstract_inverted_index.supervised | 90 |
| abstract_inverted_index.<p>We | 50 |
| abstract_inverted_index.alternative | 21 |
| abstract_inverted_index.correlation | 171 |
| abstract_inverted_index.demonstrate | 133 |
| abstract_inverted_index.effectively | 97 |
| abstract_inverted_index.efficiently | 53 |
| abstract_inverted_index.extraction. | 14, 66 |
| abstract_inverted_index.frequencies | 55, 145 |
| abstract_inverted_index.functioning | 76 |
| abstract_inverted_index.parameters. | 101 |
| abstract_inverted_index.streamlined | 124 |
| abstract_inverted_index.traditional | 104 |
| abstract_inverted_index.architecture | 43 |
| abstract_inverted_index.biologically | 30 |
| abstract_inverted_index.encompassing | 36 |
| abstract_inverted_index.frequencies. | 85 |
| abstract_inverted_index.spectrogram. | 158 |
| abstract_inverted_index.demonstrating | 168 |
| abstract_inverted_index.time-frequency | 26, 107 |
| abstract_inverted_index.<p>Unlike | 103 |
| abstract_inverted_index.process.</p> | 130 |
| abstract_inverted_index.<p>Simulation | 131 |
| abstract_inverted_index.transform.</p> | 176 |
| abstract_inverted_index.Spike-Timing-Dependent | 91 |
| abstract_inverted_index.<p>Time-frequency | 0 |
| abstract_inverted_index.spike-continuous-time-neuron | 39 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.13517769 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |