Unsupervised sparse coding-based spiking neural network for real-time spike sorting Article Swipe
Alexis Melot
,
Sean U. N. Wood
,
Yannick Coffinier
,
Pierre Yger
,
Fabien Alibart
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/2634-4386/ae006b
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/2634-4386/ae006b
Spike sorting is a crucial step in decoding multichannel extracellular neural signals, enabling the identification of individual neuronal activity. A key challenge in brain–machine interfaces is achieving real-time, low-power spike sorting at the edge while keeping high neural decoding performance. This study introduces the neuromorphic sparse sorter (NSS), a compact two-layer spiking neural network optimized for efficient spike sorting. NSS leverages the locally competitive algorithm for sparse coding to extract relevant features from noisy events with reduced computational demands. NSS learns to sort detected spike waveforms in an online fashion and operates entirely unsupervised. To exploit multi-bit spike coding capabilities of neuromorphic platforms like Intel’s Loihi 2, a custom neuron model was implemented, enabling flexible power-performance trade-offs via adjustable spike bit-widths. Evaluations on simulated and real-world tetrode signals with biological drift showed NSS outperformed established pipelines such as WaveClus3 and PCA + KMeans. With 2-bit graded spikes, NSS on Loihi 2 outperformed NSS implemented with leaky integrate-and-fire neuron and achieved an F 1 -score of 77% (+10% improvement) while consuming 8.6 mW (+1.65 mW) when tested on a drifting recording, with a computational processing time of 0.25 ms (+60 µ s) per inference.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/2634-4386/ae006b
- OA Status
- gold
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413802164
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4413802164Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/2634-4386/ae006bDigital Object Identifier
- Title
-
Unsupervised sparse coding-based spiking neural network for real-time spike sortingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-28Full publication date if available
- Authors
-
Alexis Melot, Sean U. N. Wood, Yannick Coffinier, Pierre Yger, Fabien AlibartList of authors in order
- Landing page
-
https://doi.org/10.1088/2634-4386/ae006bPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/2634-4386/ae006bDirect OA link when available
- Concepts
-
Spike sorting, Neuromorphic engineering, Computer science, Spike (software development), Spiking neural network, Neural coding, Neural decoding, Decoding methods, Artificial intelligence, Artificial neural network, Pattern recognition (psychology), Perceptron, Sorting, Algorithm, Software engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4413802164 |
|---|---|
| doi | https://doi.org/10.1088/2634-4386/ae006b |
| ids.doi | https://doi.org/10.1088/2634-4386/ae006b |
| ids.openalex | https://openalex.org/W4413802164 |
| fwci | 0.0 |
| type | article |
| title | Unsupervised sparse coding-based spiking neural network for real-time spike sorting |
| awards[0].id | https://openalex.org/G5737957619 |
| awards[0].funder_id | https://openalex.org/F4320334678 |
| awards[0].display_name | |
| awards[0].funder_award_id | 773228 |
| awards[0].funder_display_name | European Research Council |
| biblio.issue | 3 |
| biblio.volume | 5 |
| biblio.last_page | 034015 |
| biblio.first_page | 034015 |
| 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 | 0.9998999834060669 |
| 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/T12611 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9991000294685364 |
| 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 Reservoir Computing |
| topics[2].id | https://openalex.org/T10581 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9983000159263611 |
| 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 | Neural dynamics and brain function |
| funders[0].id | https://openalex.org/F4320334678 |
| funders[0].ror | https://ror.org/0472cxd90 |
| funders[0].display_name | European Research Council |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | GBP |
| apc_list.value_usd | 2453 |
| apc_paid.value | 2000 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 2453 |
| concepts[0].id | https://openalex.org/C2777613131 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9405249953269958 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2003571 |
| concepts[0].display_name | Spike sorting |
| concepts[1].id | https://openalex.org/C151927369 |
| concepts[1].level | 3 |
| concepts[1].score | 0.8802725076675415 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1981312 |
| concepts[1].display_name | Neuromorphic engineering |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7941992282867432 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2781390188 |
| concepts[3].level | 2 |
| concepts[3].score | 0.7916463613510132 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q25203449 |
| concepts[3].display_name | Spike (software development) |
| concepts[4].id | https://openalex.org/C11731999 |
| concepts[4].level | 3 |
| concepts[4].score | 0.717484176158905 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q9067355 |
| concepts[4].display_name | Spiking neural network |
| concepts[5].id | https://openalex.org/C77637269 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5831729769706726 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7002051 |
| concepts[5].display_name | Neural coding |
| concepts[6].id | https://openalex.org/C40743351 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5116322636604309 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7002049 |
| concepts[6].display_name | Neural decoding |
| concepts[7].id | https://openalex.org/C57273362 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4867396354675293 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q576722 |
| concepts[7].display_name | Decoding methods |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4748958647251129 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C50644808 |
| concepts[9].level | 2 |
| concepts[9].score | 0.47306692600250244 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[9].display_name | Artificial neural network |
| concepts[10].id | https://openalex.org/C153180895 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4449133276939392 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[10].display_name | Pattern recognition (psychology) |
| concepts[11].id | https://openalex.org/C60908668 |
| concepts[11].level | 3 |
| concepts[11].score | 0.442798376083374 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q690207 |
| concepts[11].display_name | Perceptron |
| concepts[12].id | https://openalex.org/C111696304 |
| concepts[12].level | 2 |
| concepts[12].score | 0.43510767817497253 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q2303697 |
| concepts[12].display_name | Sorting |
| concepts[13].id | https://openalex.org/C11413529 |
| concepts[13].level | 1 |
| concepts[13].score | 0.1771598756313324 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[13].display_name | Algorithm |
| concepts[14].id | https://openalex.org/C115903868 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q80993 |
| concepts[14].display_name | Software engineering |
| keywords[0].id | https://openalex.org/keywords/spike-sorting |
| keywords[0].score | 0.9405249953269958 |
| keywords[0].display_name | Spike sorting |
| keywords[1].id | https://openalex.org/keywords/neuromorphic-engineering |
| keywords[1].score | 0.8802725076675415 |
| keywords[1].display_name | Neuromorphic engineering |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7941992282867432 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/spike |
| keywords[3].score | 0.7916463613510132 |
| keywords[3].display_name | Spike (software development) |
| keywords[4].id | https://openalex.org/keywords/spiking-neural-network |
| keywords[4].score | 0.717484176158905 |
| keywords[4].display_name | Spiking neural network |
| keywords[5].id | https://openalex.org/keywords/neural-coding |
| keywords[5].score | 0.5831729769706726 |
| keywords[5].display_name | Neural coding |
| keywords[6].id | https://openalex.org/keywords/neural-decoding |
| keywords[6].score | 0.5116322636604309 |
| keywords[6].display_name | Neural decoding |
| keywords[7].id | https://openalex.org/keywords/decoding-methods |
| keywords[7].score | 0.4867396354675293 |
| keywords[7].display_name | Decoding methods |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.4748958647251129 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[9].score | 0.47306692600250244 |
| keywords[9].display_name | Artificial neural network |
| keywords[10].id | https://openalex.org/keywords/pattern-recognition |
| keywords[10].score | 0.4449133276939392 |
| keywords[10].display_name | Pattern recognition (psychology) |
| keywords[11].id | https://openalex.org/keywords/perceptron |
| keywords[11].score | 0.442798376083374 |
| keywords[11].display_name | Perceptron |
| keywords[12].id | https://openalex.org/keywords/sorting |
| keywords[12].score | 0.43510767817497253 |
| keywords[12].display_name | Sorting |
| keywords[13].id | https://openalex.org/keywords/algorithm |
| keywords[13].score | 0.1771598756313324 |
| keywords[13].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.1088/2634-4386/ae006b |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210212933 |
| locations[0].source.issn | 2634-4386 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2634-4386 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Neuromorphic Computing and Engineering |
| locations[0].source.host_organization | https://openalex.org/P4310320083 |
| locations[0].source.host_organization_name | IOP Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| locations[0].source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Neuromorphic Computing and Engineering |
| locations[0].landing_page_url | https://doi.org/10.1088/2634-4386/ae006b |
| locations[1].id | pmh:oai:doaj.org/article:e70da81dab9e46769555144cb4fd55f1 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Neuromorphic Computing and Engineering, Vol 5, Iss 3, p 034015 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/e70da81dab9e46769555144cb4fd55f1 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5092524554 |
| authorships[0].author.orcid | https://orcid.org/0009-0001-4305-8566 |
| authorships[0].author.display_name | Alexis Melot |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Alexis Melot |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5016964600 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6821-1619 |
| authorships[1].author.display_name | Sean U. N. Wood |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sean U. N. Wood |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5019078293 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Yannick Coffinier |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yannick Coffinier |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5019494088 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-1376-5240 |
| authorships[3].author.display_name | Pierre Yger |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Pierre Yger |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5091510784 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9591-220X |
| authorships[4].author.display_name | Fabien Alibart |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Fabien Alibart |
| authorships[4].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://doi.org/10.1088/2634-4386/ae006b |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Unsupervised sparse coding-based spiking neural network for real-time spike sorting |
| has_fulltext | False |
| 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 | 0.9998999834060669 |
| 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/W3089892344, https://openalex.org/W2960220682, https://openalex.org/W4372267706, https://openalex.org/W4313442939, https://openalex.org/W4386227293, https://openalex.org/W2885510266, https://openalex.org/W4288055417, https://openalex.org/W4287758233, https://openalex.org/W3136467750, https://openalex.org/W4392309369 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1088/2634-4386/ae006b |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210212933 |
| best_oa_location.source.issn | 2634-4386 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2634-4386 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Neuromorphic Computing and Engineering |
| best_oa_location.source.host_organization | https://openalex.org/P4310320083 |
| best_oa_location.source.host_organization_name | IOP Publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| best_oa_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Neuromorphic Computing and Engineering |
| best_oa_location.landing_page_url | https://doi.org/10.1088/2634-4386/ae006b |
| primary_location.id | doi:10.1088/2634-4386/ae006b |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210212933 |
| primary_location.source.issn | 2634-4386 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2634-4386 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Neuromorphic Computing and Engineering |
| primary_location.source.host_organization | https://openalex.org/P4310320083 |
| primary_location.source.host_organization_name | IOP Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Neuromorphic Computing and Engineering |
| primary_location.landing_page_url | https://doi.org/10.1088/2634-4386/ae006b |
| publication_date | 2025-08-28 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2040309074, https://openalex.org/W2966268805, https://openalex.org/W2136104104, https://openalex.org/W2767493192, https://openalex.org/W3142226569, https://openalex.org/W2467288641, https://openalex.org/W2791680029, https://openalex.org/W3104213443, https://openalex.org/W2756375591, https://openalex.org/W2096424603, https://openalex.org/W2900268635, https://openalex.org/W3166235535, https://openalex.org/W2068711549, https://openalex.org/W3038842541, https://openalex.org/W3178771700, https://openalex.org/W4400720550, https://openalex.org/W4225687736, https://openalex.org/W4200386178, https://openalex.org/W3200892417, https://openalex.org/W4289938705, https://openalex.org/W4322627015, https://openalex.org/W4387357402, https://openalex.org/W3025907477, https://openalex.org/W2512274313, https://openalex.org/W3088461492, https://openalex.org/W1990007244, https://openalex.org/W2547346556, https://openalex.org/W2908199053, https://openalex.org/W3139657805, https://openalex.org/W2783525259, https://openalex.org/W3111992249, https://openalex.org/W3214253320, https://openalex.org/W2102397476, https://openalex.org/W4390993442, https://openalex.org/W2100543212, https://openalex.org/W2591673237, https://openalex.org/W2186584449, https://openalex.org/W4399302025, https://openalex.org/W1983634702, https://openalex.org/W2146842127, https://openalex.org/W2537543972, https://openalex.org/W2068913008, https://openalex.org/W4386388638, https://openalex.org/W1514207267, https://openalex.org/W2105464873, https://openalex.org/W2160547390, https://openalex.org/W2612973180, https://openalex.org/W2960052088, https://openalex.org/W2124134481, https://openalex.org/W2130996045, https://openalex.org/W2514526063, https://openalex.org/W2802154369, https://openalex.org/W3213485303, https://openalex.org/W2608368962, https://openalex.org/W4297619700, https://openalex.org/W1973009575, https://openalex.org/W2875003334, https://openalex.org/W2972599999, https://openalex.org/W2963309332 |
| referenced_works_count | 59 |
| abstract_inverted_index.+ | 142 |
| abstract_inverted_index.1 | 163 |
| abstract_inverted_index.2 | 151 |
| abstract_inverted_index.A | 20 |
| abstract_inverted_index.F | 162 |
| abstract_inverted_index.a | 4, 49, 108, 178, 182 |
| abstract_inverted_index.2, | 107 |
| abstract_inverted_index.To | 95 |
| abstract_inverted_index.an | 88, 161 |
| abstract_inverted_index.as | 138 |
| abstract_inverted_index.at | 32 |
| abstract_inverted_index.in | 7, 23, 87 |
| abstract_inverted_index.is | 3, 26 |
| abstract_inverted_index.mW | 172 |
| abstract_inverted_index.ms | 188 |
| abstract_inverted_index.of | 16, 101, 165, 186 |
| abstract_inverted_index.on | 123, 149, 177 |
| abstract_inverted_index.s) | 191 |
| abstract_inverted_index.to | 69, 82 |
| abstract_inverted_index.µ | 190 |
| abstract_inverted_index.77% | 166 |
| abstract_inverted_index.8.6 | 171 |
| abstract_inverted_index.NSS | 60, 80, 133, 148, 153 |
| abstract_inverted_index.PCA | 141 |
| abstract_inverted_index.and | 91, 125, 140, 159 |
| abstract_inverted_index.for | 56, 66 |
| abstract_inverted_index.key | 21 |
| abstract_inverted_index.mW) | 174 |
| abstract_inverted_index.per | 192 |
| abstract_inverted_index.the | 14, 33, 44, 62 |
| abstract_inverted_index.via | 118 |
| abstract_inverted_index.was | 112 |
| abstract_inverted_index.(+60 | 189 |
| abstract_inverted_index.0.25 | 187 |
| abstract_inverted_index.This | 41 |
| abstract_inverted_index.With | 144 |
| abstract_inverted_index.edge | 34 |
| abstract_inverted_index.from | 73 |
| abstract_inverted_index.high | 37 |
| abstract_inverted_index.like | 104 |
| abstract_inverted_index.sort | 83 |
| abstract_inverted_index.step | 6 |
| abstract_inverted_index.such | 137 |
| abstract_inverted_index.time | 185 |
| abstract_inverted_index.when | 175 |
| abstract_inverted_index.with | 76, 129, 155, 181 |
| abstract_inverted_index.(+10% | 167 |
| abstract_inverted_index.2-bit | 145 |
| abstract_inverted_index.Loihi | 106, 150 |
| abstract_inverted_index.Spike | 1 |
| abstract_inverted_index.drift | 131 |
| abstract_inverted_index.leaky | 156 |
| abstract_inverted_index.model | 111 |
| abstract_inverted_index.noisy | 74 |
| abstract_inverted_index.spike | 30, 58, 85, 98, 120 |
| abstract_inverted_index.study | 42 |
| abstract_inverted_index.while | 35, 169 |
| abstract_inverted_index.(+1.65 | 173 |
| abstract_inverted_index.(NSS), | 48 |
| abstract_inverted_index.-score | 164 |
| abstract_inverted_index.coding | 68, 99 |
| abstract_inverted_index.custom | 109 |
| abstract_inverted_index.events | 75 |
| abstract_inverted_index.graded | 146 |
| abstract_inverted_index.learns | 81 |
| abstract_inverted_index.neural | 11, 38, 53 |
| abstract_inverted_index.neuron | 110, 158 |
| abstract_inverted_index.online | 89 |
| abstract_inverted_index.showed | 132 |
| abstract_inverted_index.sorter | 47 |
| abstract_inverted_index.sparse | 46, 67 |
| abstract_inverted_index.tested | 176 |
| abstract_inverted_index.KMeans. | 143 |
| abstract_inverted_index.compact | 50 |
| abstract_inverted_index.crucial | 5 |
| abstract_inverted_index.exploit | 96 |
| abstract_inverted_index.extract | 70 |
| abstract_inverted_index.fashion | 90 |
| abstract_inverted_index.keeping | 36 |
| abstract_inverted_index.locally | 63 |
| abstract_inverted_index.network | 54 |
| abstract_inverted_index.reduced | 77 |
| abstract_inverted_index.signals | 128 |
| abstract_inverted_index.sorting | 2, 31 |
| abstract_inverted_index.spikes, | 147 |
| abstract_inverted_index.spiking | 52 |
| abstract_inverted_index.tetrode | 127 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.achieved | 160 |
| abstract_inverted_index.decoding | 8, 39 |
| abstract_inverted_index.demands. | 79 |
| abstract_inverted_index.detected | 84 |
| abstract_inverted_index.drifting | 179 |
| abstract_inverted_index.enabling | 13, 114 |
| abstract_inverted_index.entirely | 93 |
| abstract_inverted_index.features | 72 |
| abstract_inverted_index.flexible | 115 |
| abstract_inverted_index.neuronal | 18 |
| abstract_inverted_index.operates | 92 |
| abstract_inverted_index.relevant | 71 |
| abstract_inverted_index.signals, | 12 |
| abstract_inverted_index.sorting. | 59 |
| abstract_inverted_index.Intel’s | 105 |
| abstract_inverted_index.WaveClus3 | 139 |
| abstract_inverted_index.achieving | 27 |
| abstract_inverted_index.activity. | 19 |
| abstract_inverted_index.algorithm | 65 |
| abstract_inverted_index.challenge | 22 |
| abstract_inverted_index.consuming | 170 |
| abstract_inverted_index.efficient | 57 |
| abstract_inverted_index.leverages | 61 |
| abstract_inverted_index.low-power | 29 |
| abstract_inverted_index.multi-bit | 97 |
| abstract_inverted_index.optimized | 55 |
| abstract_inverted_index.pipelines | 136 |
| abstract_inverted_index.platforms | 103 |
| abstract_inverted_index.simulated | 124 |
| abstract_inverted_index.two-layer | 51 |
| abstract_inverted_index.waveforms | 86 |
| abstract_inverted_index.adjustable | 119 |
| abstract_inverted_index.biological | 130 |
| abstract_inverted_index.individual | 17 |
| abstract_inverted_index.inference. | 193 |
| abstract_inverted_index.interfaces | 25 |
| abstract_inverted_index.introduces | 43 |
| abstract_inverted_index.processing | 184 |
| abstract_inverted_index.real-time, | 28 |
| abstract_inverted_index.real-world | 126 |
| abstract_inverted_index.recording, | 180 |
| abstract_inverted_index.trade-offs | 117 |
| abstract_inverted_index.Evaluations | 122 |
| abstract_inverted_index.bit-widths. | 121 |
| abstract_inverted_index.competitive | 64 |
| abstract_inverted_index.established | 135 |
| abstract_inverted_index.implemented | 154 |
| abstract_inverted_index.capabilities | 100 |
| abstract_inverted_index.implemented, | 113 |
| abstract_inverted_index.improvement) | 168 |
| abstract_inverted_index.multichannel | 9 |
| abstract_inverted_index.neuromorphic | 45, 102 |
| abstract_inverted_index.outperformed | 134, 152 |
| abstract_inverted_index.performance. | 40 |
| abstract_inverted_index.computational | 78, 183 |
| abstract_inverted_index.extracellular | 10 |
| abstract_inverted_index.unsupervised. | 94 |
| abstract_inverted_index.identification | 15 |
| abstract_inverted_index.brain–machine | 24 |
| abstract_inverted_index.power-performance | 116 |
| abstract_inverted_index.integrate-and-fire | 157 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.41282213 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |