An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike Trains Article Swipe
Gorana Mijatović
,
Yuri Antonacci
,
Tatjana Lončar-Turukalo
,
Ludovico Minati
,
Luca Faes
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tbme.2021.3073833
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/tbme.2021.3073833
Objective: While understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience, existing methods either do not consider the inherent point-process nature of spike trains or are based on parametric assumptions. This work presents an information-theoretic framework for the model-free, continuous-time estimation of both undirected (symmetric) and directed (Granger-causal) interactions between spike trains. Methods: The framework computes the mutual information rate (MIR) and the transfer entropy rate (TER) for two point processes X and Y, showing that the MIR between X and Y can be decomposed as the sum of the TER along the directions X Y and Y X. We present theoretical expressions and introduce strategies to estimate efficiently the two measures through nearest neighbor statistics. Results: Using simulations of independent and coupled spike train processes, we show the accuracy of MIR and TER to assess interactions even for weakly coupled and short realizations, and prove the superiority of continuous-time estimation over the standard discrete-time approach. In a real data scenario of recordings from in-vitro preparations of spontaneously-growing cultures of cortical neurons, we show the ability of MIR and TER to describe how the functional organization of the networks of spike train interactions emerges through maturation of the neuronal cultures. Conclusion and Significance: the proposed framework provides principled measures to assess undirected and directed spike train interactions with more efficiency and flexibility than previous discrete-time or parametric approaches, opening new perspectives for the analysis of point-process data in neuroscience and many other fields.
Related Topics
Concepts
Point process
Spike (software development)
Spike train
Computer science
Parametric statistics
Mutual information
Measure (data warehouse)
Train
Information theory
Entropy (arrow of time)
Transfer entropy
Artificial intelligence
Principle of maximum entropy
Data mining
Mathematics
Physics
Statistics
Software engineering
Cartography
Geography
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://hdl.handle.net/10447/511659
- OA Status
- green
- Cited By
- 38
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3155462549
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3155462549Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tbme.2021.3073833Digital Object Identifier
- Title
-
An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike TrainsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-01Full publication date if available
- Authors
-
Gorana Mijatović, Yuri Antonacci, Tatjana Lončar-Turukalo, Ludovico Minati, Luca FaesList of authors in order
- Landing page
-
https://hdl.handle.net/10447/511659Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://hdl.handle.net/10447/511659Direct OA link when available
- Concepts
-
Point process, Spike (software development), Spike train, Computer science, Parametric statistics, Mutual information, Measure (data warehouse), Train, Information theory, Entropy (arrow of time), Transfer entropy, Artificial intelligence, Principle of maximum entropy, Data mining, Mathematics, Physics, Statistics, Software engineering, Cartography, Geography, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
38Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 10, 2023: 6, 2022: 8, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3155462549 |
|---|---|
| doi | https://doi.org/10.1109/tbme.2021.3073833 |
| ids.mag | 3155462549 |
| ids.openalex | https://openalex.org/W3155462549 |
| fwci | 3.89406362 |
| type | article |
| title | An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike Trains |
| awards[0].id | https://openalex.org/G832876765 |
| awards[0].funder_id | https://openalex.org/F4320322729 |
| awards[0].display_name | |
| awards[0].funder_award_id | 451-03-68/2020-14/200156 |
| awards[0].funder_display_name | Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja |
| awards[1].id | https://openalex.org/G7707468454 |
| awards[1].funder_id | https://openalex.org/F4320332999 |
| awards[1].display_name | |
| awards[1].funder_award_id | 856967 |
| awards[1].funder_display_name | Horizon 2020 Framework Programme |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10581 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | Neural dynamics and brain function |
| topics[1].id | https://openalex.org/T11601 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.9997000098228455 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2804 |
| topics[1].subfield.display_name | Cellular and Molecular Neuroscience |
| topics[1].display_name | Neuroscience and Neural Engineering |
| topics[2].id | https://openalex.org/T10540 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9979000091552734 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1304 |
| topics[2].subfield.display_name | Biophysics |
| topics[2].display_name | Advanced Fluorescence Microscopy Techniques |
| funders[0].id | https://openalex.org/F4320322729 |
| funders[0].ror | https://ror.org/01znas443 |
| funders[0].display_name | Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja |
| funders[1].id | https://openalex.org/F4320332999 |
| funders[1].ror | https://ror.org/00k4n6c32 |
| funders[1].display_name | Horizon 2020 Framework Programme |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C88871306 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7333134412765503 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7208287 |
| concepts[0].display_name | Point process |
| concepts[1].id | https://openalex.org/C2781390188 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6928853392601013 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q25203449 |
| concepts[1].display_name | Spike (software development) |
| concepts[2].id | https://openalex.org/C2909946758 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6923820376396179 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q194277 |
| concepts[2].display_name | Spike train |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6453108787536621 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C117251300 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6409374475479126 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1849855 |
| concepts[4].display_name | Parametric statistics |
| concepts[5].id | https://openalex.org/C152139883 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5993813872337341 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q252973 |
| concepts[5].display_name | Mutual information |
| concepts[6].id | https://openalex.org/C2780009758 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5419796705245972 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6804172 |
| concepts[6].display_name | Measure (data warehouse) |
| concepts[7].id | https://openalex.org/C190839683 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5179773569107056 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2448197 |
| concepts[7].display_name | Train |
| concepts[8].id | https://openalex.org/C52622258 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5050690770149231 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q131222 |
| concepts[8].display_name | Information theory |
| concepts[9].id | https://openalex.org/C106301342 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4869771897792816 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q4117933 |
| concepts[9].display_name | Entropy (arrow of time) |
| concepts[10].id | https://openalex.org/C182049051 |
| concepts[10].level | 3 |
| concepts[10].score | 0.45373693108558655 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q17147155 |
| concepts[10].display_name | Transfer entropy |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.4083993434906006 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C9679016 |
| concepts[12].level | 2 |
| concepts[12].score | 0.2593558430671692 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1417473 |
| concepts[12].display_name | Principle of maximum entropy |
| concepts[13].id | https://openalex.org/C124101348 |
| concepts[13].level | 1 |
| concepts[13].score | 0.2389308214187622 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[13].display_name | Data mining |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.22251877188682556 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C121332964 |
| concepts[15].level | 0 |
| concepts[15].score | 0.1021338701248169 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[15].display_name | Physics |
| concepts[16].id | https://openalex.org/C105795698 |
| concepts[16].level | 1 |
| concepts[16].score | 0.09371215105056763 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[16].display_name | Statistics |
| concepts[17].id | https://openalex.org/C115903868 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q80993 |
| concepts[17].display_name | Software engineering |
| concepts[18].id | https://openalex.org/C58640448 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[18].display_name | Cartography |
| concepts[19].id | https://openalex.org/C205649164 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[19].display_name | Geography |
| concepts[20].id | https://openalex.org/C62520636 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[20].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/point-process |
| keywords[0].score | 0.7333134412765503 |
| keywords[0].display_name | Point process |
| keywords[1].id | https://openalex.org/keywords/spike |
| keywords[1].score | 0.6928853392601013 |
| keywords[1].display_name | Spike (software development) |
| keywords[2].id | https://openalex.org/keywords/spike-train |
| keywords[2].score | 0.6923820376396179 |
| keywords[2].display_name | Spike train |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6453108787536621 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/parametric-statistics |
| keywords[4].score | 0.6409374475479126 |
| keywords[4].display_name | Parametric statistics |
| keywords[5].id | https://openalex.org/keywords/mutual-information |
| keywords[5].score | 0.5993813872337341 |
| keywords[5].display_name | Mutual information |
| keywords[6].id | https://openalex.org/keywords/measure |
| keywords[6].score | 0.5419796705245972 |
| keywords[6].display_name | Measure (data warehouse) |
| keywords[7].id | https://openalex.org/keywords/train |
| keywords[7].score | 0.5179773569107056 |
| keywords[7].display_name | Train |
| keywords[8].id | https://openalex.org/keywords/information-theory |
| keywords[8].score | 0.5050690770149231 |
| keywords[8].display_name | Information theory |
| keywords[9].id | https://openalex.org/keywords/entropy |
| keywords[9].score | 0.4869771897792816 |
| keywords[9].display_name | Entropy (arrow of time) |
| keywords[10].id | https://openalex.org/keywords/transfer-entropy |
| keywords[10].score | 0.45373693108558655 |
| keywords[10].display_name | Transfer entropy |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.4083993434906006 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/principle-of-maximum-entropy |
| keywords[12].score | 0.2593558430671692 |
| keywords[12].display_name | Principle of maximum entropy |
| keywords[13].id | https://openalex.org/keywords/data-mining |
| keywords[13].score | 0.2389308214187622 |
| keywords[13].display_name | Data mining |
| keywords[14].id | https://openalex.org/keywords/mathematics |
| keywords[14].score | 0.22251877188682556 |
| keywords[14].display_name | Mathematics |
| keywords[15].id | https://openalex.org/keywords/physics |
| keywords[15].score | 0.1021338701248169 |
| keywords[15].display_name | Physics |
| keywords[16].id | https://openalex.org/keywords/statistics |
| keywords[16].score | 0.09371215105056763 |
| keywords[16].display_name | Statistics |
| language | en |
| locations[0].id | pmh:oai:iris.unipa.it:10447/511659 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306401065 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Nova Science Publishers (Nova Science Publishers, Inc.) |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | other-oa |
| locations[0].pdf_url | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | info:eu-repo/semantics/article |
| locations[0].license_id | https://openalex.org/licenses/other-oa |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://hdl.handle.net/10447/511659 |
| authorships[0].author.id | https://openalex.org/A5016282913 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7409-8130 |
| authorships[0].author.display_name | Gorana Mijatović |
| authorships[0].countries | RS |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I170726198 |
| authorships[0].affiliations[0].raw_affiliation_string | Faculty of Technical Sciences, University of Novi Sad, Serbia |
| authorships[0].institutions[0].id | https://openalex.org/I170726198 |
| authorships[0].institutions[0].ror | https://ror.org/00xa57a59 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I170726198 |
| authorships[0].institutions[0].country_code | RS |
| authorships[0].institutions[0].display_name | University of Novi Sad |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Gorana Mijatovic |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Faculty of Technical Sciences, University of Novi Sad, Serbia |
| authorships[1].author.id | https://openalex.org/A5000782436 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6956-6323 |
| authorships[1].author.display_name | Yuri Antonacci |
| authorships[1].countries | IT |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I900890020 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Physics and Chemistry "Emilio Segrè,", University of Palermo, Italy |
| authorships[1].institutions[0].id | https://openalex.org/I900890020 |
| authorships[1].institutions[0].ror | https://ror.org/044k9ta02 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I900890020 |
| authorships[1].institutions[0].country_code | IT |
| authorships[1].institutions[0].display_name | University of Palermo |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yuri Antonacci |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Physics and Chemistry "Emilio Segrè,", University of Palermo, Italy |
| authorships[2].author.id | https://openalex.org/A5003713136 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3582-8073 |
| authorships[2].author.display_name | Tatjana Lončar-Turukalo |
| authorships[2].countries | RS |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I170726198 |
| authorships[2].affiliations[0].raw_affiliation_string | Faculty of Technical Sciences, University of Novi Sad, Serbia |
| authorships[2].institutions[0].id | https://openalex.org/I170726198 |
| authorships[2].institutions[0].ror | https://ror.org/00xa57a59 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I170726198 |
| authorships[2].institutions[0].country_code | RS |
| authorships[2].institutions[0].display_name | University of Novi Sad |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tatjana Loncar-Turukalo |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Faculty of Technical Sciences, University of Novi Sad, Serbia |
| authorships[3].author.id | https://openalex.org/A5091055664 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-2532-1674 |
| authorships[3].author.display_name | Ludovico Minati |
| authorships[3].countries | IT |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I193223587 |
| authorships[3].affiliations[0].raw_affiliation_string | Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy |
| authorships[3].institutions[0].id | https://openalex.org/I193223587 |
| authorships[3].institutions[0].ror | https://ror.org/05trd4x28 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I193223587 |
| authorships[3].institutions[0].country_code | IT |
| authorships[3].institutions[0].display_name | University of Trento |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ludovico Minati |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy |
| authorships[4].author.id | https://openalex.org/A5036606451 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-3271-5348 |
| authorships[4].author.display_name | Luca Faes |
| authorships[4].countries | IT |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I900890020 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Engineering, University of Palermo, Palermo, Italy |
| authorships[4].institutions[0].id | https://openalex.org/I900890020 |
| authorships[4].institutions[0].ror | https://ror.org/044k9ta02 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I900890020 |
| authorships[4].institutions[0].country_code | IT |
| authorships[4].institutions[0].display_name | University of Palermo |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Luca Faes |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Engineering, University of Palermo, Palermo, Italy |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | http://hdl.handle.net/10447/511659 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike Trains |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10581 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | Neural dynamics and brain function |
| related_works | https://openalex.org/W3124771927, https://openalex.org/W3212925274, https://openalex.org/W4225940264, https://openalex.org/W2750125254, https://openalex.org/W2057609120, https://openalex.org/W4313422465, https://openalex.org/W2168024814, https://openalex.org/W2889544313, https://openalex.org/W2067412945, https://openalex.org/W2111510771 |
| cited_by_count | 38 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 9 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 10 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 6 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 8 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 5 |
| locations_count | 1 |
| best_oa_location.id | pmh:oai:iris.unipa.it:10447/511659 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306401065 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| 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 | Nova Science Publishers (Nova Science Publishers, Inc.) |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | other-oa |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | info:eu-repo/semantics/article |
| best_oa_location.license_id | https://openalex.org/licenses/other-oa |
| 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://hdl.handle.net/10447/511659 |
| primary_location.id | pmh:oai:iris.unipa.it:10447/511659 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306401065 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Nova Science Publishers (Nova Science Publishers, Inc.) |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | other-oa |
| primary_location.pdf_url | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | info:eu-repo/semantics/article |
| primary_location.license_id | https://openalex.org/licenses/other-oa |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://hdl.handle.net/10447/511659 |
| publication_date | 2021-12-01 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2607565677, https://openalex.org/W2158948112, https://openalex.org/W1978506705, https://openalex.org/W2995974119, https://openalex.org/W2486848538, https://openalex.org/W2029148261, https://openalex.org/W2992983693, https://openalex.org/W2079959006, https://openalex.org/W2149013149, https://openalex.org/W2036681456, https://openalex.org/W2155525875, https://openalex.org/W2755840641, https://openalex.org/W2326655560, https://openalex.org/W2033079303, https://openalex.org/W2095787854, https://openalex.org/W2544092617, https://openalex.org/W2124264958, https://openalex.org/W2041782669, https://openalex.org/W563793012, https://openalex.org/W2749908780, https://openalex.org/W3112735565, https://openalex.org/W2129621723, https://openalex.org/W2797486699, https://openalex.org/W2165417170, https://openalex.org/W2060464767, https://openalex.org/W2952121911, https://openalex.org/W2083579647, https://openalex.org/W2166186596, https://openalex.org/W2178225550, https://openalex.org/W2003900693, https://openalex.org/W2079914348, https://openalex.org/W2054402190, https://openalex.org/W3035258118, https://openalex.org/W4251749444, https://openalex.org/W2161485858, https://openalex.org/W2409304057, https://openalex.org/W1529448825, https://openalex.org/W2964650265, https://openalex.org/W2074019524, https://openalex.org/W2066009496, https://openalex.org/W2084008199, https://openalex.org/W1991266025, https://openalex.org/W2044770804, https://openalex.org/W2567528096, https://openalex.org/W3126099958, https://openalex.org/W2092939357, https://openalex.org/W3154625295, https://openalex.org/W2164467450, https://openalex.org/W2055632453, https://openalex.org/W2240661524 |
| referenced_works_count | 50 |
| abstract_inverted_index.X | 82, 90, 105 |
| abstract_inverted_index.Y | 92, 106, 108 |
| abstract_inverted_index.a | 17, 168 |
| abstract_inverted_index.In | 167 |
| abstract_inverted_index.We | 110 |
| abstract_inverted_index.X. | 109 |
| abstract_inverted_index.Y, | 84 |
| abstract_inverted_index.an | 44 |
| abstract_inverted_index.as | 96 |
| abstract_inverted_index.be | 94 |
| abstract_inverted_index.do | 25 |
| abstract_inverted_index.in | 20, 248 |
| abstract_inverted_index.is | 16 |
| abstract_inverted_index.of | 9, 32, 52, 99, 130, 141, 159, 172, 177, 180, 187, 197, 200, 207, 245 |
| abstract_inverted_index.on | 38 |
| abstract_inverted_index.or | 35, 236 |
| abstract_inverted_index.to | 117, 145, 191, 220 |
| abstract_inverted_index.we | 137, 183 |
| abstract_inverted_index.MIR | 88, 142, 188 |
| abstract_inverted_index.TER | 101, 144, 190 |
| abstract_inverted_index.The | 64 |
| abstract_inverted_index.and | 56, 72, 83, 91, 107, 114, 132, 143, 152, 155, 189, 212, 223, 231, 250 |
| abstract_inverted_index.are | 36 |
| abstract_inverted_index.can | 93 |
| abstract_inverted_index.for | 47, 78, 149, 242 |
| abstract_inverted_index.how | 193 |
| abstract_inverted_index.key | 18 |
| abstract_inverted_index.new | 240 |
| abstract_inverted_index.not | 26 |
| abstract_inverted_index.sum | 98 |
| abstract_inverted_index.the | 3, 28, 48, 67, 73, 87, 97, 100, 103, 120, 139, 157, 163, 185, 194, 198, 208, 214, 243 |
| abstract_inverted_index.two | 79, 121 |
| abstract_inverted_index.This | 41 |
| abstract_inverted_index.both | 53 |
| abstract_inverted_index.data | 170, 247 |
| abstract_inverted_index.even | 148 |
| abstract_inverted_index.from | 12, 174 |
| abstract_inverted_index.many | 251 |
| abstract_inverted_index.more | 229 |
| abstract_inverted_index.over | 162 |
| abstract_inverted_index.rate | 70, 76 |
| abstract_inverted_index.real | 169 |
| abstract_inverted_index.show | 138, 184 |
| abstract_inverted_index.than | 233 |
| abstract_inverted_index.that | 86 |
| abstract_inverted_index.with | 228 |
| abstract_inverted_index.work | 42 |
| abstract_inverted_index.(MIR) | 71 |
| abstract_inverted_index.(TER) | 77 |
| abstract_inverted_index.Using | 128 |
| abstract_inverted_index.While | 1 |
| abstract_inverted_index.along | 102 |
| abstract_inverted_index.among | 6 |
| abstract_inverted_index.based | 37 |
| abstract_inverted_index.other | 252 |
| abstract_inverted_index.point | 80 |
| abstract_inverted_index.prove | 156 |
| abstract_inverted_index.short | 153 |
| abstract_inverted_index.spike | 10, 33, 61, 134, 201, 225 |
| abstract_inverted_index.topic | 19 |
| abstract_inverted_index.train | 135, 202, 226 |
| abstract_inverted_index.units | 15 |
| abstract_inverted_index.assess | 146, 221 |
| abstract_inverted_index.either | 24 |
| abstract_inverted_index.mutual | 68 |
| abstract_inverted_index.nature | 31 |
| abstract_inverted_index.trains | 11, 34 |
| abstract_inverted_index.weakly | 150 |
| abstract_inverted_index.ability | 186 |
| abstract_inverted_index.between | 60, 89 |
| abstract_inverted_index.coupled | 133, 151 |
| abstract_inverted_index.emerges | 204 |
| abstract_inverted_index.entropy | 75 |
| abstract_inverted_index.fields. | 253 |
| abstract_inverted_index.methods | 23 |
| abstract_inverted_index.nearest | 124 |
| abstract_inverted_index.opening | 239 |
| abstract_inverted_index.present | 111 |
| abstract_inverted_index.showing | 85 |
| abstract_inverted_index.through | 123, 205 |
| abstract_inverted_index.trains. | 62 |
| abstract_inverted_index.Methods: | 63 |
| abstract_inverted_index.Results: | 127 |
| abstract_inverted_index.accuracy | 140 |
| abstract_inverted_index.analysis | 244 |
| abstract_inverted_index.computes | 66 |
| abstract_inverted_index.consider | 27 |
| abstract_inverted_index.cortical | 181 |
| abstract_inverted_index.cultures | 179 |
| abstract_inverted_index.describe | 192 |
| abstract_inverted_index.directed | 57, 224 |
| abstract_inverted_index.estimate | 118 |
| abstract_inverted_index.existing | 22 |
| abstract_inverted_index.in-vitro | 175 |
| abstract_inverted_index.inherent | 29 |
| abstract_inverted_index.measures | 122, 219 |
| abstract_inverted_index.multiple | 13 |
| abstract_inverted_index.neighbor | 125 |
| abstract_inverted_index.networks | 199 |
| abstract_inverted_index.neuronal | 14, 209 |
| abstract_inverted_index.neurons, | 182 |
| abstract_inverted_index.patterns | 5 |
| abstract_inverted_index.presents | 43 |
| abstract_inverted_index.previous | 234 |
| abstract_inverted_index.proposed | 215 |
| abstract_inverted_index.provides | 217 |
| abstract_inverted_index.scenario | 171 |
| abstract_inverted_index.standard | 164 |
| abstract_inverted_index.transfer | 74 |
| abstract_inverted_index.approach. | 166 |
| abstract_inverted_index.cultures. | 210 |
| abstract_inverted_index.framework | 46, 65, 216 |
| abstract_inverted_index.introduce | 115 |
| abstract_inverted_index.processes | 81 |
| abstract_inverted_index.Conclusion | 211 |
| abstract_inverted_index.Objective: | 0 |
| abstract_inverted_index.decomposed | 95 |
| abstract_inverted_index.directions | 104 |
| abstract_inverted_index.efficiency | 230 |
| abstract_inverted_index.estimation | 51, 161 |
| abstract_inverted_index.functional | 195 |
| abstract_inverted_index.maturation | 206 |
| abstract_inverted_index.parametric | 39, 237 |
| abstract_inverted_index.principled | 218 |
| abstract_inverted_index.processes, | 136 |
| abstract_inverted_index.recordings | 8, 173 |
| abstract_inverted_index.strategies | 116 |
| abstract_inverted_index.undirected | 54, 222 |
| abstract_inverted_index.(symmetric) | 55 |
| abstract_inverted_index.approaches, | 238 |
| abstract_inverted_index.efficiently | 119 |
| abstract_inverted_index.expressions | 113 |
| abstract_inverted_index.flexibility | 232 |
| abstract_inverted_index.independent | 131 |
| abstract_inverted_index.information | 69 |
| abstract_inverted_index.interaction | 4 |
| abstract_inverted_index.model-free, | 49 |
| abstract_inverted_index.simulations | 129 |
| abstract_inverted_index.statistics. | 126 |
| abstract_inverted_index.superiority | 158 |
| abstract_inverted_index.theoretical | 112 |
| abstract_inverted_index.assumptions. | 40 |
| abstract_inverted_index.interactions | 59, 147, 203, 227 |
| abstract_inverted_index.neuroscience | 249 |
| abstract_inverted_index.organization | 196 |
| abstract_inverted_index.perspectives | 241 |
| abstract_inverted_index.preparations | 176 |
| abstract_inverted_index.simultaneous | 7 |
| abstract_inverted_index.Significance: | 213 |
| abstract_inverted_index.discrete-time | 165, 235 |
| abstract_inverted_index.neuroscience, | 21 |
| abstract_inverted_index.point-process | 30, 246 |
| abstract_inverted_index.realizations, | 154 |
| abstract_inverted_index.understanding | 2 |
| abstract_inverted_index.continuous-time | 50, 160 |
| abstract_inverted_index.(Granger-causal) | 58 |
| abstract_inverted_index.information-theoretic | 45 |
| abstract_inverted_index.spontaneously-growing | 178 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.93420535 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |