Neuromorphic Networks as Revealed by Features Similarity Article Swipe
Alexandre Benatti
,
Henrique Ferraz de Arruda
,
Luciano Costa
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.10571
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.10571
The study of neuronal morphology is important not only for its potential relationship with neuronal dynamics, but also as a means to classify diverse types of cells and compare than among species, organs, and conditions. In the present work, we approach this interesting problem by using the concept of coincidence similarity, as well as a respectively derived method for mapping datasets into networks. The coincidence similarity has been found to allow some specific interesting properties which have allowed enhanced performance (selectivity and sensitivity) concerning several pattern recognition tasks. Several combinations of 20 morphological features were considered, and the respective networks were obtained by maximizing the literal modularity (in supervised manner) respectively to the involved parameters. Well-separated groups were obtained that provide a rich representation of the main similarity interrelationships between the 735 considered neuronal cells. A sequence of network configurations illustrating the progressive merging between cells and groups was also obtained by varying one of the coincidence parameters.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.10571
- https://arxiv.org/pdf/2207.10571
- OA Status
- green
- Cited By
- 1
- References
- 54
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- OpenAlex ID
- https://openalex.org/W4281999649
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https://openalex.org/W4281999649Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2207.10571Digital Object Identifier
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Neuromorphic Networks as Revealed by Features SimilarityWork title
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preprintOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-06-13Full publication date if available
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Alexandre Benatti, Henrique Ferraz de Arruda, Luciano CostaList of authors in order
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https://arxiv.org/abs/2207.10571Publisher landing page
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https://arxiv.org/pdf/2207.10571Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2207.10571Direct OA link when available
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Neuromorphic engineering, Similarity (geometry), Computer science, Artificial intelligence, Cognitive science, Psychology, Artificial neural network, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.between | 129, 144 |
| abstract_inverted_index.compare | 28 |
| abstract_inverted_index.concept | 47 |
| abstract_inverted_index.derived | 56 |
| abstract_inverted_index.diverse | 23 |
| abstract_inverted_index.literal | 105 |
| abstract_inverted_index.manner) | 109 |
| abstract_inverted_index.mapping | 59 |
| abstract_inverted_index.merging | 143 |
| abstract_inverted_index.network | 138 |
| abstract_inverted_index.organs, | 32 |
| abstract_inverted_index.pattern | 85 |
| abstract_inverted_index.present | 37 |
| abstract_inverted_index.problem | 43 |
| abstract_inverted_index.provide | 120 |
| abstract_inverted_index.several | 84 |
| abstract_inverted_index.varying | 152 |
| abstract_inverted_index.approach | 40 |
| abstract_inverted_index.classify | 22 |
| abstract_inverted_index.datasets | 60 |
| abstract_inverted_index.enhanced | 78 |
| abstract_inverted_index.features | 93 |
| abstract_inverted_index.involved | 113 |
| abstract_inverted_index.networks | 99 |
| abstract_inverted_index.neuronal | 3, 14, 133 |
| abstract_inverted_index.obtained | 101, 118, 150 |
| abstract_inverted_index.sequence | 136 |
| abstract_inverted_index.species, | 31 |
| abstract_inverted_index.specific | 72 |
| abstract_inverted_index.dynamics, | 15 |
| abstract_inverted_index.important | 6 |
| abstract_inverted_index.networks. | 62 |
| abstract_inverted_index.potential | 11 |
| abstract_inverted_index.concerning | 83 |
| abstract_inverted_index.considered | 132 |
| abstract_inverted_index.maximizing | 103 |
| abstract_inverted_index.modularity | 106 |
| abstract_inverted_index.morphology | 4 |
| abstract_inverted_index.properties | 74 |
| abstract_inverted_index.respective | 98 |
| abstract_inverted_index.similarity | 65, 127 |
| abstract_inverted_index.supervised | 108 |
| abstract_inverted_index.coincidence | 49, 64, 156 |
| abstract_inverted_index.conditions. | 34 |
| abstract_inverted_index.considered, | 95 |
| abstract_inverted_index.interesting | 42, 73 |
| abstract_inverted_index.parameters. | 114, 157 |
| abstract_inverted_index.performance | 79 |
| abstract_inverted_index.progressive | 142 |
| abstract_inverted_index.recognition | 86 |
| abstract_inverted_index.similarity, | 50 |
| abstract_inverted_index.(selectivity | 80 |
| abstract_inverted_index.combinations | 89 |
| abstract_inverted_index.illustrating | 140 |
| abstract_inverted_index.relationship | 12 |
| abstract_inverted_index.respectively | 55, 110 |
| abstract_inverted_index.sensitivity) | 82 |
| abstract_inverted_index.morphological | 92 |
| abstract_inverted_index.Well-separated | 115 |
| abstract_inverted_index.configurations | 139 |
| abstract_inverted_index.representation | 123 |
| abstract_inverted_index.interrelationships | 128 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |