Performance Comparison of Different HTM-Spatial Pooler Algorithms Based on Information-Theoretic Measures Article Swipe
Shiva Sanati
,
Modjtaba Rouhani
,
Ghosheh Abed Hodtani
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-024-11546-8
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-024-11546-8
Hierarchical temporal memory (HTM) is a promising unsupervised machine-learning algorithm that models key principles of neocortical computation. One of the main components of HTM is the spatial pooler (SP), which encodes binary input streams into sparse distributed representations (SDRs). In this paper, we propose an information-theoretic framework for the performance comparison of HTM-spatial pooler (SP) algorithms, specifically, for quantifying the similarities and differences between sparse distributed representations in SP algorithms. We evaluate SP's standalone performance, as well as HTM's overall performance. Our comparison of various SP algorithms using Renyi mutual information, Renyi divergence, and Henze–Penrose divergence measures reveals that the SP algorithm with learning and a logarithmic boosting function yields the most effective and useful data representation. Moreover, the most effective SP algorithm leads to superior HTM results. In addition, we utilize our proposed framework to compare HTM with other state-of-the-art sequential learning algorithms. We illustrate that HTM exhibits superior adaptability to pattern changes over time than long short term memory (LSTM), gated recurrent unit (GRU) and online sequential extreme learning machine (OS-ELM) algorithms. This superiority is evident from the lower Renyi divergence of HTM (0.23) compared to LSTM6000 (0.33), LSTM3000 (0.38), GRU (0.41), and OS-ELM (0.49). HTM also achieved the highest Renyi mutual information value of 0.79, outperforming LSTM6000 (0.73), LSTM3000 (0.71), GRU (0.68), and OS-ELM (0.62). These findings not only confirm the numerous advantages of HTM over other sequential learning algorithm, but also demonstrate the effectiveness of our proposed information-theoretic approach as a powerful framework for comparing and evaluating various learning algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11063-024-11546-8
- https://link.springer.com/content/pdf/10.1007/s11063-024-11546-8.pdf
- OA Status
- hybrid
- Cited By
- 2
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391883475
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391883475Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11063-024-11546-8Digital Object Identifier
- Title
-
Performance Comparison of Different HTM-Spatial Pooler Algorithms Based on Information-Theoretic MeasuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-16Full publication date if available
- Authors
-
Shiva Sanati, Modjtaba Rouhani, Ghosheh Abed HodtaniList of authors in order
- Landing page
-
https://doi.org/10.1007/s11063-024-11546-8Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s11063-024-11546-8.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s11063-024-11546-8.pdfDirect OA link when available
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Computational intelligence, Computer science, Algorithm, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 1, 2022: 1Per-year citation counts (last 5 years)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.utilize | 132 |
| abstract_inverted_index.various | 85, 252 |
| abstract_inverted_index.(OS-ELM) | 173 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.LSTM3000 | 191, 212 |
| abstract_inverted_index.LSTM6000 | 189, 210 |
| abstract_inverted_index.achieved | 200 |
| abstract_inverted_index.approach | 243 |
| abstract_inverted_index.boosting | 108 |
| abstract_inverted_index.compared | 187 |
| abstract_inverted_index.evaluate | 72 |
| abstract_inverted_index.exhibits | 149 |
| abstract_inverted_index.findings | 220 |
| abstract_inverted_index.function | 109 |
| abstract_inverted_index.learning | 104, 143, 171, 232, 253 |
| abstract_inverted_index.measures | 97 |
| abstract_inverted_index.numerous | 225 |
| abstract_inverted_index.powerful | 246 |
| abstract_inverted_index.proposed | 134, 241 |
| abstract_inverted_index.results. | 128 |
| abstract_inverted_index.superior | 126, 150 |
| abstract_inverted_index.temporal | 2 |
| abstract_inverted_index.Moreover, | 118 |
| abstract_inverted_index.addition, | 130 |
| abstract_inverted_index.algorithm | 10, 102, 123 |
| abstract_inverted_index.comparing | 249 |
| abstract_inverted_index.effective | 113, 121 |
| abstract_inverted_index.framework | 47, 135, 247 |
| abstract_inverted_index.promising | 7 |
| abstract_inverted_index.recurrent | 164 |
| abstract_inverted_index.advantages | 226 |
| abstract_inverted_index.algorithm, | 233 |
| abstract_inverted_index.algorithms | 87 |
| abstract_inverted_index.comparison | 51, 83 |
| abstract_inverted_index.components | 22 |
| abstract_inverted_index.divergence | 96, 183 |
| abstract_inverted_index.evaluating | 251 |
| abstract_inverted_index.illustrate | 146 |
| abstract_inverted_index.principles | 14 |
| abstract_inverted_index.sequential | 142, 169, 231 |
| abstract_inverted_index.standalone | 74 |
| abstract_inverted_index.HTM-spatial | 53 |
| abstract_inverted_index.algorithms, | 56 |
| abstract_inverted_index.algorithms. | 70, 144, 174, 254 |
| abstract_inverted_index.demonstrate | 236 |
| abstract_inverted_index.differences | 63 |
| abstract_inverted_index.distributed | 37, 66 |
| abstract_inverted_index.divergence, | 93 |
| abstract_inverted_index.information | 205 |
| abstract_inverted_index.logarithmic | 107 |
| abstract_inverted_index.neocortical | 16 |
| abstract_inverted_index.performance | 50 |
| abstract_inverted_index.quantifying | 59 |
| abstract_inverted_index.superiority | 176 |
| abstract_inverted_index.Hierarchical | 1 |
| abstract_inverted_index.adaptability | 151 |
| abstract_inverted_index.computation. | 17 |
| abstract_inverted_index.information, | 91 |
| abstract_inverted_index.performance, | 75 |
| abstract_inverted_index.performance. | 81 |
| abstract_inverted_index.similarities | 61 |
| abstract_inverted_index.unsupervised | 8 |
| abstract_inverted_index.effectiveness | 238 |
| abstract_inverted_index.outperforming | 209 |
| abstract_inverted_index.specifically, | 57 |
| abstract_inverted_index.Henze–Penrose | 95 |
| abstract_inverted_index.representation. | 117 |
| abstract_inverted_index.representations | 38, 67 |
| abstract_inverted_index.machine-learning | 9 |
| abstract_inverted_index.state-of-the-art | 141 |
| abstract_inverted_index.information-theoretic | 46, 242 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.52055522 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |