Predictive Learning in Energy-based Models with Attractor Structures Article Swipe
Xingsi Dong
,
Peng Yuan
,
Si Wu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.13997
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.13997
Predictive models are highly advanced in understanding the mechanisms of brain function. Recent advances in machine learning further underscore the power of prediction for optimal representation in learning. However, there remains a gap in creating a biologically plausible model that explains how the neural system achieves prediction. In this paper, we introduce a framework that employs an energy-based model (EBM) to capture the nuanced processes of predicting observation after action within the neural system, encompassing prediction, learning, and inference. We implement the EBM with a hierarchical structure and integrate a continuous attractor neural network for memory, constructing a biologically plausible model. In experimental evaluations, our model demonstrates efficacy across diverse scenarios. The range of actions includes eye movement, motion in environments, head turning, and static observation while the environment changes. Our model not only makes accurate predictions for environments it was trained on, but also provides reasonable predictions for unseen environments, matching the performances of machine learning methods in multiple tasks. We hope that this study contributes to a deep understanding of how the neural system performs prediction.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.13997
- https://arxiv.org/pdf/2501.13997
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406840435
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406840435Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2501.13997Digital Object Identifier
- Title
-
Predictive Learning in Energy-based Models with Attractor StructuresWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-23Full publication date if available
- Authors
-
Xingsi Dong, Peng Yuan, Si WuList of authors in order
- Landing page
-
https://arxiv.org/abs/2501.13997Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2501.13997Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2501.13997Direct OA link when available
- Concepts
-
Attractor, Computer science, Energy (signal processing), Artificial intelligence, Mathematics, Statistics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4406840435 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2501.13997 |
| ids.doi | https://doi.org/10.48550/arxiv.2501.13997 |
| ids.openalex | https://openalex.org/W4406840435 |
| fwci | |
| type | preprint |
| title | Predictive Learning in Energy-based Models with Attractor Structures |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10320 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.8180000185966492 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Neural Networks and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C164380108 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7181112766265869 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q507187 |
| concepts[0].display_name | Attractor |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.48311755061149597 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C186370098 |
| concepts[2].level | 2 |
| concepts[2].score | 0.45928311347961426 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q442787 |
| concepts[2].display_name | Energy (signal processing) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.3267328143119812 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C33923547 |
| concepts[4].level | 0 |
| concepts[4].score | 0.17699837684631348 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[4].display_name | Mathematics |
| concepts[5].id | https://openalex.org/C105795698 |
| concepts[5].level | 1 |
| concepts[5].score | 0.08614647388458252 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[5].display_name | Statistics |
| concepts[6].id | https://openalex.org/C134306372 |
| concepts[6].level | 1 |
| concepts[6].score | 0.05965203046798706 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[6].display_name | Mathematical analysis |
| keywords[0].id | https://openalex.org/keywords/attractor |
| keywords[0].score | 0.7181112766265869 |
| keywords[0].display_name | Attractor |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.48311755061149597 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/energy |
| keywords[2].score | 0.45928311347961426 |
| keywords[2].display_name | Energy (signal processing) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.3267328143119812 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/mathematics |
| keywords[4].score | 0.17699837684631348 |
| keywords[4].display_name | Mathematics |
| keywords[5].id | https://openalex.org/keywords/statistics |
| keywords[5].score | 0.08614647388458252 |
| keywords[5].display_name | Statistics |
| keywords[6].id | https://openalex.org/keywords/mathematical-analysis |
| keywords[6].score | 0.05965203046798706 |
| keywords[6].display_name | Mathematical analysis |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2501.13997 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2501.13997 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2501.13997 |
| locations[1].id | doi:10.48550/arxiv.2501.13997 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2501.13997 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5066799202 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Xingsi Dong |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Dong, Xingsi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5079342967 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3298-9904 |
| authorships[1].author.display_name | Peng Yuan |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yuan, Pengxiang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5101884230 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Si Wu |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Wu, Si |
| authorships[2].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://arxiv.org/pdf/2501.13997 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Predictive Learning in Energy-based Models with Attractor Structures |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10320 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.8180000185966492 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Neural Networks and Applications |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W3049714198, https://openalex.org/W2960434112, https://openalex.org/W2036805113, https://openalex.org/W2592279130, https://openalex.org/W2049158118, https://openalex.org/W2123827981, https://openalex.org/W4205994410 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2501.13997 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| 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 | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2501.13997 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| 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://arxiv.org/abs/2501.13997 |
| primary_location.id | pmh:oai:arXiv.org:2501.13997 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2501.13997 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2501.13997 |
| publication_date | 2025-01-23 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 31, 35, 52, 84, 89, 97, 168 |
| abstract_inverted_index.In | 47, 101 |
| abstract_inverted_index.We | 79, 161 |
| abstract_inverted_index.an | 56 |
| abstract_inverted_index.in | 5, 14, 26, 33, 119, 158 |
| abstract_inverted_index.it | 139 |
| abstract_inverted_index.of | 9, 21, 65, 113, 154, 171 |
| abstract_inverted_index.to | 60, 167 |
| abstract_inverted_index.we | 50 |
| abstract_inverted_index.EBM | 82 |
| abstract_inverted_index.Our | 130 |
| abstract_inverted_index.The | 111 |
| abstract_inverted_index.and | 77, 87, 123 |
| abstract_inverted_index.are | 2 |
| abstract_inverted_index.but | 143 |
| abstract_inverted_index.eye | 116 |
| abstract_inverted_index.for | 23, 94, 137, 148 |
| abstract_inverted_index.gap | 32 |
| abstract_inverted_index.how | 41, 172 |
| abstract_inverted_index.not | 132 |
| abstract_inverted_index.on, | 142 |
| abstract_inverted_index.our | 104 |
| abstract_inverted_index.the | 7, 19, 42, 62, 71, 81, 127, 152, 173 |
| abstract_inverted_index.was | 140 |
| abstract_inverted_index.also | 144 |
| abstract_inverted_index.deep | 169 |
| abstract_inverted_index.head | 121 |
| abstract_inverted_index.hope | 162 |
| abstract_inverted_index.only | 133 |
| abstract_inverted_index.that | 39, 54, 163 |
| abstract_inverted_index.this | 48, 164 |
| abstract_inverted_index.with | 83 |
| abstract_inverted_index.(EBM) | 59 |
| abstract_inverted_index.after | 68 |
| abstract_inverted_index.brain | 10 |
| abstract_inverted_index.makes | 134 |
| abstract_inverted_index.model | 38, 58, 105, 131 |
| abstract_inverted_index.power | 20 |
| abstract_inverted_index.range | 112 |
| abstract_inverted_index.study | 165 |
| abstract_inverted_index.there | 29 |
| abstract_inverted_index.while | 126 |
| abstract_inverted_index.Recent | 12 |
| abstract_inverted_index.across | 108 |
| abstract_inverted_index.action | 69 |
| abstract_inverted_index.highly | 3 |
| abstract_inverted_index.model. | 100 |
| abstract_inverted_index.models | 1 |
| abstract_inverted_index.motion | 118 |
| abstract_inverted_index.neural | 43, 72, 92, 174 |
| abstract_inverted_index.paper, | 49 |
| abstract_inverted_index.static | 124 |
| abstract_inverted_index.system | 44, 175 |
| abstract_inverted_index.tasks. | 160 |
| abstract_inverted_index.unseen | 149 |
| abstract_inverted_index.within | 70 |
| abstract_inverted_index.actions | 114 |
| abstract_inverted_index.capture | 61 |
| abstract_inverted_index.diverse | 109 |
| abstract_inverted_index.employs | 55 |
| abstract_inverted_index.further | 17 |
| abstract_inverted_index.machine | 15, 155 |
| abstract_inverted_index.memory, | 95 |
| abstract_inverted_index.methods | 157 |
| abstract_inverted_index.network | 93 |
| abstract_inverted_index.nuanced | 63 |
| abstract_inverted_index.optimal | 24 |
| abstract_inverted_index.remains | 30 |
| abstract_inverted_index.system, | 73 |
| abstract_inverted_index.trained | 141 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.accurate | 135 |
| abstract_inverted_index.achieves | 45 |
| abstract_inverted_index.advanced | 4 |
| abstract_inverted_index.advances | 13 |
| abstract_inverted_index.changes. | 129 |
| abstract_inverted_index.creating | 34 |
| abstract_inverted_index.efficacy | 107 |
| abstract_inverted_index.explains | 40 |
| abstract_inverted_index.includes | 115 |
| abstract_inverted_index.learning | 16, 156 |
| abstract_inverted_index.matching | 151 |
| abstract_inverted_index.multiple | 159 |
| abstract_inverted_index.performs | 176 |
| abstract_inverted_index.provides | 145 |
| abstract_inverted_index.turning, | 122 |
| abstract_inverted_index.attractor | 91 |
| abstract_inverted_index.framework | 53 |
| abstract_inverted_index.function. | 11 |
| abstract_inverted_index.implement | 80 |
| abstract_inverted_index.integrate | 88 |
| abstract_inverted_index.introduce | 51 |
| abstract_inverted_index.learning, | 76 |
| abstract_inverted_index.learning. | 27 |
| abstract_inverted_index.movement, | 117 |
| abstract_inverted_index.plausible | 37, 99 |
| abstract_inverted_index.processes | 64 |
| abstract_inverted_index.structure | 86 |
| abstract_inverted_index.Predictive | 0 |
| abstract_inverted_index.continuous | 90 |
| abstract_inverted_index.inference. | 78 |
| abstract_inverted_index.mechanisms | 8 |
| abstract_inverted_index.predicting | 66 |
| abstract_inverted_index.prediction | 22 |
| abstract_inverted_index.reasonable | 146 |
| abstract_inverted_index.scenarios. | 110 |
| abstract_inverted_index.underscore | 18 |
| abstract_inverted_index.contributes | 166 |
| abstract_inverted_index.environment | 128 |
| abstract_inverted_index.observation | 67, 125 |
| abstract_inverted_index.prediction, | 75 |
| abstract_inverted_index.prediction. | 46, 177 |
| abstract_inverted_index.predictions | 136, 147 |
| abstract_inverted_index.biologically | 36, 98 |
| abstract_inverted_index.constructing | 96 |
| abstract_inverted_index.demonstrates | 106 |
| abstract_inverted_index.encompassing | 74 |
| abstract_inverted_index.energy-based | 57 |
| abstract_inverted_index.environments | 138 |
| abstract_inverted_index.evaluations, | 103 |
| abstract_inverted_index.experimental | 102 |
| abstract_inverted_index.hierarchical | 85 |
| abstract_inverted_index.performances | 153 |
| abstract_inverted_index.environments, | 120, 150 |
| abstract_inverted_index.understanding | 6, 170 |
| abstract_inverted_index.representation | 25 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |