dc.contributor.author |
Bensemann, Joshua |
|
dc.contributor.author |
Witbrock, Michael |
|
dc.coverage.spatial |
England |
|
dc.date.accessioned |
2022-05-19T23:57:16Z |
|
dc.date.available |
2022-05-19T23:57:16Z |
|
dc.date.issued |
2021-06-08 |
|
dc.identifier.citation |
(2021). Heliyon, 7(6), e07246-. |
|
dc.identifier.issn |
2405-8440 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/59376 |
|
dc.description.abstract |
There have been several recent attempts at using Artificial Intelligence systems to model aspects of consciousness (Gamez, 2008; Reggia, 2013). Deep Neural Networks have been given additional functionality in the present attempt, allowing them to emulate phenological aspects of consciousness by self-generating information representing multi-modal inputs as either sounds or images. We added these functions to determine whether knowledge of the input's modality aids the networks' learning. In some cases, these representations caused the model to be more accurate after training and for less training to be required for the model to reach its highest accuracy scores. |
|
dc.format.medium |
Electronic-eCollection |
|
dc.language |
eng |
|
dc.publisher |
Elsevier BV |
|
dc.relation.ispartofseries |
Heliyon |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Hebbian learning |
|
dc.subject |
Neural network |
|
dc.subject |
Synthetic phenomenology |
|
dc.subject |
Neurosciences |
|
dc.subject |
1.2 Psychological and socioeconomic processes |
|
dc.subject |
Mental health |
|
dc.subject |
Science & Technology |
|
dc.subject |
Multidisciplinary Sciences |
|
dc.subject |
Science & Technology - Other Topics |
|
dc.subject |
CONSCIOUSNESS |
|
dc.title |
The effects of implementing phenomenology in a deep neural network. |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.heliyon.2021.e07246 |
|
pubs.issue |
6 |
|
pubs.begin-page |
e07246 |
|
pubs.volume |
7 |
|
dc.date.updated |
2022-04-28T00:16:53Z |
|
dc.rights.holder |
Copyright: The author |
en |
dc.identifier.pmid |
34179532 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/34179532 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
research-article |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
855406 |
|
pubs.org-id |
Science |
|
pubs.org-id |
School of Computer Science |
|
dc.identifier.eissn |
2405-8440 |
|
dc.identifier.pii |
S2405-8440(21)01349-9 |
|
pubs.number |
e07246 |
|
pubs.record-created-at-source-date |
2022-04-28 |
|