How Neurons in Deep Models Relate with Neurons in the Brain
In dealing with the algorithmic aspects of intelligent systems, the analogy with the biological brain has always been attractive, and has often had a dual function. On the one hand, it has been an effective source of inspiration for their design, while, on the other hand, it has been used as the jus...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/14/9/272 |
| _version_ | 1797520536100667392 |
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| author | Arianna Pavone Alessio Plebe |
| author_facet | Arianna Pavone Alessio Plebe |
| author_sort | Arianna Pavone |
| collection | DOAJ |
| description | In dealing with the algorithmic aspects of intelligent systems, the analogy with the biological brain has always been attractive, and has often had a dual function. On the one hand, it has been an effective source of inspiration for their design, while, on the other hand, it has been used as the justification for their success, especially in the case of Deep Learning (DL) models. However, in recent years, inspiration from the brain has lost its grip on its first role, yet it continues to be proposed in its second role, although we believe it is also becoming less and less defensible. Outside the chorus, there are theoretical proposals that instead identify important demarcation lines between DL and human cognition, to the point of being even incommensurable. In this article we argue that, paradoxically, the partial indifference of the developers of deep neural models to the functioning of biological neurons is one of the reasons for their success, having promoted a pragmatically opportunistic attitude. We believe that it is even possible to glimpse a biological analogy of a different kind, in that the essentially heuristic way of proceeding in modern DL development bears intriguing similarities to natural evolution. |
| first_indexed | 2024-03-10T07:58:10Z |
| format | Article |
| id | doaj.art-82c0a7a806454c70a74daad48c3e420e |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T07:58:10Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-82c0a7a806454c70a74daad48c3e420e2023-11-22T11:40:28ZengMDPI AGAlgorithms1999-48932021-09-0114927210.3390/a14090272How Neurons in Deep Models Relate with Neurons in the BrainArianna Pavone0Alessio Plebe1Department of Cognitive Science, Università di Messina, via Concezione n.6/8, 98122 Messina, ItalyDepartment of Cognitive Science, Università di Messina, via Concezione n.6/8, 98122 Messina, ItalyIn dealing with the algorithmic aspects of intelligent systems, the analogy with the biological brain has always been attractive, and has often had a dual function. On the one hand, it has been an effective source of inspiration for their design, while, on the other hand, it has been used as the justification for their success, especially in the case of Deep Learning (DL) models. However, in recent years, inspiration from the brain has lost its grip on its first role, yet it continues to be proposed in its second role, although we believe it is also becoming less and less defensible. Outside the chorus, there are theoretical proposals that instead identify important demarcation lines between DL and human cognition, to the point of being even incommensurable. In this article we argue that, paradoxically, the partial indifference of the developers of deep neural models to the functioning of biological neurons is one of the reasons for their success, having promoted a pragmatically opportunistic attitude. We believe that it is even possible to glimpse a biological analogy of a different kind, in that the essentially heuristic way of proceeding in modern DL development bears intriguing similarities to natural evolution.https://www.mdpi.com/1999-4893/14/9/272brain–computer analogyalgorithmic explainabilityincommensurable algorithmic solutionsblack-box |
| spellingShingle | Arianna Pavone Alessio Plebe How Neurons in Deep Models Relate with Neurons in the Brain Algorithms brain–computer analogy algorithmic explainability incommensurable algorithmic solutions black-box |
| title | How Neurons in Deep Models Relate with Neurons in the Brain |
| title_full | How Neurons in Deep Models Relate with Neurons in the Brain |
| title_fullStr | How Neurons in Deep Models Relate with Neurons in the Brain |
| title_full_unstemmed | How Neurons in Deep Models Relate with Neurons in the Brain |
| title_short | How Neurons in Deep Models Relate with Neurons in the Brain |
| title_sort | how neurons in deep models relate with neurons in the brain |
| topic | brain–computer analogy algorithmic explainability incommensurable algorithmic solutions black-box |
| url | https://www.mdpi.com/1999-4893/14/9/272 |
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