Version 1
: Received: 31 August 2023 / Approved: 1 September 2023 / Online: 1 September 2023 (07:25:53 CEST)
How to cite:
Molina, E.; Rodriguez, X. The Gap between Deep Learning and the Nervous System. Preprints2023, 2023090024. https://doi.org/10.20944/preprints202309.0024.v1
Molina, E.; Rodriguez, X. The Gap between Deep Learning and the Nervous System. Preprints 2023, 2023090024. https://doi.org/10.20944/preprints202309.0024.v1
Molina, E.; Rodriguez, X. The Gap between Deep Learning and the Nervous System. Preprints2023, 2023090024. https://doi.org/10.20944/preprints202309.0024.v1
APA Style
Molina, E., & Rodriguez, X. (2023). The Gap between Deep Learning and the Nervous System. Preprints. https://doi.org/10.20944/preprints202309.0024.v1
Chicago/Turabian Style
Molina, E. and Ximena Rodriguez. 2023 "The Gap between Deep Learning and the Nervous System" Preprints. https://doi.org/10.20944/preprints202309.0024.v1
Abstract
A remarkable strengths of deep neural networks lies in their ability to discover patterns and representations from complex data. Deep learning models can learn and refine features at different levels of abstraction, enabling them to handle complex tasks in computer vision recognition, natural language processing, and speech recognition. The ability to generalize from examples, coupled with the capacity to process vast amounts of data, empowers deep learning to achieve state-of-the-art results across a wide spectrum of applications. An argument for this ability is based on similarities between deep neural networks and the nervous system. In this report, we argue that despite the remarkable performance of deep learning, there are still gaps between deep learning and the nervous system that needs to be closed to enable deep learning doing tasks that currently only the nervous system can perform
Keywords
nervous system; deep learning; artifitial intelligence
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
