preprints.org > engineering > electrical and electronic engineering > doi: 10.20944/preprints202309.0024.v1

Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 31 August 2023 / Approved: 1 September 2023 / Online: 1 September 2023 (07:25:53 CEST)

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

nervous system; deep learning; artifitial intelligence

Engineering, Electrical and Electronic Engineering

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