Web Neural Network with Complete DiGraphs

Fuente: arXiv
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Auteur principal: Li, Frank
Format: Preprint
Publié: 2024
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author Li, Frank
author_facet Li, Frank
contents This paper introduces a new neural network model that aims to mimic the biological brain more closely by structuring the network as a complete directed graph that processes continuous data for each timestep. Current neural networks have structures that vaguely mimic the brain structure, such as neurons, convolutions, and recurrence. The model proposed in this paper adds additional structural properties by introducing cycles into the neuron connections and removing the sequential nature commonly seen in other network layers. Furthermore, the model has continuous input and output, inspired by spiking neural networks, which allows the network to learn a process of classification, rather than simply returning the final result.
format Preprint
id arxiv_https___arxiv_org_abs_2401_04134
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Web Neural Network with Complete DiGraphs
Li, Frank
Neural and Evolutionary Computing
Artificial Intelligence
This paper introduces a new neural network model that aims to mimic the biological brain more closely by structuring the network as a complete directed graph that processes continuous data for each timestep. Current neural networks have structures that vaguely mimic the brain structure, such as neurons, convolutions, and recurrence. The model proposed in this paper adds additional structural properties by introducing cycles into the neuron connections and removing the sequential nature commonly seen in other network layers. Furthermore, the model has continuous input and output, inspired by spiking neural networks, which allows the network to learn a process of classification, rather than simply returning the final result.
title Web Neural Network with Complete DiGraphs
topic Neural and Evolutionary Computing
Artificial Intelligence
url https://arxiv.org/abs/2401.04134