Rethinking Deep Learning: Propagating Information in Neural Networks without Backpropagation and Statistical Optimization

• URL: http://arxiv.org/abs/2409.03760v1
• Date: Sun, 18 Aug 2024 09:22:24 GMT
• Title: Rethinking Deep Learning: Propagating Information in Neural Networks without Backpropagation and Statistical Optimization
• Authors: Kei Itoh,
• Abstract summary: This study discusses the information propagation capabilities and potential practical applications of NNs as neural system mimicking structures. In this study, the NNs architecture comprises fully connected layers using step functions as activation functions, with 0-15 hidden layers, and no weight updates. The accuracy is calculated by comparing the average output vectors of the training data for each label with the output vectors of the test data, based on vector similarity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing strong AI signifies the arrival of technological singularity, contributing greatly to advancing human civilization and resolving social issues. Neural networks (NNs) and deep learning, which utilize NNs, are expected to lead to strong AI due to their biological neural system-mimicking structures. However, the statistical weight optimization techniques commonly used, such as error backpropagation and loss functions, may hinder the mimicry of neural systems. This study discusses the information propagation capabilities and potential practical applications of NNs as neural system mimicking structures by solving the handwritten character recognition problem in the Modified National Institute of Standards and Technology (MNIST) database without using statistical weight optimization techniques like error backpropagation. In this study, the NNs architecture comprises fully connected layers using step functions as activation functions, with 0-15 hidden layers, and no weight updates. The accuracy is calculated by comparing the average output vectors of the training data for each label with the output vectors of the test data, based on vector similarity. The results showed that the maximum accuracy achieved is around 80%. This indicates that NNs can propagate information correctly without using statistical weight optimization. Additionally, the accuracy decreased with an increasing number of hidden layers. This is attributed to the decrease in the variance of the output vectors as the number of hidden layers increases, suggesting that the output data becomes smooth. This study's NNs and accuracy calculation methods are simple and have room for various improvements. Moreover, creating a feedforward NNs that repeatedly cycles through 'input -> processing -> output -> environmental response -> input -> ...' could pave the way for practical software applications.

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