Given the multi-layered structure of the network, the internal representation of the world is incrementally constructed through a series of successive layers. Consequently, the output can be regarded as the subjective world model of the neural network. The input for the neural network represents the true objective reality, while the output corresponds to the learned representation of this external reality. We are therefore assuming that a multilayer neural network is learning to survive in a specific environment and is therefore building a representation of its environment that minimises its surprise. Our analysis is built upon the free energy principle proposed by Friston (Schwartenbeck and al, 2013), which posits that a structure trying to survive within a given environment will develop an internal representation of that environment that mimeses its surprise.” It is under this framework that we examine the learning process of a multilayer neural network, which similarly aims to adapt to a specific environment by constructing a representation that reduces its surprise. This post expands and further elaborates of a previous blog post by the author (Seidou Sand, 2023). The objective of this blog post is to show that the observer effect, which is so puzzling in our physical world, has a logical explanation for a layer in a multilayers neural network and that that explanation involves a learning process.
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