# Eigen Artificial Neural Networks

## TL;DR

This paper introduces a novel physical analogy for neural network optimization, modeling it as a conservative force field governed by an eigenvalue equation similar to Schrödinger's, linking energy minimization to reduced prediction error.

## Contribution

It presents a new framework that treats neural network training as a physical system, deriving a scalar potential from mutual information and applying quantum mechanics concepts to optimize network parameters.

## Key findings

- Energy minimization reduces average prediction error.
- The framework links mutual information to potential energy in the network.
- A recursive procedure refines the state function for better optimization.

## Abstract

This work has its origin in intuitive physical and statistical considerations. The problem of optimizing an artificial neural network is treated as a physical system, composed of a conservative vector force field. The derived scalar potential is a measure of the potential energy of the network, a function of the distance between predictions and targets. Starting from some analogies with wave mechanics, the description of the system is justified with an eigenvalue equation that is a variant of the Schr\~odinger equation, in which the potential is defined by the mutual information between inputs and targets. The weights and parameters of the network, as well as those of the state function, are varied so as to minimize energy, using an equivalent of the variational theorem of wave mechanics. The minimum energy thus obtained implies the principle of minimum mutual information (MinMI). We also propose a definition of the potential work produced by the force field to bring a network from an arbitrary probability distribution to the potential-constrained system, which allows to establish a measure of the complexity of the system. At the end of the discussion we expose a recursive procedure that allows to refine the state function and bypass some initial assumptions, as well as a discussion of some topics in quantum mechanics applied to the formalism, such as the uncertainty principle and the temporal evolution of the system. Results demonstrate how the minimization of energy effectively leads to a decrease in the average error between network predictions and targets.