# Black-Hole evaporation from the perspective of neural networks

## TL;DR

This paper explores black-hole evaporation using neural network models, analyzing how the Hamiltonian evolves and how synaptic connections change from excitatory to inhibitory during evaporation.

## Contribution

It introduces a novel neural network framework to study black-hole evaporation and identifies conditions for synaptic transition during the process.

## Key findings

- Synaptic connections switch from gravitatory to inhibitory during evaporation.
- The Hamiltonian evolution reveals key conditions for black-hole dynamics.
- Neural network models can simulate aspects of black-hole physics.

## Abstract

We study the black-hole evaporation from the perspective of neural networks. We then analyze the evolution of the Hamiltonian, finding in this way the conditions under which the synapse connecting the neurons changes from gravitatory to inhibitory during the evaporation process.