Neural Network Degeneration and its Relationship to the Brain

TL;DR

This paper explores how neural network degradation techniques can model neurodegenerative diseases, providing insights into memory loss and learning dysfunction by analyzing error functions during network deterioration.

Contribution

It introduces specific degradation methods applied to neural networks to simulate brain diseases, offering a novel approach to studying neurodegeneration and brain network disruptions.

Findings

Degradation methods reveal patterns of memory loss.

Error functions correlate with neurodegenerative symptoms.

Insights into brain network dysfunction mechanisms.

Abstract

This report discusses the application of neural networks (NNs) as small segments of the brain. The networks representing the biological connectome are altered both spatially and temporally. The degradation techniques applied here are "weight degradation", "weight scrambling", and variable activation function. These methods aim to shine light on the study of neurodegenerative diseases such as Alzheimer's, Huntington's and Parkinson's disease as well as strokes and brain tumors disrupting the flow of information in the brain's network. Fundamental insights to memory loss and generalized learning dysfunction are gained by monitoring the network's error function during network degradation. The biological significance of each facet is also discussed.