Stochastic Neural Manifolds: Bridging Experience Buffers and Dynamic Topologies

Introduction

In Part 1, we introduced the core principles of neural manifolds, memory consolidation, and latent space representations, showcasing how autoencoders and regularization techniques can be used to model high-dimensional data and visualize it using t-SNE. In this follow-up, we’ll explore more advanced concepts, including stochastic updates, dynamic topology adjustments, and the use of an experience buffer.

These new mechanisms introduce a biologically inspired dynamic behaviour to the neural network model, offering significant improvements in both memory consolidation and latent space representation.

By the end of this article, you will have a comprehensive understanding of how these changes work, their mathematical formulations, and the results they produce. We will also explore potential use cases and discuss why these advancements are crucial for furthering machine learning models that aim to emulate human learning and memory systems.