In this article, I explore in detail how different components in neural networks contribute to their ability to perform abstraction,  a core feature of cognition.

Medium_JS is a curated collection of insights and tutorials on JavaScript development, designed to help developers stay informed and inspired in the ever-evolving world of web development. By featuring a selection of high-quality articles, tutorials, and expert opinions from the JavaScript community, Medium_JS offers  guidance on mastering JavaScript language features, exploring modern frameworks and libraries, and solving common development challenges. Whether you're a frontend developer, a full-stack engineer, or an aspiring JavaScript enthusiast, Medium_JS provides a  knowledge and resources to fuel your JavaScript journey.

Medium

This post explores the concept of abstraction in neural networks and its importance in cognition. Abstraction is the act of ignoring irrelevant details and focusing on the relevant parts. Neural networks implement abstraction by using non-injective mappings and discarding irrelevant features. Abstraction is closely related to generalization, where more abstract assumptions lead to more widely applicable conclusions. Abstraction plays a crucial role in our ability to understand and learn from the world around us.

Understanding Abstractions in Neural Networks

Duality of Abstraction and Generalization