Hopfield Network is a form of recurrent artificial neural network. Hopfield networks are classical models of memory and collective processing in networks of abstract McCulloch-Pitts neurons, but they have not been widely used in signal processing as they usually have small memory capacity (scaling linearly in the number of neurons) and are challenging to train, especially on noisy data. Hopfield nets serve as content-addressable memory systems with binary threshold nodes. The units in Hopfield nets are binary threshold units, i.e. the units only take on two different values for their states and the value is determined by whether or not the units input exceeds their threshold. Hopfield nets normally have units that take on values of 1 or -1. Every pair of units i and j in a Hopfield network have a connection that is described by the connectivity weight. The constraint that weights must be symmetric guarantees the energy function decreases monotonically while following the activation rules, and the network may exhibit some periodic or chaotic behavior if non-symmetric weights are used. However, it was found that this chaotic behavior is confined to a relatively small space and does not impair the network’s ability to act as a content-addressable associative memory system.
Was the above useful? Please share with others on social media.
Recommended reading list:
|Data Science from Scratch: First Principles with Python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases