Associative Memory Models: Emulating Human Brain Functions

Hopfield Network Model: This is one of the most widely used models for associative memory. It's a type of recurrent artificial neural network capable of storing and retrieving patterns.

Boltzmann Machine Model: This model is also a type of recurrent artificial neural network capable of learning and storing patterns. It's based on the principles of statistical mechanics and uses a stochastic approach to learning.

Self-organizing Map Model: This model is based on the concept of competitive learning and is used for clustering and visualization of high-dimensional data. It's also known as a Kohonen network.

Neural Gas Model: This is another type of unsupervised neural network used for clustering and pattern recognition. It's based on the concept of self-organizing maps and uses a competitive learning approach.

ART Model: Adaptive Resonance Theory (ART) is a family of neural networks used for pattern recognition and classification. They're based on the principles of self-organization and self-stabilization.