Auto-associative neural networks (eg the Hopfield model implementing the standard
Hebbian prescription) serve as a foundational framework for pattern recognition and …

We study bi-directional associative neural networks that, exposed to noisy examples of an
extensive number of random archetypes, learn the latter (with or without the presence of a …

Restricted Boltzmann Machines (RBMs) are generative models designed to learn from data
with a rich underlying structure. In this work, we explore a teacher-student setting where a …

Pyramidal cells that emit spikes when the animal is at specific locations of the environment
are known as" place cells": these neurons are thought to provide an internal representation …

This thesis explores the development of artificial neural networks (ANNs) grounded in
cognitive principles. Specifically, it focuses on Associative Memories (AMs) and how to …

Restricted Boltzmann machines (RBMs) are among the simplest unsupervised models
implementing data/representation duality. The learning curves of RBMs trained on structured …

The Hopfield model was introduced by Hopfield in [21] as a neural network model to
emulate and understand the mechanism of associative memory that our brain performs …

요 약일반적으로 지도 학습은 학습 데이터 쌍 구성 방법과 학습 구조에 따라 학습 성능이
달라지는 문제점이 있다. 따라서 실세계 응용문제에 적용할 경우에는 학습 및 인식 성능이 …