Genetic Evolution of Neural Networks

Evolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on genetic algorithms, a subset of evolutionary computation, with particular regard to the field of neuroevolution, which is the application of GAs to the generation of functioning neural networks. The most widely adopted techniques are thereby explained and contrasted. The experimentation chapter finally shows an implementation of a genetic algorithm, inspired by existing algorithms, with the objective of optimizing a novel kind of artificial neural network

Desarrollo de librería para manejo de redes neuronales en Java para tecnofactor

En el presente proyecto de grado se pretende diseñar y desarrollar una librería de trabajo que pueda ser utilizada para la creación y entrenamiento de Redes Neuronales, definida de manera que su uso sea sencillo por parte de desarrolladores Java. Se utilizará la librería desarrollada para diseñar y obtener una red neuronal capaz de reconocer dígitos escritos a mano, a partir de la base de datos MNIST.The aim of the present project is to design and develop a working library that enables the creation and adaptation of Neural Networks, defined in a way that is simple to use by Java developers. The developed library will be used to design and obtain a neural network capable of recognizing handwritten digits, from the MNIST database

Investigation and Modelling of a Cortical Learning Algorithm in the Neocortex

Many algorithms today provide a good machine learning solution in the specific problem domain, like pattern recognition, clustering, classification, sequence learning, image recognition, etc. They are all suitable for solving some particular problem but are limited regarding flexibility. For example, the algorithm that plays Go cannot do image classification, anomaly detection, or learn sequences. Inspired by the functioning of the neocortex, this work investigates if it is possible to design and implement a universal algorithm that can solve more complex tasks more intelligently in the way the neocortex does. Motivated by the remarkable replication degree of the same and similar circuitry structures in the entire neocortex, this work focuses on the idea of the generality of the neocortex cortical algorithm and suggests the existence of canonical cortical units that can solve more complex tasks if combined in the right way inside of a neural network. Unlike traditional neural networks, algorithms used and created in this work rely only on the finding of neural sciences. Initially inspired by the concept of Hierarchical Temporal Memory (HTM), this work demonstrates how Sparse Encoding, Spatial- and Sequence-Learning can be used to model an artificial cortical area with the cortical algorithm called Neural Association Algorithm (NAA). The proposed algorithm generalises the HTM and can form canonical units that consist of biologically inspired neurons, synapses, and dendrite segments and explains how interconnected canonical units can build a semantical meaning. Results demonstrate how such units can store a large amount of information, learn sequences, build contextual associations that create meaning and provide robustness to noise with high spatial similarity. Inspired by findings in neurosciences, this work also improves some aspects of the existing HTM and introduces the newborn stage of the algorithm. The extended algorithm takes control of a homeostatic plasticity mechanism and ensures that learned patterns remain stable. Finally, this work also delivers the algorithm for the computation over distributed mini-columns that can be executed in parallel using the Actor Programming Model