Combinatorial Generalisation in Machine Vision

The human capacity for generalisation, i.e. the fact that we are able to successfully perform a familiar task in novel contexts, is one of the hallmarks of our intelligent behaviour. But what mechanisms enable this capacity that is at the same time so impressive but comes so naturally to us? This is a question that has driven copious amounts of research in both Cognitive Science and Artificial Intelligence for almost a century, with some advocating the need for symbolic systems and others the benefits of distributed representations. In this thesis we will explore which principles help AI systems to generalise to novel combinations of previously observed elements (such as color and shape) in the context of machine vision. We will show that while approaches such as disentangled representation learning showed initial promise, they are fundamentally unable to solve this generalisation problem. In doing so we will illustrate the need to perform severe tests of models in order to properly assess their limitations. We will also see how such failures are robust across different datasets, training modalities and in the internal representations of the models. We then show that a different type of system that attempts to learn object-centric representations is capable of solving the generalisation challenges that previous models could not. We conclude by discussing the implications of these results for long-standing questions regarding the kinds of cognitive systems that are required to solve generalisation problems

A novel multi-dimensional regression model based on Gaussian Networks

Modeling and prediction in continuous domains are one of the most important and studied problems in Mathematics and Computer Science. Models that can not only solve regression tasks, but also expose the interdependencies inside the domain are of high value for researchers in many fields. One of the most popular methods for learning the relations between variables in a continuous domain are Gaussian Networks. In this thesis we present a new model that can learn a Gaussian Network. This model can later be used for regression or analysis of the relations in the domain, with a particular interest in its application in the field of Neuroscience.---RESUMEN---Modelar y predecir en dominios continuos es uno de los problemas más estudiados en el campo de las Matemáticas y la Ciencia de la Computación. Modelos que no solamente puedan resolver problemas de regresión, si no también exponer las relaciones entre las variables de un dominio son de gran valor para los investigadores de muchos campos científicos. Uno de los modelos más populares usados para resolver estos problemas son las Redes Gaussianas. En esta tesis se presenta un nuevo modelo basado en Redes Gaussianas que puede ser ajustado a partir de datos para luego ser utilizado en tareas de regresión y análisis, con un especial interés en su aplicación al campo de la Neurociencia

Model-Based Inference of Synaptic Transmission

Synaptic computation is believed to underlie many forms of animal behavior. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals

Deep Problems with Neural Network Models of Human Vision

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral benchmark datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain benchmark datasets (e.g., single cell responses or fMRI data). However, most behavioral and brain benchmarks report the outcomes of observational experiments that do not manipulate any independent variables, and we show that the good prediction on these datasets may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on predicting observational data. We conclude by briefly summarizing various promising modelling approaches that focus on psychological data