Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus

Learning Mechanisms to account for the Speed, Selectivity and Invariance of Responses in the visual Cortex

Dans cette thèse je propose plusieurs mécanismes de plasticité synaptique qui pourraient expliquer la rapidité, la sélectivité et l'invariance des réponses neuronales dans le cortex visuel. Leur plausibilité biologique est discutée. J'expose également les résultats d'une expérience de psychophysique pertinente, qui montrent que la familiarité peut accélérer les traitements visuels. Au delà de ces résultats propres au système visuel, les travaux présentés ici créditent l'hypothèse de l'utilisation des dates de spikes pour encoder, décoder, et traiter l'information dans le cerveau - c'est la théorie dite du 'codage temporel'. Dans un tel cadre, la Spike Timing Dependent Plasticity pourrait jouer un rôle clef, en détectant des patterns de spikes répétitifs et en permettant d'y répondre de plus en plus rapidement.In this thesis I propose various activity-driven synaptic plasticity mechanisms that could account for the speed, selectivity and invariance of the neuronal responses in the visual cortex. Their biological plausibility is discussed. I also present the results of a relevant psychophysical experiment demonstrating that familiarity can accelerate visual processing. Beyond these results on the visual system, the studies presented here also credit the hypothesis that the brain uses the spike times to encode, decode, and process information - a theory referred to as 'temporal coding'. In such a framework the Spike Timing Dependent Plasticity may play a key role, by detecting repeating spike patterns and by generating faster and faster responses to those patterns

Self-organising maps : statistical analysis, treatment and applications.

This thesis presents some substantial theoretical analyses and optimal treatments of Kohonen's self-organising map (SOM) algorithm, and explores the practical application potential of the algorithm for vector quantisation, pattern classification, and image processing. It consists of two major parts. In the first part, the SOM algorithm is investigated and analysed from a statistical viewpoint. The proof of its universal convergence for any dimensionality is obtained using a novel and extended form of the Central Limit Theorem. Its feature space is shown to be an approximate multivariate Gaussian process, which will eventually converge and form a mapping, which minimises the mean-square distortion between the feature and input spaces. The diminishing effect of the initial states and implicit effects of the learning rate and neighbourhood function on its convergence and ordering are analysed and discussed. Distinct and meaningful definitions, and associated measures, of its ordering are presented in relation to map's fault-tolerance. The SOM algorithm is further enhanced by incorporating a proposed constraint, or Bayesian modification, in order to achieve optimal vector quantisation or pattern classification. The second part of this thesis addresses the task of unsupervised texture-image segmentation by means of SOM networks and model-based descriptions. A brief review of texture analysis in terms of definitions, perceptions, and approaches is given. Markov random field model-based approaches are discussed in detail. Arising from this a hierarchical self-organised segmentation structure, which consists of a local MRF parameter estimator, a SOM network, and a simple voting layer, is proposed and is shown, by theoretical analysis and practical experiment, to achieve a maximum likelihood or maximum a posteriori segmentation. A fast, simple, but efficient boundary relaxation algorithm is proposed as a post-processor to further refine the resulting segmentation. The class number validation problem in a fully unsupervised segmentation is approached by a classical, simple, and on-line minimum mean-square-error method. Experimental results indicate that this method is very efficient for texture segmentation problems. The thesis concludes with some suggestions for further work on SOM neural networks

Learning Sensorimotor Abstractions

Projecte final de carrera fet en col.laboració amb Aalto University. School of Science and Technology. Faculty of Information and Natural SciencesIn order to interact with real environments, performing daily tasks, autonomous agents (as machines or robots) cannot be hard-coded. Given all the possible scenarios and, in each scenario, all the possible variations, it is impossible to take into account every single situation that the autonomous agent may encounter. Humans are able to interact with the changing world using as a guidance the sensory input perceived. Thus, autonomous agents need to be able to adapt to a changing environment. This work proposes a biologically inspired solution that allows the agent to learn representations and skills autonomously that prepare the agent for future learning tasks. The biologically inspired solution proposed here, called a cognitive architecture, follows the hierarchical architecture found in the cerebral cortex. This model permits the autonomous agent to extract useful information from the sensory input data it receives. The information is coded in abstractions, which are invariant features found within the input patterns. The cognitive architecture uses slowness as a principle for extracting features. In principle, unsupervised learning algorithms based on slowness try to find relevant and slowly changing data. This information could be useful for self evaluation. The agent tries to learn how to manipulate the sensory abstractions, by linking those to the motor ones. This allows the robot to find the mapping between the motor actions it is taking and the changes it is able to produce in the surrounding environment. Using the cognitive architecture, an example will be implemented. An agent, who knows nothing about the environment it is placed on, will be able to learn how to move towards different places in space in an efficient (not random) way. Starting from random movements and capturing the sensory input data, it is able to learn concepts such as place and distance, which permits it to learn how to move towards a target efficiently

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field